Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Proceedings of Five Day Workshop on

FINANCIAL ECONOMETRICS

On 15th to 19th October 2019

Organised by

Research and Post Graduate Department of Commerce

Government College, Attingal

Sponsored by Directorate of Collegiate Education Government of

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Workshop Organising Committee

Dr. V. Manikantan Nair, Principal, Government College, Attingal Dr. K. Pradeep Kumar, Co-ordinatror, Five day Workshop Dr. Lt. Sunilraj N.V, Co-coordinator, Five day Workshop Sunil S., Head of the Department of Commerce Dr. Anitha S., Associate Professor Dr. Sajeev H., Assistant Professor Dr. Sarun S.G, Assistant Professor Manikantan G., Assistant Professor Dr. Shanimon S., Assistant Professor Dr. Binu R., Assistant Professor

Editors

Dr. K. PRADEEP KUMAR

Dr. Lt. SUNILRAJ N.V.

January, 2020

Research and Post Graduate Department of Commerce

Government College, Attingal

ISBN: 978-81-936576-7-6

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

CONTENTS 1 REPORT OF FIVE DAY WORKSHOP ON FINANCIAL ECONOMETRICS 4 - 7 2 TECHNICAL SESSIONS I TO VIII Dr.S. KEVIN 8 - 27 3 TIME SERIES REGRESSION Dr. P.N. HARIKUMAR 28-32 4 INTRODUCTION TO GRETL Dr. K. PRADEEP KUMAR 33- 35 PERFORMANCE EVALUATION OF ENTREPRENEURSHIP DEVELOPMENT

SCHEMES OF NATIONAL HANDICAPPED 5 AND FINANCE DEVELOPMENT Dr. SHANIMON S 36 - 44 CORPORATION TRENDS IN GLOBAL AQUACULTURE 6 Dr. ANITHA S. 45 - 47 PRODUCTION PERFORMANCE EVALUATION OF SBI LIFE 7 ANSA S. 48 - 52 INSURANCE COMPANY ARIMA MODEL IN PREDICTING NSE NIFTY50 8 Dr. LAKSHMANAN M.P INDEX 53 - 61 FINANCIAL DEEPENING AND ECONOMIC 9 Dr. PRADEEP KUMAR N. DEVELOPMENT OF INDIA 62 - 66 ANALYSIS OF TRENDS AND GROWTH OF

10 DIGITAL RETAIL PAYMENTS SYSTEM IN SUNIL S. 67 - 78 INDIA KILLING THE GOLDEN GOOSE- THE CASE OF PRAGEETH P 11 PRIVATE BUSES IN KERALA Dr. ANZER R.N. 79 - 86 TRENDS AND GROWTH OF TOURISM IN 12 KERALA THANSIYA N. 87 - 92 A CROSS-SECTIONAL ANALYSIS ON THE INFLUENCE OF VARIOUS COSTS OF 13 Dr. K. PRADEEP KUMAR 93-96 AQUACULTURE ACTIVITIES ON REVENUE FROM AQUACULTURE

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

REPORT OF FIVE DAY WORKSHOP ON FINANCIAL ECONOMETRICS

MAIN THEME OF THE WORKSHOP to select Dr. S. Kevin, the former Pro-Vice Chancellor of and Financial Econometrics is selected as Professor of Commerce to impart the the topic for the workshop basically to train foundation training and Dr. Vijayamohanan the Commerce teachers in various Universities Pillai, Professor, Centre for Development of Kerala as the subject is recently included in Studies to impart advanced training in the the latest revision made in the UG and PG selected topic. Both trainers are well curriculum of various Universities in Kerala. recognized and handling the subject for many In Kerala University and many other years. Thus the Workshop was scheduled from Universities, the subject of Financial 15th the 19th October, 2019 as per their Econometrics has included under two major convenience. Frequent discussions with these modules in the Course „Quantitative two subject experts enables us to locate the Techniques and Financial Econometrics‟ in the major modules to be included in the workshop PG curriculum. As the content of the syllabus in a sequence from simple to complex. Thus is new to Commerce teachers, a short term the basic modules were assigned to Dr. S. training course in the form of a workshop is Kevin and the advanced modules were considered essential. At the same time, there is assigned to Dr. Vijayamohanan Pillai N. shift in focus of data analysis towards analysis of econometric data. Researchers are using PLAN FUND ALLOCATION Econometric modeling techniques in cross The College Council meeting held on sectional data, time series data and panel data. th In practice, these models are used for many 30 July, 2019 allocates an amount of Rs. practical situations involving measuring the 65000/ (Rupees Sixty Five Thousand Only) volatility, CAPM, simulation and the like. from the Plan fund sanctioned for FDP by the Thus the topic of workshop is highly relevant Directorate of Collegiate Education for the for Commerce and Management research now- conduct of Workshop by the Research and Post Graduate Department of Commerce. The a –days. Thus the Research and Post Graduate th Department of Commerce selected Financial Department meeting held on 14 August, Econometrics as the main theme of the 2019 assigned Dr. K Pradeep Kumar, workshop during this plan period (2019-20). Associate Professor of Commerce and Dr.Lt. Sunilraj N.V., Assistant Professor of SELECTION OF CHIEF TRAINERS Commerce as Co-ordinator and Co- coordinator respectively. An Organising As we need to impart both basic and Committee consisting of Principal, Head of the advanced level training in the subject with a Department and other Faculty members of direct focus on two groups of participants viz. Commerce was also constituted for preparing Teachers and Research Scholars, we decided

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 programme schedule and necessary stages for participants. The Five day Workshop is the successful conduct of the programme. inaugurated by Dr. V. Manikantan Nair, Principal Government College, Attingal. In his SELECTION OF PARTICIPANTS inaugural address he appreciates the efforts As the theme is focused on Teachers and taken by the Research and P.G Department of Research Scholars, the organizing committee Commerce in promoting research in decided to invite all teachers and research Commerce and Management subjects through scholars in various universities by clearly thes series workshops organized successfully explaining the theme and various modules every year. Dr. K. Pradeep Kumar, the Co- through the publicity materials. Personal e- ordinator of the Workshop presented the theme mails were sent to Heads of the Department of of the Workshop by quoting the series of Commerce of various Colleges and Workshops conducted successfully by the Universities to depute the teacher who is Research and Post Graduate Department of handling the subject or in need of training in Commerce since 2015 in every October. He the subject of Financial Econometrics. In mentioned the importance of this short term addition, brochures were sent to all Colleges, training programme in imparting skills to Univerisity Departments and Self Financing Teachers for handling the new subject and Colleges by clearly specifying the sequence of molding their research capabilities in modules included in different technical econometric data analysis. Dr. S. Kevin, the sessions. Participants are requested to register Chief trainer assigned for the first two days, in through e-mail by emphasizing their need for his special address congratulates the participation in the programme. Based on the Departmental initiates by the faculty for response of the participants, 21 teachers from promoting research in the subject. Prof. various Colleges of Kerala and 24 research Lakshmi Chandrasekhar, the Vice Principal of scholars were screened and selected for the College signified the importance of these participation in the programme. Selection workshop and express her gratitude to the memo is issued to all participants with strict Director of Collegiate Education for stipulation on adherence to attendance in all sponsoring these valid seminars and days. In addition to that 9 faculty members of workshops. Dr. Lt. Sunilraj N.V, the Co- Commerce Department, Govt. College, coordinator of the Workshop proposed vote of Attingal and 5 full-time research scholars of thanks in the inaugural session. The inaugural the Research Centre and 30 M.Com students session concluded by 9.50 a.m. of the Department participated in the TECHNICAL SESSIONS BY Programme. Thus a total of 89 participants DR. S. KEVIN attended the Five day workshop. Technical sessions from I to VIII were INAUGURATION AND THEME assigned to Dr. S. Kevin, the former Pro-Vice PRESENTATION Chancellor of University of Kerla and a former In the inaugural session, Prof. Sunil S., Head Professor of Commerce. The first technical of the Department of Commerce welcomed the session begins at 10.00 a.m with a brief

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 introduction of Chief Trainer by Dr. Anitha S., Collinearity was disussed with solutions and at Assistant Professor of Commerce, Government the concluding session, the concpt of Co- College, Attingal. In the first technical session integration and its intricacies were discussed. on Introduction to Financial Econometrics, Dr. The second day comes to an end at 4.30 p.m. S. Kevin, clearly presented the importance of Participants were given feedback form to Financial Econometrics and its applications in evaluate the technical sessions of Dr. S. Kevin Micro Economics including business and and the hospitality of the department in serving industry. He answered the simple to complex the guest participants. queries made by the participants patiently and encourage them to widen the subject base by TECHNICAL SESSIONS BY reading books in Basic Econometrics. Tea and Dr.VIJAYAMOHANAN PILLAI N snacks were served to participants as Dr. Vijayamohanan Pillai, Professor of refreshments during the sessions without any Economics, Centre for Development Studies tea break. Clean and safe drinking water is also has assigned 10 technical sessions in the provided in the corner of the workshop venue. workshop to impart practical skills in In the second technical session, the trainer developing econometric models through sound focused on the importance of Normality of theoretical base and practical applications. He Distributions in Econometric modeling. The explained the concepts in all technical sessions training was done through examples with through GRETL applications . The major sufficient proofs to clarify the doubts of themes of various technical sessions are participants. The second technical session advanced applications of Econometrics like concluded by 1.00 p.m. Lunch break was Time Series as a Stochastic Process, AR, MA given for 30 minutes . In the third technical and ARMA processors, Tests for non- session, the Chief trainer Dr. S. Kevin explains stationarity, ARIMA modeling, Co-integration the process of simple and multiple linear methods, Volatility models, Nature of Panel with the help of practical data models, Panel data Error component examples. In the last technical session of the models, and random effects model. In addition first day, serial correlation and the use of to these, he engaged a full session for training Durbin Watson Statistics was explained with participants in GRETL applications. After his case studies. The first day of the workshop session participants are given hands on th concluded at 4.30 p.m. On 16 October,2019, training in the computer lab with example files the technical session starts sharp at 9.30 a.m. supplied by the Chief Trainer.In the practical In the morning sessions, Stationarity of Time sessions, Dr. Binu R., Assistant Professor of series data and Unit root test and the problem Commerce assisted the Chief trainer in of heteroskedasticity were explained. A delivering the practical knowledge. In the complete learning atmosphere was clearly whole three days assigned to Dr. visible in the whole sessions. Tea and snacks Vijayamohanan Pillai. N., the participants are were served during the sessions. The after anxiously hearing and observing the new sets noon sessions after lunch started at 1.30 p.m . of knowledge and appreciated his skills in In the beginning session, the problem of Multi training. In the whole three days also the

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 participants are provided with refreshments knowledge gained from the workshop for their and lunch on time. The organsing committee academic and future research purposes. The collected the Feed Back form given for Report of the Workshop is presented by Dr. evaluating the three day sessions assigned to Binu R, Assistant Professor of Commerce with Dr. Vijayamohanan Pillai. N. a briefing on all technical sessions by the two resource persons. Certificates were distributed VALEDICTORY SESSION to all participants by the Principal and Dr. The whole technical sessions completed by Vijayamohanan Pillai. N. Dr. K. Pradeep 3.00 p.m on 19th October, 2019. The Kumar, the Co-ordinator of the workshop valedictory session started after tea break. The proposed Vote of Thanks to participants and session was chaired by Prof. S. Sunil, Head of all stakeholders for its successful conduct. The the Department of Commerce. Prof. Valedictory session concluded at 4.00 p.m SibuKumar D, the incharge Principal delivered with chanting of National Anthem. All the valedictory address after the presidential participants relived from the institution at 4.30 address. In the presidential address, the head of p.m. the department express warm regards to all Dr. K. PRADEEP KUMAR participants and suggest them to use the Dr. Lt. SUNILRAJ N.V

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

TECHNICAL SESSIONS I TO VIII

Dr. S. KEVIN

Former Professor and Pro-Vice Chancellor, University of Kerala

Technical Session I objective to provide numerical values to the parameters of economic relationships. Origin of Econometrics Econometrics applies statistical methods and Jan Tinbergen is considered by many to be mathematical techniques to economic data to one of the founding fathers of econometrics. explain phenomena and create models. A He was a famous Dutch Economist. He won model is a simplified representation of a real the first Nobel Prize in Economics in 1969 world process. All the variables which the along with Ragnar Frish for developing experimenter thinks are relevant to explain the applied dynamic models in analysis of phenomenon are included in the model. It is economic processes. Ragnar Anton Kittil "the quantitative analysis of actual Frisch a Norwegian Economist is credited economic phenomena” with coining the term in the sense in which it Econometrics and Statistics is used today. Econometrics differs from ordinary statistics. What is Econometrics? In ordinary statistics, the empirical data is Econometrics means “economic measurement” collected, recorded, tabulated and used in Econometrics may be defined as the social describing the pattern in their development science in which the tools of economic theory, over time. Ordinary statistics is a descriptive mathematics, and statistical inference are aspect of economics. It does not provide either applied to the analysis of economic the explanations or measurement of the phenomena. [Arthur S. Goldberger, parameters of the relationships. Econometrics Econometric Theory, John Wiley & Sons, is typically used to explain how the economy New York, 1964, p. 1.] works. It deals with the measurement of Econometrics deals with the measurement of economic relationships. economic relationships (e.g., price and Basic Tool in Econometrics demand) . It is an integration of economics, A basic tool for econometrics is the multiple mathematical economics and statistics with an model. In modern

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 econometrics, other statistical tools are 3. Specification of the Econometric Model of frequently used, but linear regression is still Consumption the most frequently used starting point for an But relationships between economic variables analysis. are generally inexact. For example, size of Methodology of Econometrics family, ages of the members in the family, 1. Statement of theory or hypothesis. family religion, etc., are likely to exert some 2. Specification of the mathematical model of influence on consumption the theory To allow for the inexact relationships between 3.Specification of the statistical, or economic variables, the econometrician would econometric, model modify the deterministic consumption function 4. Obtaining the data as follows: 5.Estimation of the parameters of the Y = β1 + β2X + u econometric model where u is known as the disturbance, or 6. Hypothesis testing error term. 7. Forecasting or prediction It may well represent all those factors that 8. Using the model for control or policy affect consumption but are not taken into purposes account explicitly. consumption but are not taken into account explicitly 1. Statement of Theory or Hypothesis 4. Obtaining Data Keynes postulated that the marginal propensity The Y variable in this example is the aggregate to consume (MPC), the rate of change of (for the economy as a whole) personal consumption for a unit (say, a dollar) change consumption expenditure (PCE) and the X in income, is greater than zero but less than 1. variable is gross domestic product (GDP), a 2. Specification of the Mathematical Model measure of aggregate income, for 1982 – 1996 of Consumption period (annual data). Y = β1 + β2X 0 < β2 < 1 5. Estimation of the Econometric Model where Y = consumption expenditure and X = The regression analysis is the main tool used income, and where β1 and β2, known as the to obtain the estimates. parameters of the model, are, respectively, the Thus, the estimated consumption function is: intercept and slope coefficients. Y = −184.08 + 0.7064 X

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

6. Hypothesis Testing misspecification. The test has proven to be useful in detecting model misspecification. In our example we found the MPC to be about The Purpose of Econometrics 0.70. Is this value statistically significant. This The purpose of Econometric analysis is to can be tested using inferential statistics. produce the best model that will minimize the 7. Forecasting or Prediction forecast errors. We may use the model to predict the future Structure of Econometric Data value(s) of the dependent variable Y on the 1. Time series data basis of known or expected future value(s) of Time series data give information about the the explanatory variable X. But forecast numerical values of variables from period to errors are inevitable given the statistical period and are collected over time nature of our analysis. Residuals (difference 2. Cross section data between the predicted values and the actual The cross section data give information on the values) represent the forecast errors. variables concerning individual agents (e.g., Model Misspecification consumers or producers) at a given point of Model misspecification occurs when some time. important variables are omitted.The model 3. Panel data then will not account for some important The panel data are the data from repeated relationships or linearities. Model survey of a single (cross-section) sample in misspecification will cause bias in the different periods of time. remaining parameter estimates. An important 4. Dummy variable data source of bias in OLS estimation is omitted They reflect only the presence/absence of a variables that are correlated with the included characteristic. For example, the variable explanatory variables.Often the reason for `gender‟ takes two values – male or female. omission is that these variables are These values can be represented as „1‟ for unobservable (e.g., human ability). In such male and „0‟ for female. cases, data on proxy variables can be used. Features of Economic Data Ramsey RESET Test Normality of data James B. Ramsey (1969) proposed a Serial correlation or auto correlation misspecification test known as Regression Stationarity of data Specification Error Test (RESET). The test Cointegration of two data series helps to find whether the model suffers from

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Causality of variables Example: Sales is dependent variable Multicollinearity Advertisement is independent variable. Heteroskedasticity Regression Equation- Example Econometrics includes a study of these features of economic data Y – Sales (3000,3500, 4000,4500,5000) X – Advertisement expenditure TECHNICAL SESSION II & III (15,20,25,30,35) Regression Analysis Y = a + b X Association between Variables Y = 1500 + 100 X The statistical tools used to study association/ This is an exact or deterministic relationship. relationships between variables are Correlation and Regression. Correlation Studies the Thus Regression analysis is a set of statistical methods used for the estimation of relationships association between variables that may be between a dependent variable and one or related. A measure of covariation. Indicates more independent variables. Can be utilized to magnitude and direction of covariation. Karl assess the strength of the relationship between Perason‟s Coefficient of Correlation (r) is the variables and for modeling the future widely used measure of correlation. r may be relationship between them. positive or negative.r varies from (-)1 to 1. Coefficient of determination is r square (R2). Regression analysis includes several Measures the extent of variation explained by variations, such as linear, multiple linear, and the relation. In research, universe parameters are nonlinear. The most common models are inferred from sample statistics. When correlation simple linear and multiple linear. is significant, the inference is that correlation Multiple Linear Regression exists in the population also. p-value indicates Multiple independent variables are used in the the probability of the correlation occurring by model. The mathematical representation of chance. If p-value is less than 0.05, correlation multiple linear regression is: is stated to be significant. Y = a + b1X1 + b2X2 + b3X3 + ϵ

Regression analysis is the Study of relationship Where:Y – dependent variable, X1, X2, X3 – between variables. Related variables are independent (explanatory) variables a – categorised as: Dependent and independent. intercept (constant) b1, b2, b3 – slopes (regression coefficients) ϵ – residual (error)

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Output of Regression Analysis OLS Technique The output consists of four important pieces of Mathematically, regression uses a linear information: function to approximate (predict) the (a) the R2 value represents the proportion of dependent variable. Error is an inevitable part in the dependent variable that can be of the prediction-making process. Regression explained by our independent variable uses a technique known as Ordinary Least However, R2 is based on the sample and is a Square (OLS) to reduce error to the lowest positively biased estimate of the proportion of level. OLS technique tries to reduce the sum of the variance of the dependent variable squared errors by finding the best possible accounted for by the regression model (i.e., it value of regression coefficients (β0, β1, etc.) is too large) (b) an adjusted R2 value which corrects Regression Analysis- Linear Model positive bias to provide a value that would be Assumptions expected in the population There exists a linear and additive relationship (c) the F value and its p-value indicating the between dependent (DV) and independent statistical significance of the regression model. variables (IV). By linear, it means that the F value is the ratio of explained variance to change in DV by 1 unit change in IV is unexplained variance of the model constant. By additive, it means the effect of X (d) the coefficients for the constant and on Y is independent of other variables. independent variable (with their t-values and There must be no correlation among p-values) which is the information you need to independent variables. Presence of correlation predict the dependent variable, using the in independent variables lead independent variable to Multicollinearity. If variables are correlated, The Standard Error it becomes extremely difficult for the model to Is a measure of the precision of the model. It determine the true effect of IVs on DV. reflects the average error of the regression The sumof the residuals (error) is zero. model. We want the standard error to be as The error terms must possess constant small as possible.The standard error is used to variance. Absence of constant variance leads get a confidence interval for your predicted to heteroskedestacity. values.

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

The error terms must be uncorrelated. Presence Homoskeasticity Vs. Heteroskedasticity of correlation in error terms is known describes a situation in as Autocorrelation. which the error term is the same across all The dependent variable and the error terms values of the independent variables. must possess a normal distribution. (the violation of TECHNICAL SESSION IV homoscedasticity) is present when the size of Hetereoskedasticity the error term differs across values of an Hetero (different or unequal) is the opposite of independent variable. The impact of violating Homo (same or equal)…Skedastic means the assumption of homoscedasticity is a matter spread or scatter…Homoskedasticity = equal of degree, increasing as heteroscedasticity spread Heteroskedasticity = unequal spread. increases. Refers to non constant volatility.A sequence of random variables is heteroskedastic, if the The Types of Heteroskedasticity random variables have different Unconditional: is predictable, and most often signifying high and low volatility.A sequence relates to variables that are cyclical by nature. of random variables is called homoskedastic if can include higher retail sales reported during it has constant variance. the traditional holiday shopping period or the Volatility Clustering increase in usage of electricity during warmer months. future periods of high and low Benoit Mandelbrot defined it as the volatility can be identified. observation that "large changes tend to be followed by large changes, of either sign, and Conditional: is not predictable by nature. small changes tend to be followed by small There is no sign that leads analysts to believe changes”.High volatility and low volatility data will become more or less scattered at any occur in alternating clusters. This volatility point in time. Financial products are clustering is known as heteroskedasticity. considered subject to conditional When heteroskedasticity is autocorrelated, it is heteroskedasticity as not all changes can be known as auto regressive conditional attributed to specific events or seasonal heteroskedasticity (ARCH ). changes. future periods of high and low volatility cannot be identified

Examples: share prices, exchange rates

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Consequences of Heteroskedasticity  Glejser test Homoskedasticity is needed to justify the usual  Brown–Forsythe test t tests, F tests, and confidence intervals for  Harrison–McCabe test OLS () estimation of  Breusch–Pagan test the linear regression model, even with large  sample sizes. In case of heteroskedasticity, the  Cook–Weisberg test OLS estimators are no longer the BLUE (Best Models of Heteroskedasticity Linear Unbiased Estimators) because they are ARCH –type models are commonly employed no longer efficient, so the regression in modeling financial time series that exhibit predictions will be inefficient too. time-varying volatility and volatility clustering Importance of Heteroskedasticity (heteroskedasticity). Commonly used ARCH Is important in interpreting linear regression models are ARCH (Auto regressive Is the extent to which the variance of the conditional heteroskedasticity) model and residuals depends on the predictor variable. GARCH (Generalized auto regressive The residual in linear regression is the amount conditional heteroskedasticity) model of difference between the actual outcome and the outcome predicted by the model. TECHNICAL SESSION V Heteroscedasticity (the violation of Multicollinearity homoscedasticity) is present when the size of The Independent variables in a multiple the error term (residual) differs across values regression model should be independent. Multi of an independent variable. The residuals collinearity occurs when independent represent the error of your model. If the variables in a regression model are correlated amount of error in your model changes as the It can cause problems when you fit the model variables change, then you do not have a very and interpret the results. good model. Why is Multicollinearity a problem? Tests for Heteroskedasticity A key goal of regression analysis is to estimate There are several methods to test for the the relationship between each independent presence of heteroscedasticity variable and the dependent variable. The  Levene's test regression coefficient represents the mean  Goldfeld–Quandt test change in the dependent variable for 1 unit  Park test change in an independent variable when

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 you hold all of the other independent variables between independent variables and the constant.It becomes difficult for the model strength of that correlation. Statistical software to estimate the relationship between each calculates a VIF for each independent variable independent variable and the dependent Calculation of Tolerence and VIF variable independently because the The VIF may be calculated for each predictor independent variables tend to change in unison by doing a linear regression of that predictor when there is multicollinearity. on all the other predictors, and then obtaining Types of Multicollinearity the R2 from that regression. Tolerance is (1- Data multi collinearity: present in the data set R2). The VIF is 1/(1-R2). It is called the itself.Structural multi collinearity: when a variance inflation factor because it estimates new independent variable is created from the how much the variance of a coefficient is data set (for example, a variable value is “inflated” because of linear dependence with squared to create another variable) other predictors. Thus, a VIF of 1.8 tells us that the variance (the square of the standard When there is no need to reduce error) of a particular coefficient is 80% larger Multicollinearity? than it would be if that predictor was If you have only moderate multi collinearity. completely uncorrelated with all the other If multi collinearity is not present for the predictors. independent variables that you are particularly Variance Inflation Factor (VIF) interested in. VIFs start at 1 and have no upper limit. A If your primary goal is to make predictions, value of 1 indicates that there is no correlation and you don‟t need to understand the role of between this independent variable and any each independent variable. others. VIFs between 1 and 5 suggest that Testing for Multicollinearity there is a moderate correlation, but it is not Tolerance and the Variance Inflation Factor severe enough to warrant corrective measures. (VIF) are two collinearity diagnostic factors VIFs greater than 5 represent critical levels of that can help to identify multi collinearity. The multi collinearity where the coefficients are variable‟s tolerance is 1-R2. A small tolerance poorly estimated, and the p-values are value indicates multicollinearity. The Variance questionable.If the value of tolerance is less Inflation Factor (VIF) is the reciprocal of than 0.1 and, simultaneously, the value of VIF Tolerance or 1/Tolerance. The variance inflation factor (VIF) identifies correlation

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

10 and above, then the multi collinearity is 2. Skewness method problematic. 3. Tests of Normality How to resolve structural multicollinearity? Histogram method is a graphical method Structural multi collinearity can be resolved by Skewness method and Tests of Normality centering the independent variables. Centering make use of descriptive statistics. the variables is also known as standardizing Histogram Method the variables by subtracting the mean. This Looks at a histogram of the data with the process involves calculating the mean for each normal curve superimposed. continuous independent variable and then Normal Data subtracting the mean from all observed values of that variable. Then, use these centered variables in the model. How to resolve Data Multicollinearity? The potential solutions include the following: Remove some of the highly correlated independent variables. Linearly combine the independent variables, such as adding them Non-normal Data together. Perform an analysis designed for highly correlated variables, such as principal components analysis or partial least squares regression. If you can accept less precise coefficients, or a regression model with a high R-squared but hardly any statistically significant variables, then not doing anything Evaluation of Histogram Method about the multicollinearity might be the best This method provides a sense of the normality solution. of data. All samples deviate somewhat from TECHNICAL SESSION VI normal, so the question is how much deviation Normality of Data from the black line indicates “non-normality” There are three interrelated approaches to Histogram provides no hard-and-fast rules. determine normality of data 1. Histogram method

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Descriptive Statistics The value of skewness is 1.797. The question Quantities computed from the data set to is “how much” skewness render the data non- describe different characteristics of the data set normal. This is an arbitrary determination, and (central value, variability, symmetry, sometimes difficult to interpret using the peakedness). Mean, median, mode, Measures values of Skewness. of variability, Minimum, maximum, range, Tests for Normality Quartiles, interquartile range, percentiles Standard deviation, variance(Mean +/- 2 SD = Tests for normality take into account both 95 percent of observations) Skewness :zero Skewness (symmetry) and Kurtosis value represents symmetry; positive value (peakedness) simultaneously. The implies right skewed distribution; negative Kolmogorov-Smirnov (K-S) test and Shapiro- value implies left skewed distribution. Wilk (S-W) test are designed to test normality Measures of peakedness/flatness :Kurtosis: by comparing your data to a normal absolute kurtosis for normal distribution is 3; distribution with the same mean and standard relative kurtosis = absolute value – 3Positive deviation of your sample. Jarque–Bera test is value indicates peaked curve.Negative value another test used for testing normality of data indicates flat curve. Interpretation of test results Skewness measures the symmetry of the If the test result is significant (p-value less distribution. Skewness is 0 in a normal than .05), then the data are non-normal. If the distribution; so the farther away from 0, the test is NOT significant (p-value greater than more non-normal the distribution. A positively 0.05), then the data are normal. skewed distribution has scores clustered to the left, with the tail extending to the right. A Example negatively skewed distribution has scores clustered to the right, with the tail extending to the left

Evaluation of Skewness Method

The histogram above for variable2 represents positive skewness (tail extending to the right)

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Jarque Bera (JB) Test JB = [5] X [1.5625 + 0.0625] JB = 8.125 The Jarque–Bera test is a goodness-of-fit TECHNICAL SESSIONS VII &VIII test of whether sample data have the skewness and kurtosis matching a normal distribution. ANALYSIS OF FINANCIAL TIME SERIES The test is named after Carlos Jarque and Outline Anil K. Bera. Durbin-Watson test for testing serial The test statistic JB is defined as: correlation or autocorrelation JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2] Unit root test for testing stationarity of time where series data n is the number of observations, Cointegration of nonstationary variables S is the sample skewness, C is the sample kurtosis, What is a Time Series? k is the number of regressors (being 1 outside A series of data relating to different equally a regression context) spaced time intervals, such as years, months, For Normal Distribution days, hours, etc. Values of the Distribution Examples: n = 30 S = 0 C = 3 k = 1 The closing prices of the share of ICICI Bank Formula for test statistic for 320 consecutive days. US Dollar exchange JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2] rates recorded for 280 consecutive trading days Computation of test statistic Significance of Time Series Analysis 2 2 JB = [(30 – 1 + 1)/6] X [0 + ¼(3 – 3) ] JB = [5] X [0] Time series analysis attempts to understand the JB = 0 past and predict the future. Systematic For Non-normal Distribution procedures and techniques, including statistical tools and econometric models, are used for the Values of the Distribution purpose.The primary objective is to detect n = 30 S = 1.5 C = 4 k = 1 regularities and structures in data that will be Formula for test statistic helpful in forecasting future values of the JB = [(n – k + 1)/ 6] x [S2 + ¼(C – 3)2] variable.The forecasted future values are Computation of test statistic useful in planning and policy making activities JB = [(30 – 1 + 1)/6] X [1.52 + ¼(4 – 3)2]

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

For example, the forecasted exchange rates of and the lagged time series indicates the foreign currencies can help the exporters and presence of autocorrelation in the original time importers in planning and managing their series. Autocorrelation may be defined as the foreign exchange risk. correlation between observations of a time A Financial or Economic Time Series series at different distances apart. Contains data regarding financial and Autocorrelation confirms non-randomness in economic variables such as interest rates, share the data series. prices, exchange rates, etc. Data in a financial Significance of Serial Correlation or economic time series are expected to be The essence of serial correlation is to see how random variables as fluctuations in these data sequential observations in a time series affect are uncertain and unpredictable. The daily US each other. If we can find the structure in these Dollar exchange rates are considered as observations it will help us improve our random variables and the series may be forecasts and simulation accuracy. considered as a stochastic time series (as Durbin-Watson Test opposed to a deterministic time series). Are the data in a financial time series Durbin-Watson test is popularly used to detect random variables? the presence of autocorrelation in time series Random Variables data. A test that the residuals from a linear Random variables are independent of each regression or multiple regression are other. If the data are related to each other, they independent. Original time series can be taken represent non-random variables. as the independent series (Xt).Lagged time

Randomness of Data in a Financial Time series can be taken as the dependent series (Yt) Series Regression equation: Yt = α + β Xt An important feature to be examined. Autocorrelation, also known as serial The residuals from the linear regression correlation or serial dependence, is an between the original series and the lagged important feature of time series data. If a new series can be used for the test series is created by taking the daily exchange rates with time lag of one day and is compared with the original time series, existence of a covariation between the original time series

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Original and lagged time series Hypothesis for Testing Share prices of Asian Paints

Days Spot price Spot price (Original) (Lagged) 1 1279.8 1251.75 2 1251.75 1224.7 3 1224.7 1209.5 4 1209.5 1247.8 5 1247.8 1201.4 6 1201.4 1225.95 Residual Calculation 7 1225.95 1219.05

8 1219.05 1261.2 ei (ei - ei-1)2 ei2

9 1261.2 1250.3 4.97 - 24.67 10 1250.3 1267.6 -15.46 417.39 239.1 11 1267.6 1259 -24.28 77.73 589.48 12 1259 1235.4 17.61 1754.54 310.04 13 1235.4 Regression equation: -37.83 3073.46 1431.17 -2.33 1260.28 5.43 Yt = α + β Xt + et -15.02 161.13 225.73

Yt = 944.75 + 0.236 Xt + et 28.75Yt = 944.75 1916.55+ 0.236 Xt + et 826.8 This equation can be used to calculate the 7.91 434.61 62.52 predicted values of the variable. The difference 27.78 394.91 771.68 15.1 160.85 227.9 between the observed values and the predicted -6.47 465.28 41.91 values are the residuals Total 10116.74 4756.44

Days Spot price Predicted Residuals (Lagged) prices Testing of Hypothesis 1 1251.75 1246.78 4.97 d becomes smaller as the serial correlation 2 1224.7 1240.16 -15.46 increases. Upper and lower critical values, dU 3 1209.5 1233.78 -24.28 4 1247.8 1230.19 17.61 and dL have been tabulated for different values 5 1201.4 1239.23 -37.83 of k (the number of explanatory variables) and 6 1225.95 1228.28 -2.33 n (sample size) 7 1219.05 1234.07 -15.02 8 1261.2 1232.45 28.75 If d < dL reject H0 : ρ = 0 9 1250.3 1242.39 7.91 If d > dU do not reject H0 : ρ = 0 10 1267.6 1239.82 27.78 If dL < d < dU test is inconclusive 11 1259 1243.9 15.1 12 1235.4 1241.87 -6.47

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Durbin Watson Test Statistic Stationarity Stationarity and autocorrelation are two important features of time series data. A time series is an example of a stochastic process, which is a sequence of random variables ordered in time. A time series may be

stationary or non stationary. It is said to be stationary if its mean and variance are constant Critical Values of Durbin Watson Statistic over time or are independent of time.

Sample Significance k = 1 k = 1 k= 2 k= 2 A stationary time series is one whose size level statistical properties (such as mean and DL DU DL DU variance) are constant over time. 30 0.05 1.35 1.49 1.28 1.57

40 0.05 1.44 1.54 1.39 1.6 Non-stationary Time Series 50 0.05 1.5 1.59 1.46 1.63 A time series may be subdivided into subsets 60 0.05 1.55 1.62 1.51 1.65

80 0.05 1.61 1.66 1.59 1.69 of different time periods within the overall 100 0.05 1.65 1.69 1.63 1.72 time period of the time series. Mean and 150 0.05 1.72 1.75 1.71 1.76 variance for the different subsets may be 200 0.05 1.76 1.78 1.75 1.79 calculated. The mean may vary for the

different subsets depending on whether the An Example values are increasing or decreasing over time. Time series data: Share prices of Asian Paints The variance may also vary for the different for 497 days sub periods. A time series whose mean and Durbin-Watson test result: variance vary across different time periods is d = 1.9844 said to be non-stationary dL (for α = 0.05 and k =1) = 1.76 (n = 200) Asian Paints Share Prices-Mean and Variance Period Observations Mean Variance dU (for α = 0.05 and k =1) = 1.78 (n = 200) 21st March 2017 - 11th Aug. 2017 100 1122.73 1270 Since d > dU, H0 cannot be rejected; it is 14th Aug 2017 - 5th Jan. 2018 100 1168.02 1203.23 accepted. 8th Jan. 2018 - 4th June 2018 100 1176.35 4026.11 Conclusion: There is no autocorrelation in the 5th June 2018 - 30th Oct. 2018 100 1319.69 5666.42 share price series. 31th Oct 2018 - 20th March 2018 97 1372.28 3210.71 Entire period 497 1230.97 12310.3

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Importance of Stationarity Why should we worry whether a time series is stationary or not? If it is nonstationary, it is not possible to generalize it to other time periods. If we have two independent non-stationary series, then we may find evidence of a relationship when none exits (i.e. spurious Correlogram regression problem). The relationship will be Autocorrelation is the correlation between genuine only if the two series are cointegrated. observations of the original time series and the Economic and Financial Time Series lagged time series. The lagged time series may be created with lag of one time period or more Examples: exchange rates, share prices, These than one time period. ACF (Autocorrelation are often trending and consequently non- Function) gives the correlation coefficients stationary. It is important to test whether the calculated for several lagged time series with economic or financial time series is non- increasing lag periods. A plot of the stationary. correlation coefficients against the lag length is Tests of Stationairity known as correlogram.

1. Graphical analysis ACF of Asian Paints Share Prices 2. Correlogram 3. Unit root test Graphical Analysis Plot the time series in an XY graph. Gives an initial clue as to whether the time series is stationary or not. Starting point for more formal tests of stationarity.

Graph of Share Prices of Asian Paints

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Correlogram of Asian Paints Share Prices Null hypothesis: There is unit root and time series in non- stationary Alternative hypothesis: There is no unit root and time series is stationary The null hypothesis is rejected if the test statistic is more negative than the critical value ACF Interpretation Critical Values for DF and ADF tests The table shows whether the correlation Significance level CV for constant CV for constant and but no trend trend coefficients are significant. Q-stat and its p- value indicates if the sum of the 5 percent (-) 2.86 (-) 3.41 autocorrelation coefficients is statistically 1 percent (-) 3.43 (-) 3.96 significant. ACF has to be examined to see if the correlation of the time series over several ADF test Result for Asian Paints Share lags decays quickly or slowly. If it decays Prices slowly, it is an indication that the time series is non-stationary.

Unit Root Test A statistical procedure used to test whether a time series is non-stationary and possesses a unit root. A series which has a unit root is non-stationary. Augmented Dickey-Fuller (ADF) test is commonly used to test the null hypothesis that a time series has a unit root and is non-stationary.

Dickey Fuller and Augmented Dickey Fuller Null Hypothesis: t series has a unit root Tests Exogenous: Constant Dicky-Fuller and Augmented Dicky Fuller Lag Length: 1 (Automatic Based on AIC, MAXLAG=10) tests for unit root. t-Statistic Prob.*

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Augmented difference of difference) to make it stationary, Dickey-Fuller test statistic -1.175441 0.686438 Test it is integrated of order two, denoted as I(2). critical 1% values: level -3.443379 If it has to be differenced d times to make it 5% level -2.867168 stationary, it is said to be integrated of order d, 10% level -2.569812 denoted as I(d). A stationary time series is

integrated of order zero, denoted as I(0). Inference Cointegration The ADF test statistic (tau value) is (-) In the case of two independent non-stationary 1.17544. As this value is not more negative series, we may find evidence of a relationship than the critical values, the null hypothesis when none exists (i.e. spurious regression cannot be rejected. Null hypothesis that the problem). The relationship will be genuine time series has unit root and is nonstationary only if the two series are cointegrated. It is accepted becomes necessary to examine the existence of Meaning of Unit Root Test cointegration in such cases. In an autoregressive (AR) statistical model of a Concept of Cointegration time series, the AR parameter is assumed to be An old woman and a boy are on random walk 1. In a data series Y modelled by t in the park. Information about the boy‟s Y = aY + e t+1 t t location tells us nothing about the old „a‟ is an unknown constant woman‟s location. There is no cointegration Unit root test would be a test of the hypothesis An old man and his dog are joined by a leash. that a = 1, against the alternative that „a‟ is The man and the dog are each on a random less than 1. If the time series has unit root (a = walk. But they cannot wander too far from 1), the series is said to be nonstationary. each other because of the leash. The random Integrated Time Series processes describing their paths are A nonstationary time series is known as an cointegrated. integrated time series or series with stochastic Testing for Cointegration trend. It could be made stationary by To test whether two I(1) series are differencing (that is, subtracting the preceding cointegrated, we examine whether the value from it current value). If a time series residuals are stationary or I(0). In the case of becomes stationary after differencing it once, it two nonstationary I(1) series, Y and X, if the is said to be integrated of order one, denoted as residuals of the regression Yt = α + β Xt + et are I(1). If it has to be differenced twice (i.e.,

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 stationary, then the variables are said to be cointegrated. The Engle-Granger test is used for testing cointegration between variables. Example Two series are considered for the analysis Nifty values are taken as the independent variable. Asian Paints share prices are taken as the dependent variable No. of observations: 497 Regression analysis is proposed for studying the relationship between the variables.The two series are financial time series and hence stationarity of the series have to be examined. If the series are nonstationary, then existence of cointegration has to be examined before the regression results can be relied upon. Results of Engle-Granger Cointegration Test

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

No. of observations: 76 Regression analysis is proposed for studying the relationship between spot price and futures price. The two series are financial time series and hence stationarity of the series have to be examined. If the series are nonstationary, then existence of cointegration has to be examined before the regression results can be relied upon. Results of Engle-Granger Cointegration Test

Inference The two time series of Nifty values and Asian Paints share prices are nonstationary. The Residual series of the regression between the two series is also non stationary. Cointegration exists only if the residual series is stationary. There is no cointegration between the two series, as the residuals series is nonstationary. The regression results are not reliable, in the absence of cointegration.

Another Example Two price series are considered for the analysis. Spot prices of Asian Paints are taken as the independent variable. Futures prices of Asian Paints are taken as the dependent variable

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Inference The two time series of Spot prices and Futures prices of Asian Paints are nonstationary. The Residual series of the regression between the two series is stationary. Cointegration exists if the residual series is stationary. There is cointegration between the two series, as the residuals series is stationary. The regression results are reliable, as the variables are cointegrated.

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

TIME SERIES REGRESSION

Dr. P.N. Harikumar Associate Professor, Catholicate College,

INTRODUCTION

Time series is a set of observations generated A time series is said to be a consequent effect sequentially in time. If the set is continuous of four possible forces acting at a point of then the time series is continuous. If the set is time. They are trend, seasonal variation, discrete then the time series is discrete. oscillation and random component. A Time Generally discrete time series are more series is said to be an effect of these four adopted in Econometric studies. “Sequential components and the researcher may choose in time” is only to mean successive from the two alternative models ie. Additive observations and hence a sequence of or multiplicative models. In an additive observations observed over a space may also model, these forces or components are added be considered as a time series. Generally the up to give time series. This means that at term „Time Series‟ is used to refer this kind every point of time these four forces may be of data also. Analysis of discrete time series in operation and hence there may be an effect is relatively easier. Usually time series are due to Trend (T), an effect due to Seasonal observed over equal interval of time. Variation (S), Oscillation (O) and Random However, this is not a restriction on the component (R) and the value of observation scope of time series analysis. of the variable at that point of time is taken The usage of time series models is twofold: as the sum of these four effects. It the 1. Obtain an understanding of the underlying variable is taken as y and its observation at

forces and structure that produced the time „t‟ is denoted by yt then it is assumed to observed data be given by yt = Tt+St+Ot+Rt. Similarly the 2. Fit a model and proceed to forecasting and alternative model will be obtained by

monitoring. multiplying these effects to get yt. Examples of Time series data A study of time series aims at identifying the Business and Economics : weekly share possible contributions of these effects and prices, monthly profits, sales forecasting, after eliminating these effects the remaining budgetary analysis, stock market analysis, series called as „Residual Series‟ is taken up yield projections for high end solutions using different types Meteorology: daily rainfall, wind speed, of models. However, the identification of temperature effects due to Trend and Seasonal Variations Sociology : crime figures, employment are highly valuable in Econometric Studies. figures Definitions Trend: A smooth movement of observations DESCRIPTIVE UNDERLYING FORCES either upwards or down wards over a

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 relatively long period of time is called a turn a time series into Non Stationary. Using Trend. Possible reasons for trend in Regresson with two time series may produce observed series may be attributed to Spurious or nonsense regression. The R- development of dependent factors like square may be very high, yet the regression technology, health and the like. will be meaningless. Seasonal Variation : In certain cases of time The key concepts to be considered will be the series a systematic behavior of up and down concepts called Stochastic Processes, which movement can be observed repeatedly over a are taken as concepts generating the time fixed period of time interval. As an example, series. Stationary Processes, Pure Random textile sales may be seen to vary in a Processes, Non-stationary Processes, systematic manner over a year. This type of Integration Variables, Co-integrtion and Unit series may provide valuable information that Root Test are explained in sequence. may be useful in future predictions. Random Process or Stochastic Process is a Oscillations: Suppose a time series is devoid collection of random variables, usually of effects due to trend and seasonal variation, ordered in time. Such processes can be then it may be observed to oscillate around a defined on other spaces also. They may be constant value. A look at recordings from further classified into discrete and continuous ECG or variation in the movement of sensex depending upon the observations are in index over a normal day may be examples. continuum or at discrete points. Random Component: Above all the other Stationary Stochastic Process: A stochastic effects having a possibility of explanation, process is said to be stationary if its mean there may be a large number of forces acting and variance are constant over time and the and adding a random effect to the series. covariance between two time points depends A study of time series is directed at the upon only the time difference and not on the estimation of explainable effects due to choice of the time points. A majority of time Trend and Seasonal Variation; eliminate series assumes that the underlying process is them from the observed series; try for stationary. If a time series is not stationary in explanation of the residual series using this sense, it is called as non-stationary. For statistical models. In order to achieve them, example Random Walk Model, used in the there are conventional methods having a well study of stock prices comes under this head. built computational capability. Also, there Random Walk Model is considered in two are high end methods involving modeling ways; Random Walk Model with Drift and under different assumptions. Random Walk Model without Drift. Let Yt Use of time series is an enchanting area in be the variable observed at time point „t‟, Time Series Analysis. The types of analyses then the RWM is stated as vary with respect to the type of the series. Yt= Yt-1 +ut Empirical work assumes that the underlying Where ut is an error term with mean 0 and time series is stationary. The second concept variance σ2.It can be observed that this concerned with time series analysis is Auto results in

Correlation. Presence of auto correlation may Y1 = Y0+u1

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Y2 = Y1+u2 = Y0+u1+u2 the Y and X time series. Subjecting these Y3 = Y2+u3 = Y0+u1+u2+u3 time series individually to unit root analysis As the time goes on increasing, the mean will and suppose you find that they both are I(1); remain the same but the variance will go on that is they are auto-regressive of order 1 increasing making the series non-stationary. with autoregressive coefficient being 1; that Similarly, the RWM with drift can be written is, they contain a unit root. Suppose, then, as Yt=ᵭ +Yt-1+ut where ᵭ is the drift that we regress Y on X as follows. parameter. The difference between Yt = β1+β2Xt+ut (1)

Yt – Yt-1 = ᵭ+ut. drifts upwards or downwards Let us write this as depending upon the value ᵭ. Here, both mean ut = Yt-βt-β2Xt (2) and variance go on increasing over time, Suppose we now subject ut to unit root suggesting that the series is non-stationary. analysis and find that it is stationary; that is. Unit Root Stochastic Process: Suppose we It is I(0). This is an interesting situation, for write the RWM as Yt=ρYt-1 +ut where although Yt and Xt are individually I(1), that -1 ≤ ρ ≤ 1 is the classical autoregressive is, they have stochastic trends, their linear model. When ρ is 1, the model is called Unit combination (2) is I(0). So to speak,the linear Root Stochastic Process. The model leads to combination cancels out the stochastic trends a non stationary series observed earlier. Thus in the two series. If you take consumption non-stationarity, Random Walk and Unit and income as two I(1) variables, savings Root stochastic process are equivalent defined as income – consumption could be concepts . If lρl ≤1, the model leads to I(0). As a result, a regression of consumption stationary series. on income as in (1) would be meaningful. In Integrated Stochastic Process: RWM is but this case we say that the two variables are co- a specific case of a more general class of integrated. models called as Integrated Model. It was A number of methods for testing co- claimed in the last part that the RWM integration have been proposed in the without drift is non-stationary. But its first literature. Two simple methods are the DF or difference (Yt-Yt-1) = ∆Yt=ut is stationary. ADF unit root test on the residuals estimated Hence, we call RWM without drift as „an from the co,-integrating regression and the Integrated Process of Order 1” Similarly, if Co-integrating regression Durbin-Watson the difference of the first difference is test. Engle and Granger have calculated the stationary process is called as Integrated critical significance values. Therefore, the process or order 2. In general if pth order DF and ADF tests in the present context are differences of a series results in a stationary known as Engle-Granger (EG) and series, the process is called as „Integrated of Augmented Engle-Granger (AEG) tests order p‟ denoted as I(ρ). The Unit Root Test : A test of stationarity Co-integration: The regression of a non- that is popular is the Unit Root Test. For this stationary time series on another non- purpose Dickey and Fuller have developed a stationary time series may produce a spurious test for δ =0. The associated statistics is regression. Let us suppose that we consider called as Tau-statistics using simulation

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 exercises and prepared extensive tables where ut is a random variable with mean 0 providing critical values. This is called as DF and variance σ2, called an error term (with test for stationarity. This test assumes that the normal distribution for the error term, it is error term ut is uncorrelated. But in the case called as white noise). Extending this idea, where ut are correlated, Dickey-Fuller that the error at time „t‟ is a weighted average developed another test called Augmented of previous and current errors, then the Dickey Fuller test (ADF test). This is done model can be written as with a modification of the considered model Yt= µ+β0ut+β1ut-1, called the first order by adding the lagged values of the dependent Moving Average process. Extending this variable. Still δ=0 is tested and the same ideas, to „q‟ previous time points we get a tables for DF test can be used here also. model called as qth order Moving Average Approaches to Time Series Forecasting Process MA(q). There are different approaches to forecasting Auto Regressive Moving Average (ARMA) with time series such as Exponential Process: If there is a reason to believe that a Smoothing, Single Equation Regression process Yt has the characteristics of both AR Models, Simultaneous Equations Models, and MA process, then another model can be ARIMA Models and VAR Models. The obtained with ARMA models, given by ARIMA Models coming under Box-Jenkins Yt=θ+αtYt-1+β0ut+β1ut-1 Methodology include Auto-Regressive(AR), Notice the presence of lag of order one only Moving Average (MA) and Auto Regressive for AR and MA components. This is called Integrated Moving Average (ARIMA) ARMA(1,1) Process. In general, one can Models. Here a brief view of these define ARMA (p,q) processes, where the AR techniques is given. process is of order „p‟ while that of MA of Auto Regressive (AR) Process: Let Yt be order „q‟. the variable at time „t‟ in the process. Then in Auto Regressive Integrated Moving some situation, this may linearly depend Average (ARIMA) Process: It is already upon its value in the preceding time point or stated that the analysis is comfortable when points. This can be written as Yt=α1Yt-1+ut, the series is a stationary series. If the given which is called as a first order Auto series is stationary, it is called I(0), Regressive Process (AR(1)). The current „Integrated of order 0‟. Generally, the given year GDP may depend upon last year GDP. series is difference for required number of The model may be written in a modified way successive repetitions to get into stationarity. as (Yt-δ)=α1(Yt-1-δ)+ut, where δ is the mean If a given series is differenced for „d‟ times of pth order Auto Regressive Process before applying ARMA (p,q) model, then the (AR(p)). resulting model is called as ARIMA(p,d,q) Moving Average (MA) Process: In the same Model. way, consider that realization of a variable at In order to estimate the model and use the a time point „t‟ is given by a disturbance of a results for forecasting, Box-Jenkins method value, say average of a stationary aprocess, is used. While considering ARIMA (p,d,q0 the model can be specified as Yt=µ+ut, model, knowledge about p,d,q are not

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 known. Hence, the most important issue is comfortable computation here. It is to be that of deciding these constants. Box-Jenkins noted that a significant Durbin Watson methodology consist of four steps for Statistic should be further followed to check analyzing a series and uses the results for for ARCH effect also. forecasting with ARIMA(p,d,q) model. They With this addition to regression models, are identification, Estimation, Diagnostic other improvements started to come in the Checking and Forecasting. This is a high end area that has resulted in a more general model activity which needs expert assistance. format for ARCH models called as Generalised Autoregressive Conditional ARCH and GARCH Models Heteroscedasticity (GARCH) model. We Auto Regressive Conditional consider simple GARCH(1,1) model; in fact Heteroscedasticity (ARCH) and hese are certain principles which can be Generalised Auto Regressive Conditional adopted for any model. GARCH (1,1) can be Heteroscedasticity (GARCH) models are written as 2 2 2 models useful in the study of time series σt =α0+α1ut-1 +α2σt-1 exhibiting Volatality Clustering. By volatility This implies that the conditional variance of clustering it meant that the behavior of the „u‟ at time‟t‟ in the model, where „u‟ is the series showing wide swings for a time error term, depends upon not only on the interval followed by periods in which there is squred error term in the previous time‟t-1‟ relative calm. This implies that the variance (as in ARCH) but also on its conditional of the series varies over time. This variance of the previous time period. This heteroscedasticity or unequal variance may model can be genaralised into GARCH(p,q) have an autoregressive structure, ie variance where p lagged terms of the squared error observed over different points of time may and q lagged terms of the conditional 2 be auto correlated. Generally Xt is taken as a variances. Again, estimation of such models measure of volatility. Accepting this, one are by GLS method with computation of way of verifying such volatility clustering required matrices from the data using will be to model the behavior using AR(1) software. 2 2 model, Xt = β0+β1X t-1+ut in the simplest case, where ut is the usual error term. If β1 is 0, there is no volatility; hence a test is performed for the hypothesis β1=0 with the usual test. Depending upon need higher order models can be used. If the hypothesis is rejected, the presence of volatility clustering is accepted then the series presents an ARCH situation. What should be done in that case? In such situations, the regression is obtained by the Generalised Least Square Method. There are many software providing

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

INTRODUCTION TO GRETL

Dr. K. PRADEEP KUMAR Associate Professor Government College Attingal

Introduction menu. All other menu options enables the user GRETL (Gnu,Regression, Econometrics and to customize and use the database to obtain Time Series Library) is an an open source, descriptive as well as inferential results sophisticated, cross platform, flexible, user contained in the dataset. The most frequently friendly, accurate and extensible econometric used menu options for a beginner are file, software package. The Gretl code base tools, variable and model options. originally derived from program ESL Data files (Econometrics Software Library) written by Gretl has its own native file formats. The basic Professor Ramu Ramanathan of the University data format is one in which we use the suffix of California, San Diago. Gretl as an open .gdt a dataset that is stored in Extended source software have been developed through Markup Language(XML). The system data numerous developers of free and open source files have the suffix .dtd which is installed in software. Richard Stalman of Free Software the system data directory. In addition to this Foundation adopt it as a GNU program after one can import data files in spreadsheets, its finalization. As a cross platform software, SPSS, STATA files,Eviews workfiles, SAS Gretl program is compatiable to operating export files, plain text files etc. Likely, huge systems like Linux, MS Windows, and Mac databases that are available online can also be OSX. The installation of Gretl in MS windows accessed with a database handling routine. is just a matter of downloading These online databases can be accessed gretl_instal.exe and running the program. through menu item File-Databases. The most The Main Window Menus common online database is RATS-4 Reading from left to right of main window (Regression Analysis of Time Series). One can menu bar, we can see file, Tools, Data, View, also create a dataset from scratch by opening Add, Sample, Variable, Model and Help file-Newdata Set (Cntl +N). The subsequent options. Thus one can simply start working on windows will prompt you the steps in creating Gretl by opening data or database from the file your own dataset. While doing so the

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 programme will ask you to specify the nature Using the menu option “Data- Datastructure”, of your dataset. On the basis of nature, the data the user can change the nature of the dataset or set may be database. 1. Cross sectional Data Practice 1: Building a Regression model in Gretl 2. Time Series Data (using sample data files of Ramanathan) 3. Panel Data Procedure Cross sectional Data is a collection of 1. Open Gretl (Start- All Programmes- observations (behavior) of multiple subjects Gretl or Click Shortcut icon created in (entities) at a single point of time. For example Desktop): Gretl Opens maximum temperature, humidity and wind 2. Go to File- Open Data- Sample files – (behaviours) in Thriruvananthapuram, select the tab named Ramanathan : The whole st and Wayanad (entities) on 1 Ramanathan data files will be listed. January,2020. Time Series Data is a collection of observartions (behaviours) for a single subject (entity) at equally spaced different time intervals. For example maximum temperature, humidity and wind (behaviours) in Wayanad town (Single entity) on the first day of every year starting from 2010 to 2020. 3. Select the dataset named disposable income Panel Data or Longitudinal data which is also and consumption ( A double click enables called as cross sectional time series data is a opening of the dataset): The data set opens collection of observations (behaviours) for multiple subjects (entities) at multiple instances (Time). For example the maximum temperature, humidity and wind (behaviours) in Thriruvananthapuram city, Ernakulam town and Wayanad town (multiple entities) on the first day of every year starting from 2010 to 2020 (multiple time period)

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

This file contains the classic econometric consumption function. The data window above displays the current data file, the variable ID, the variable name, a brief description tag and the range of data. 4. To build a simple regression model, select model from menu bar- from the options in model menu click OLS (Ordinary Least

Squares) : a specify model window appears as The output window shows the model based on given below. the data given and its various descriptive and inferential statistics. The window also contains menus that allow the user to inspect or graph the residuals and fitted values and to run various diagnostic tests on the model.

5. Select Ct (Consumption) as dependent variable and Yt (Disposable Income) as

Repressor variable (Independent Variable) using the arrow marks in the respective boxes. Then click OK. The window displaying the regression output will appear as given below.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

PERFORMANCE EVALUATION OF ENTREPRENEURSHIP DEVELOPMENT SCHEMES OF NATIONAL HANDICAPPED AND FINANCE DEVELOPMENT CORPORATION

Dr. SHANIMON S Assistant Professor Government College, Attingal

Abstract

The growth and development of all economies highly depend on entrepreneurial activity. Entrepreneurs are the nerves of economic development as they provide a source of income and employment for themselves. They create an atmosphere of employment generation for others; produce new and innovative product and services. Entrepreneurial supportive environments are essential for entrepreneurship development and are evolving all around the developing economies. An idea of entrepreneurial environment has five metrics, such as easy access to funding, entrepreneurial culture, entrepreneurial supportive regulatory measures, entrepreneurial supportive mechanism and entrepreneur friendly policies. The public and private sector have an equal role to the development of entrepreneurial eco-system. There are four factors necessary for entrepreneurial opportunities such as factor-driven entrepreneurship, efficiency-driven entrepreneurship, innovation-driven entrepreneurship, and necessity-driven entrepreneurship. Entrepreneurship has been considered as the backbone of economic growth. The level of economic activities of a country largely depends on the level of entrepreneurial activities in that country. Entrepreneurs are not born but can be created and nurtured through appropriate interventions in the form of entrepreneurship development programmes. In the modern competitive world a number of opportunities emerged from the evolving Information Technology Revolution. A large part of the population generally lags behind in taking advantage of emerging IT revolution. Therefore, there is a need to provide skill development through entrepreneurship development to such people in order to bring them to mainstream of economic development.

Key Words: Entrepreneurship, Economic Development.

1.1 Introduction of the socially disadvantaged groups especially for women, persons with disabilities, Entrepreneurship development programmes scheduled tribes and scheduled castes. should be designed to upgrade existing skills Entrepreneurship development and training are and to create new skills by organising various the key elements of social and economic technical training courses to the mainstream development among socially disadvantaged society. Specific tailor-made programmes groups. To undertake these tasks on regular should be designed for the skill development basis a number of national level

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 entrepreneurship development institutes and nationalized banks and other specialised autonomous bodies have been started in India. institutions. These institutes are providing assistance in The financial support, the entrepreneurial funding, entrepreneurship development development programmes and various training, research and consultancy services. entrepreneurial skill development programmes National Handicapped Finance and are mainly conduced for the economic and Development Corporation is one among such social development of differently abled agencies that provide a number of assistance persons. Through these programmes and programme for the entrepreneurship differently abled entrepreneurs are able to open development of differently abled persons. and operate their own ventures. NHFDC is 1.2 National Handicapped and Finance specialised institution in the field Development Corporation - Profile entrepreneurship development especially for disabled people with the support of National Handicapped and Finance Government of India. NHDFC has been Development Corporation is the apex level actively engaged in organising institution promoted by Ministry of Social entrepreneurship development training Justice & Empowerment, Government of programmes that are beneficial to the India. The corporation was incorporated as a differently abled persons in India. Company on 1997 under Section 25 of the Indian Companies Act, 1956 to provide 1.3 Objectives of NHFDC are as follows: financial support to handicapped people for 1. To help and support differently abled entrepreneurship development. It provides a persons in carrying out training and number of programmes for the entrepreneurship development entrepreneurship development among programmes. Differently-Aabled people. The Corporation is 2. Promoting economic growth and self- mainly engaged in financial assistance to employment ventures for the benefit of differently abled person with minimum 40% of differently abled persons. disability under micro finance schemes. 3. Providing financial assistance for NHFDC undertakes entrepreneurship persons with disabilities for the development programmes in connection with development of their entrepreneurial skill.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

4. Providing loan to persons with nodal agencies such as State Channelising disabilities for professional or technical Agencies (SCAs). education leading to vocational 1.4.1 Credit Based Schemes rehabilitation and self-employment. The credit based schemes include 5. Assisting self-employed persons with financial assistance to the person with disability in marketing their products disabilities fulfilling the eligibility criteria, in and for efficient management of self- the form of concessional loans on convenient employment ventures. terms for setting up of income generating 6. To serve as the apex national level activity. body for accelerating the process of 1.4.2 Non Credit Based Schemes entrepreneurship development Non-credit based schemes are mainly programmes among differently abled intended to increasing the entrepreneurial people in India. talents of differently abled people. These 7. To provide financial assistance to schemes are: differently abled persons to start 1. Grant for conducting or sponsoring the business venture. entrepreneurial training under the 8. To share experience and expertise in scheme of “„Financial Assistance for entrepreneurial development across the Skill and Entrepreneurial national frontiers through nodal Development”. agencies. 2. Schemes for sponsoring the 9. To provide scholarship assistance to entrepreneurs to conduct various handicapped students. Exhibitions and Trade Fairs. 3. Conducting workshops, seminars and 1.4 Schemes offered by NHFDC conferences. National Handicapped Finance and The institution extends its activities through Development Corporation has framed various various programmes to uplift the socio- schemes to assist differently abled persons. economic conditions of differently abled The schemes mainly involving credit based as persons in India. The corporation is offering well as non-credit based activities for the two types of assistance to differently abled benefit of differently abled persons. These persons under credit based and non-credit schemes are mainly implemented through based schemes. Under credit based schemes,

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

2012- financial assistance is mainly intended for 16 6921.5 13253 6958.99 13296 2013 2013- trading and service sector activities, 17 8018.51 13371 7581.94 13307 2014

agricultural and allied activities, small Source: NHDFC annual report 2014. business activities, loan to the purchase of Figure 1.1: Year Wise Achievements of vehicle for commercial hiring, educational Amount Disbursed loan, and micro credit schemes. (Credit Based Scheme) as on 31.03.2014

1.5 PERFORMANCE EVALUATION OF Amount Disbursed (in Lakhs) VARIOUS SCHEMES OFFERED BY 8000 6000 NHFDC 4000 2000 0 Year Wise Achievements Table 1.1: Details of Amount sanctioned, Amount Disbursed And Number of Beneficiaries ( Credit Based Scheme) as on 31.03.2014 Source: Annual Report NHFDC 2013-2014.

Amount Amount Total Total SL Sanctioned Disbursed Years Number of Number of NO (Rs. in (Rs. in Beneficiaries Beneficiaries Lakh) Lakh) Table (1.1) shows that the details of amount 1997- 1 25.55 11 25.55 11 1998 of loan sanctioned, amount of loan distributed 1998- 2 312.6 811 93.13 230 1999 and the total number of beneficiaries under 1999- 3 458.82 801 576.02 1164 2000 credit based schemes for a period of seventeen 2000- 4 1334.23 3330 1180.88 2645 2001 years from the period of incorporation ( 1997- 2001- 5 1522.6 4075 1283.92 2933 2002 2014). The Corporation aims at extending the 2002- 6 1756.12 4702 1841.31 4498 2003 number of beneficiaries under these schemes 2003- 7 2772.93 5635 2682.04 5565 2004 to achieve its main objectives. The number of 2004- 8 2394.06 4754 1768.55 3282 2005 beneficiaries is increased from 11 to 13307 for 2005- 9 1945.18 3951 2344.17 4765 2006 a time span of seventeen years. The amount of 2006- 10 2728.17 5034 2608.77 4831 2007 loan disbursed is increased from rupees 25.55 2007- 11 3381.62 5416 2830.37 5498 2008 lakh to 7581.94 for a period of seventeen years 2008- 12 4121.82 8159 3028.4 5950 2009 from the inception stage of this institution. 2009- 13 3801.67 6443 3079.59 6032 2010 These results show that the corporation has 2010- 14 3225.66 6007 3183.8 6356 2011 been in a path of development to achieve its 2011- 15 5537.98 10704 5085.78 10625 2012 objectives and actively engaged in organising

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 entrepreneurship development programmes schemes. The programmes are mainly intended which have been beneficial to differently abled for promoting economic development among persons in India. differently abled persons. Figure (1.2) shows that the regression Figure 1 .2: Loan Disbursed coefficient of 177.4 with R2 value of 0.697, (Credit Based Scheme) as on 31.03.2014 which shows that the regression coefficient is 12000 higher in terms of amount disbursed. The y = 177.4e0.243x 10000 R² = 0.697 growth rate is very high in term of amount of loan disbursed. The institution specialised in 8000 the field entrepreneurship with the support of 6000 Government of India and other Non-

4000 Governmental Agencies. The institution extends its activities through various credit 2000 based schemes and non-credit based schemes. 0 0 5 10 15 20 Figure 1.3: Total Number of Beneficiaries Source: NHDFC annual report 2014. (Credit Based Scheme) as on 31.03.2014

National Handicapped Finance and Number of Beneficieries Development Corporation is the apex 20000 10000 institution in entrepreneurship development 0 among differently abled people. The corporation has specialised in credit based and Source: Annual Report NHFDC 2013-2014 non-credit based assistance to differently abled The Corporation aims at extending financial people. The main aim of the institution is to provide financial support to handicapped and entrepreneurial assistance to beneficiaries under credit based and non-credit based people for entrepreneurship development. The schemes for the achievement of its objectives. institution provides number of programmes for During the period of 2013-2014, the the entrepreneurship development among Differently-Aabled people. The financial corporation attained a highest target, the number of beneficiaries during the period was support and entrepreneurial development 13307 (Table 1.1). The year wise analysis programmes are offered through various

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 shows that the in last seven year (2007-2014) have received the amount of loan to start their the number of beneficiaries all over India was business ventures. The above data shows that more than 5000. In the initial period the the number of beneficiaries who have received number of beneficiaries was negligible, during financial assistance from NFDC from the the inception stage of this institution, the period of 1997 to 2014 was increased from 11 number of beneficiaries was very stumpy. The to 13307 with a time span of seventeen years. corporation attained its objectives through a In the initial period the number of beneficiaries long period in terms of coverage of was negligible. During the period of 2013-14 beneficiaries under various schemes of the the number of beneficiaries was 13307. Corporation. During the long lasting years the STATEWISE ACHIEVEMENTS corporation has attained a remarkable growth Table 5.2: Projects Sanctioned & Disbursement made up to 31.03.2014 in the area of entrepreneurship development Amount Amount S among differently abled persons both in the Sanctioned Number of Disbursed Number of L State (Rs. in Beneficiaries (Rs. in Beneficiaries NO number of beneficiaries and amount of Lakh) Lakh) Andhra 3218 1 1429.06 4636 962.81 financial assistance (Table 1.1). Pradesh 2 Assam 176.28 323 169.78 302 Figure 1.4: Year Wise Data of Total 3 Bihar 10 81 5.5 29 Number of Beneficiary 4 Chandigarh 88.38 358 88.38 358 (Credit Based Schemes) as on 31.03.2014 5 Chattisgarh 2849.34 2267 2548.77 2210 6 Delhi 250.65 881 225.60 867 20000 7 Goa 54.03 41 54.03 41 8 Gujarat 2154.01 5490 2110.88 5307 15000y = 56.30x2.01 9 Haryana 5251.46 10079 5083.62 9815 Himachal 2414 R² = 0.855 10 2196.41 2415 2191.46 Pradesh 10000 Jammu & 1116 11 967.69 1123 960.88 Kashmir 12 Jharkhand 193.06 142 193.06 142 5000 13 Karnataka 1068.05 3491 1051.81 3360 14 Kerala 2323.41 3256 2257.21 3038 Lakshadwee 122 0 15 94.94 122 94.56 p 0 5 10 15 20 Madhya 3577 16 2702.31 4178 2085.89 Pradesh 17 Manipur 5.49 41 4.49 31 Figure (1.4) shows that the regression 18 Maharashtra 10047.55 11321 8036.18 10281 coefficient 56.30 with an R2 value of 0.855, 19 Meghalaya 307.50 530 307.50 530 20 Mizoram 50 178 50 178 which shows that the regression coefficient is 21 Nagaland 243.62 501 243.62 501 22 Orissa 1359.66 3081 1239.55 2621 higher in terms of total number of 23 Puducherry 1840.29 3259 1808.23 3209 24 Punjab 852.59 1283 829.85 1257 beneficiaries. The growth rate is very high in 25 Rajasthan 2816.14 4473 2783.89 4445 26 Sikkim 51.3 97 51.3 97 term of total number of beneficiaries, who 27 Tamil Nadu 6068.99 22967 6013.84 22593

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

28 Tripura 248.31 213 247.36 212 offtake from the corporation and loan Uttar 5404 29 2752.72 5545 2711.45 Pradesh distributed among the beneficiaries (Figure 30 Uttarakhand 1084.62 2082 1072.85 2079 West 1634 1.5). 31 721.16 2003 668.86 Bengal

Source: NHDFC annual report 2014. Table 1 .3: EDP Grant Sanctioned & Disbursement. For the Year (2013-2014) Table (1.2) shows that the state wise details of projects sanctioned and the amount of disbursement made up to 2014. The status of the Corporation has continually improved on project sanctioning and the disbursement of loans to beneficiaries over the past years. Figure 1.5: State wise list of Fund Sanctioned to Differently Abled People

State Wise List of Fund Saanctioned to Beneficiaries

Rajasthan Manipur Haryana Andhra Pradesh 0 5000 1000015000

Amount Disbursed Amount Sanctioned

Axis Title Axis Source: Annual Report of NHFDC (2013- 2014). Source: Annual Report NHFDC 2013-2014. Table (1.3) shows that the amount of Annual disbursement of loans for the benefit grant sanctioned the amount of grant disbursed of persons with disabilities in the past years by the corporation on 2013-2014. The grant shows that the corporation was totally focused was sanctioned for entrepreneurship on entrepreneurship development among development training and skill development to differently abled persons. The top three states 21 States in India. The state of West Bengal in terms of loan offtake from the Corporation utilized 50 per cent of the grant and offers during total period were Maharashtra, Tamil training facilities to entrepreneurship Nadu and Haryana. Bihar, Mizoram and Goa development. Two states namely state of were the least performed states in terms of loan Kerala and Tamil Nadu did not provide

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 training facilities to anyone. These two state participating in trade fairs and exhibitions at have been sanctioned Rs: 735508.56 lakh for local, state, national and international levels each for EDP training through various market assistance schemes. The Corporation is mainly engaged in Figure 1.6: EDP Grant Sanctioned & Disbursement. For the Year (2013-2014) financial assistance to differently abled person with minimum 40% of disability under micro 12000000 finance schemes. NHFDC undertakes 10000000 y = 12717x Amount… 8000000 R² = -0.35 entrepreneurship development programmes in 6000000 connection with nationalized banks and other 4000000 specialised institutions. The financial support, 2000000 0 the entrepreneurial development programmes and various entrepreneurial skill development programmes are mainly conducted for the economic and social development of differently abled persons. Source: Annual Report, NHFDC 2014.

Conclusion Findings 1. National Handicapped Finance and National Handicapped and Finance Development Corporation is conscious about Development Corporation is focusing on quality enhancement through entrepreneurship quality skill development on entrepreneurship development among differently abled persons. development for the well being of differently 2. The Corporation is focusing on quality abled persons. It provides special emphasis to skill development and providing special attract person with disabilities to skill and emphasis to attract person with disabilities to entrepreneurship development programmes. A skill and entrepreneurship development number of training facilities are offered to the programmes. An entrepreneur may not be able target group. Till date, the Corporation has to succeed without entrepreneurial skills and organized a number of entrepreneurship qualities, in the modern competitive market development programmes and skill environment. development trainings in all states covering a 3. The intended training facilities are large number of differently abled persons. The offering to the target group. Till date, the Corporation assists the beneficiaries in

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Corporation has organized a number of Reference entrepreneurship development programmes 1. Noel J. Lindsay, Wendy A. & Fredric and skill development trainings in all states Kropp, (2009). Start-up intentions and behavior of necessity-Based entrepreneurs A covering a large number of differently abled longitudinal study, Frontier of persons. Entrepreneurship Research, 1-5. 2. North, (1990). A Transaction Cost 4. During the year last twenty years Theory of Politics. SAGE Journals, Vol. 2, entrepreneurship development programmes Issue 4. 3. O‟ Brein et al (1997), Poverty and and skill development trainings were Social Exclusion in North and South, IDS organized in all states covering thousands of Working Paper 55. Institute of Development Studies and Poverty Research Unit, University persons with disabilities. of Sussex, Brighton. 5. The Corporation assists the 4. Peter F. Drucker (1970), Practice of Management, Allied Publishers, New Delhi. beneficiaries in participating in trade fairs and 5. Rakesh Gupta, exhibitions at local, state, national and & Ajay Pandit (2013). Innovation and growth of small and medium enterprises: role of international levels through various market environmental dynamism and firm resources assistance schemes. as moderating variables. International Journal of Entrepreneurship and Innovation 6. The Corporation provides the space for Management, 17, (4/5/6), 284 – 295. reimbursement cost of travelling and 6. Russell, W. Teasley., Richard, B. & Robinson, (2005). Modeling knowledge-based accommodation expenses and providing entrepreneurship and innovation in Japanese carriage cost of goods and daily allowances for organizations. International Journal of Entrepreneurship, 9, 19-144. the beneficiary and escort for participation in 7. Saadat Saeed, Moreno Muffatto, these fairs. Shumaila Y. Yousafzai, (2014). Exploring intergenerational influence on entrepreneurial 7. The beneficiaries are getting the intention: the mediating role of perceived chance of market opportunities through their desirability and perceived feasibility of Internationaisationl Journal of participation in these events. Such Entrepreneurship and innovation management, participation also showcases the abilities of the 18, 2/3, 134 – 153. 8. Sahalman (1987). Value creation in differently abled for the awareness of general place management: The relevance of Service public. Providers. International Journal Of Management Science and Business research,3(11), 13-19.,

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

TRENDS IN GLOBAL AQUACULTURE PRODUCTION

Dr. ANITHA S. Associate Professor Government College, Attingal

Abstract Aquaculture is the farming and husbandry of aquatic creatures under regulated or semi-regulated environmental conditions. These organisms may be fishes, crustaceans, molluscs, aquatic plants and animals. Global Production from capture fisheries showed a declining trend in the past few decades. Despite the mechanism of fishing techniques, catch per unit effort declined and unit cost of production increased. The basic reason for the declining rate of growth in fish production is attributed to over exploitation of scarce fishery resources. This necessitated a shift on emphasis from development of capture fisheries to development of culture fisheries. Scientific aquaculture- a bio technology to boost fish production through fish culture has become popular in major fish producing countries of the world. This research paper analyses the gloabal trends in aquaculture production based on the valid database of Fisheries Global Information Systems (FIGIS).

Key Words : Aquaculture, Capture Fisheries, Global trends

1.1 Introduction security and livelihoods, the Thirty-first

Global Production from capture fisheries Session of the FAO Committee on Fisheries showed a declining trend in the past few (COFI) endorsed the convening of the Global decades. Despite the mechanism of fishing Conference on Inland Fisheries: Freshwater, techniques, catch per unit effort declined and Fish and the Future (26–28 January2015). The unit cost of production increased. The basic conference was part of a memorandum of reason for the declining rate of growth in fish understanding between FAO and Michigan production is attributed to over exploitation of State University, and brought together about scarce fishery resources. This necessitated a 200 scientists, resource managers and shift on emphasis from development of capture representatives from civil society from around fisheries to development of culture fisheries. the globe. The Session accepted the following Scientific aquaculture- a bio technology to Ten steps to Responsible inland aquaculture.1. boost fish production through fish culture has Improve the assessment of biological become popular in major fish producing production to enable science-based countries of the world. In recognition of the management 2. Correctly valued inland vital role inland fisheries play in global food aquatic ecosystems 3. Promote the nutritional

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 value of inland fisheries 4. Develop and are the leading importers of fish and fisheries improve science-based approaches to fishery products. management 5. Improve communication 1.3. Trends in Gloabal Aquaculture among freshwater users 6. Improve Production governance, especially for shared water bodies 7. Develop collaborative approaches to cross- The table below shows the figures in MT sectoral integration in development agendas 8. (Million Tons) of gloabal aquaculture Respect equity and rights of stakeholders 9. production from 1997 to 2016 Make aquaculture an important ally and 10. Aquaculture Production Develop an action plan for global inland Year (World) in MTs 1997 30.23 fisheries 1998 30.25 1999 31.59 1.2. Aquaculture – Global Scenario 2000 32.41 2001 33.24 The figures from FAO FIGIS records show 2002 36.78 that 47 percent of the worlds‟ total fish 2003 38.9 2004 41.9 production (80.4 /169 Million Tons) is through 2005 44.2 aquaculture. The percent of Aquaculture to 2006 47.25 2007 49.9 Total fish production shows an increasing 2008 52.9 trend from 35 percent in 2007 to 47 percent in 2009 55.7 2010 59 2016. The figures of marine fishing shows 2011 61.8 decreasing trend from 65 percent to 53 2012 66.5 2013 70.3 percent.89.3 percent of the world aquaculture 2014 73.7 production ( 68.3/76.6 Million Tons) is from 2015 76.6 2016 80.4 the continent Asia. The average growth rate is Source : FIGIS (FAO) 4.4%. The second leading producer is America Table shows global inland fish production ( 3.2/80.4 MT). Finfish is the major item in the from the year 1997-98 to 2015-16. During aquaculture product group (52 /76.6 MT). 52.5 these 20 years, the total inland fish production % of the world export of fish and fisheries increased to 80.4 million tons from 30.23 products is from Ten countries including India. million tons. In every year, there is an increase The share of China, the leading exporter is in the production quantity is seen. 14% and that of India is 3.7%. USA and Japan

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Figure 3.2. Global Aquaculture Production Trend global aquaculture production for the 20 years from 1997 to 2016 under review. The regression model reveal that the time series trend is linear and increasing annually at the rate of 2.7781 Million tons. Even though the model fitness indicator R2 shows higher value, the validity of the model need to be tested for all assumptions in Ordinary Least Squares. Thus a pure econometric time series analysis may improve the model through various tests of validity.

Reference

M.Krishnan and P.S.Birthal (Eds.), (2010). Aquacultural Development of India: Problems and Prospects, New The world aquaculture production increased Delhi, National Centre for Agricultural from 30.23 million tons to 80.4 million tons Economics and Policy Research. over a period of 20 years from 1997 to 2016. Meade, James, W. (1998). Aquaculture Management, New Delhi, CBS Publishers The data over this period seem to show a & Distributors. linear growth in global aquaculture Meehan, W.E. (2002). Fish Culture in production. Using the function y = a+bx, the Ponds and Other Inland Waters, Pilani, equation estimated using regression tool, H.R.Publishing House. results in b = 2.7781, a = 0.5523, which is Menon, K.M. (1998). Matsyakrishi 2 (), Thiruvanthapuram, Kerala valid with a significant R value = 0.978.Thus Bhasha Institute. in every year the a marginal increase in Michael, B.N., Valenti,W.C., Tidewell, J. production is 2.7781 Million Tons. The H., D‟Abramo, L.R. &Kutty, M. N. validity of this model need to be tested for various assumptions in the time series modeling.

1.4. Conclusion

The simple linear regression model explained in this paper attempts to derive the trend in

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

PERFORMANCE EVALUATION OF SBI LIFE INSURANCE COMPANY

ANSA S.

Research Scholar, Reserach & P.G.Department of Commerce, Government College, Attingal, University of Kerala,[email protected],9446108234

Abstract

Insurance is a protection against financial loss arising on the happening of an uncertain event and also serves as a tool for capital formation. Bancassurance means selling the insurance products through the banking network. While in the initial stage, the SBI Life Insurance Company act as a bancassurance channel, now it is developing its own agency for selling insurance products. With a wide network of 908 offices span across the country SBI Life Insurance Company has a mission to emerge as the leading insurer by offering variety of life insurance products, pension schemes, ensuring high standards of customer service and better operational efficiency. The company shows a tremendous growth during the last two decades with an upward trend in the net profit after tax and net worth.

Keywords: life insurance, SBI Life, premium

Introduction large number of life insurance companies functioning in India. From the last two Life insurance policies are a safeguard against decades, commercial banks were entered into the uncertainties of life. In life insurance, the the insurance sector as a distribution channel insured transfers a risk to the insurer by paying for insurance products. There are so many tie- an amount called premium in exchange. ups and joint ventures between banks and Insurance is a protection against financial loss insurance companies were started for arising on the happening of an uncertain event marketing the insurance products. and serves as a tool for savings and investment. In India, the life insurance has its SBI Life Insurance Company was origin from the oriental life insurance incorporated on 11th October 2000 as a joint company established at Kolkata. Now a day, venture between SBI and BNP Paribas Cardif. life insurance industry in India has a 62.1% of total capital is owned by SBI and predominant place in the economy. There are a 22% owned by BNP Paribas Cardif. The other

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 investors are Value Line Pvt..Ltd. and Mc an insurance plan. The company aims to gain Rittchie Investment Pvt.Ltd. holding 1.95% its competitive advantage through customer and remaining 12% with public. The company centric approach. has an authorized capital of Rs.20 billion and a Objective of the study paid up capital of Rs.10 billion. It is one of the The study has the following objective. leading private life insurance companies that 1. To analyze the performance of SBI offer a wide range of insurance products Life Insurance Company. through its strong distribution channel. The Methodology majority shares of SBI Life Insurance The study describes the growth and Company are owned by SBI. SBI Life has a performance of SBI Life Insurance Company unique multi-channel distribution network using secondary data. The required data were comprising an expansive bancassurance collected from the annual report of SBI Life insurance Company and other journals. For channel with SBI, its largest bancassurance analyzing the performance of SBI Life, data partner in India with their individual agents were collected for a period of 12 financial networks comprising 108261 agents as on 31st years from 2008 to 2019.

March 2018 as well as other distribution PERFORMANCE EVALUATION OF SBI channels including brokers, corporate agents, LIFE INSURANCE COMPANY TABLE 1: GROSS WRITTEN PREMIUM (Rs.in billion) direct sales or other intermediaries. While in First the initial stage, the SBI Life Insurance year premiu Single Renewal Company act as a bancassurance channel, now Year m premium premium it is developing its own agency for selling 2008 33.35 14.57 8.29 2009 45.65 8.22 18.25 insurance products. SBI Life Insurance 2010 62.82 7.59 30.63 Company has a wide network of 908 offices 2011 33.9 42 53.56 spread across the country with 14,961 2012 21.93 43.39 66.02 2013 26.18 25.65 52.67 employees. It has also tie-ups with 76 2014 29.98 20.68 56.73 corporate agents, 17 bancassurance partners 2015 33.31 21.98 73.38 and 99 brokers along with 184452 trained 2016 46.31 24.76 87.19 2017 62.07 39.37 108.71 insurance personnel, catering wide range of its 2018 81.39 28.27 143.38 customers. SBI Life offers innovative and 2019 90.57 47.35 191.97 Source: Annual reports of SBI Life Insurance Company newer technologies to provide more from2008-2019 convenient options to customers for selecting

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

figure 1: Chart showing trends in Gross Written Premium

250

200 y = 13.59x - 27295 R² = 0.862 150

100 y = 3.656x - 7315.

R² = 0.350 Rs. In billion In Rs. 50 y = 2.126x - 4254. R² = 0.320 0 2006 2008 2010 2012 2014 2016 2018 2020

-50 Year First year premium Single premium Renewal premium

Figure one portraits Gross Written premium annual increase of Rs.13.59 billion with a good during the period 2008–2019. The first year explanation of 86.2%. Thus all Gross written premium increased from Rs.33.35 billion to premium shows linear trends with sufficient Rs.90.57 billion in 2019. The trend line shows explanation to the model. an annual linear increase of Rs.3.656 billion TABLE 2:PROFITS AND NET WORTH with an explanation of 35% (R2 =0.35). Likely, (Rs.in billion) the Single premium for 2008 raised from Rs.14.57 billiontoRs.47.35 billion in 2019 Year Profit after tax Networth which shows an upward trend line with an 2008 0.34 10.07 2009 -0.26 9.78 annual linear increase of Rs.2.126 billion with 2010 2.76 12.65 an explanation of 32% (R2=0.32).Similarly, 2011 3.66 16.3 the renewal plan also increased from Rs.8.29 2012 5.56 21.56 2013 6.22 27.1 billion in2008toRs.191.97 billion in 2019. The 2014 7.4 33.42 trend line clearly shows a linear trend with an 2015 8.2 40.39

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

2016 8.61 47.33 last 12 years. The company has only a profit of 2017 9.55 55.52 0.34 percent during 2008 and it increased to 2018 11.5 65.28 2019 13.27 75.76 13.27 percent during 2019. Its trend line shows Source: Annual reports of SBI Life Insurance Company upward trend with an annual increase of from 2008-2019 1.1519 with an explanation of 97%

Figure 2: Chart showing trends in Profit after Tax and Networth 80

70 y = 6.084x - 12216 60 R² = 0.957 50

40

30 Rs. In billion In Rs. y = 1.151x - 2313 20 R² = 0.973 10

0 2006 2008 2010 2012 2014 2016 2018 2020 -10 Year

Profit after tax Networth Figure two portraits net profit after tax and net (R2=0.9739). The net worth also increased worth of SBI Life insurance Company for the from 10.07 percent on 2008 to 75.76 percent on 2019 which shows an upward trend line with an annual linear increase of 6.0844 with in India. It offers variety of life insurance an explanation of 95.7% ( R2=0.9572). Thus, products through its multi distribution channel. the profit and net worth shows a high rate The company shows a tremendous growth growth for the last 12 years. during the last two decades. The gross written Conclusion premium of company shows an increasing SBI Life Insurance Company is placed as trend. The renewal premium increased at high pioneer to the development of bancassurance rate during the last 12 years.The net profit after

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 tax and net worth of the company is shows an References upward trend. The company gets more profits 1. Agrawal.A (2004).Distribution of life and net worth on 2019 as compared to insurance products in India, Insurance 2008.The company attained its competitive Chronicle, September, p.24. advantage by offering more products and 2. Sinha (2005).Bancassurance in India, services on customer centric. It has a large The Insurance Times, December, p.34. network of branches and individual agents 3. Okeahalam (2008). Success factors for spread all over India for distributing insurance bancassurance, Journal of Banking and products. SBI Life attained its operational Finance, 8,pp.22-28. efficiency by focused more on rural customers 4. www.sbilife.co.in and thereby increased the standard of living 5. Annual reports of sbi life insurance and development of society as a whole. company 6. www.irdai.gov.in

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

ARIMA MODEL IN PREDICTING NSE NIFTY50 INDEX

Dr. LAKSHMANAN M.P Assistant Professor PG Department of Commerce Government College Chittur Email:[email protected]

Abstract

The prediction of stock prices and related indices is of vital importance in the field of economics and business and many research works has been carried out over the years to develop predictive models. The historical data on index closing price was used to develop several ARIMA (Autoregressive Integrated Moving Average) models by using Box-Jenkins time series procedure and the adequate model was selected according to four performance criteria: Akaike Criterion, Schwarz Bayesian Criterion, Maximum Likelihood and Standard Error. The paper presents the process of building stock price predictive model using ARIMA Model. Published stock data obtained from NSE (National Stock Exchange) is used with stock predictive model developed. Therefore, Monthly data from January 2001 up to December 2019( 228 observations) is used for this study. The results obtained revealed that ARIMA model has high potential in short run prediction and will be helpful to investors in stock market.

Key Words: Time series, ARIMA Model, Stock/Index Price Prediction, Short term Prediction.

INTRODUCTION difficult task in financial forecasting due to

Prediction of stock/index prices are always an varied reasons especially its complex nature, interesting area of research because of its high amount of volatility, influence of global peculiar characteristics like volatility distinct market forces etc. Any investor will try to from other financial products in financial depend on a forecasting method that could market. In the information and technology era, guarantee easy profiting and minimize individuals and institutions are highly investment risk from the stock market. This empowered to make investment decisions and stands as major motivating factor for design effective strategies as to their daily and researchers in evolving and developing new future financial requirements. The prediction predictive models. The Nifty 50 is an indicator of stock/index prices is one of the most of the top 50 major companies on the NSE.A

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 large number of methods have been used for models are sed in time series data to predict NSE including AR (Autoregressive model), future points in the series. Such models are ARMA (Autoregressive Moving Average applied in cases where data is non-stationery Model), ARIMA (Autoregressive Integrated wherein differencing can be done to reduce the Moving Average Model) and so on. But non-stationarity. Non-seasonal ARIMA ARIMA is most widely used on among them. models are generally denoted ARIMA (p, d, q) Stock market price may be of opening price, where parameters are non- negative integers lowest price, highest price, adjusted closing then p, d, q refer to the autoregressive, price and volume. The study takes into account differencing, and moving average terms for the closing stock price (in Rs). The analysis of non-seasonal component of the ARIMA stock data has been done using SPSS 20 model. Seasonal ARIMA models are usually Software and Gretl and E Views 8 denoted ARIMA (p, d, q) (P, D, Q)m, where m refers to the number of periods in each season, LITERATURE REVIEW and P,D,Q refer to the autoregressive, The major works using ARIMA model in the study differencing, and moving average terms for the of stock market data are reviewed .Banerjee, D. seasonal component of the ARIMA (2014) applied ARIMA model to forecast in model.Box-Jenkins method./approach has been Indian Stock Exchange the future stock used for analysis and modeling the time series. indices. Paulo Rotela Ju-nior et al. (2014) This methodology comprises the following described ARIMA model to obtain short-term steps. forecasts to minimize prediction errors for the

Bovespa Stock Index. Renhao Jin et al. (2015) (a) Identification of model: -This stage used ARIMA model to predict in Shanghai involves finding whether the time series data is Composite Stock Price Index . All the studies stationary or not and compare the estimated were based on closing stock price. Autocorrelation Function (ACF) and Partial

OBJECTIVE OF THE STUDY Autocorrelation Function (PACF) to find a match. To forecast the closing stock price of NSE NIFTY50 using time series ARIMA Model (b) Estimation of Parameters(coefficients): - DATA & METHODOLOGY Estimating the parameters for Box Jenkins An ARIMA Model is a generalisation of an models is a complicated nonlinear estimation ARMA model in time series analysis. These

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 problem. The main approaches for fitting Box variable or other associated variables. - Jenkins models are nonlinear least squares Forecasting may also be based on expert and maximum likelihood estimation. judgments, which in turn are based on Parameter estimates are usually obtained by chronological data and experience. When maximum likelihood which is fit for time model selected is found satisfactory during the series. Estimators are always sufficient, analysis, it can be used for forecasting efficient, and consistent for Normal purpose. distribution. ARIMA model uses the historic data and (c) Diagnostic checking (verification): -The decomposes it into AR ( Auto Regressive) – diagnostic checking is pre-requisite to ensure indicates weighted moving average over past the appropriateness of the selected model. observations, Integrated (I) –indicates linear Selection of particular model can be done trends or polynomial trend and moving based on the values of certain criteria like log average (MA) –Indicates weighted moving likelihood, Akaike Information Criteria (AIC)/ average over past errors. As such it has three Bayesian Information Criteria (BIC)/ Schwarz- model parameters AR (p), I(d) and MA(q) all Bayesian Information Criteria (SBC). After combined to forming ARIMA (p,d,q) model model selection, its o be verified that whether where p represents order of auto correlation, d estimated model is satisfactory or not by represents order of integration (differencing) studying the pattern among the residuals if and q represents order of moving averages. there any. The values of ACF may be checked RESULTS & DISCUSSION to see that whether the series of residuals is white-noise. After fitting tentative model to The descriptive statistics of the NSE Nifty Fifty data for the analysis period is tabled below. data, diagnostic checks are done and overall Summary statistics, using the observations 2001:01 adequacy of the model selected can be known - 2019:12 for the variable 'Price' (228 valid by examining a quantity Q known as Ljung- observations) Box statistic that follows chi-square Table 1 Descriptive Statistics-Price

Std. distribution. Mini Maxi Deviatio N Range mum mum Mean n Skewness Kurtosis St d. Stati Stati Statis Statisti Stati Std. Stati Er stic Statistic stic tic c Statistic stic Error stic ror 228 11254.6 913.8 12168 5366.96 3210.25 .365 .161 -.869 .32 (d) Forecast. It means prediction of values of a 000 500 .4500 5570 36370 1 variable based on identified past values of that

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Figure (1) depicts the original pattern of the series to have general overview whether the time series is stationary or not and it can be seen that time series is not stationary( i.e. has random walk pattern).

PRICE

14,000

12,000

10,000

8,000

6,000 4,000 2,000

0 Figure (2) The correlogram of NSE 2002 2004 2006 2008 2010 2012 2014 2016 2018 Nifty 50 Stock Price Index Figure (1)Graphical presentation of the NSE Nifty 50 Price Index

X axis represents trading years and Y axis represents stock index price. Figure (2) is the correlogram of NSE Nifty 50 time series. The ACF the graph , it is seen that ACF dies down slowly which simply means that the time series is non- station to stationery. When series is not stationery, Figure (3) The ACF diagram of NSE it is converted to a stationery series by Nifty 50 Stock Price Index differencing. After the first difference, the series Differenced PRICE of NSE Nifty 50 becomes stationery as given infigure3 and figure 4 of the line graph and correlogram respectively.

Figure (4) The PACF diagram of NSE Nifty 50 Stock Price Index

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Differenced PRICE Figure 8and figure9 of modified series of 1,200 correlation coefficients figures of ACF and 800 PACF shows that there is stationarity in the 400 data series and most of the values lie within 0 the confidence interval which is validated by -400 ADF Unit root test result as given in figure 7. -800 The value of Durbin –Watson (DW) was -1,200 2002 2004 2006 2008 2010 2012 2014 2016 2018 0.009075 for the sample data of NSE Nifty 50 Figure 5 Graphical presentation of the NSE NIFTY 50 stock price index after first and same was 2.026736 (for first difference). differencing. The data first differenced is having d value greater than Du (1.78) as such the null hypothesis is not rejected and assumed that there is no auto correlation.

The basic idea of ARIMA model is to view the data sequence as formed by a Stochastic Process on time. When the model has been identified, it model can be used to estimate the

future value based on the past and present Figure 6 The correlogram of NSE Nifty 50 stock value of the time series. Based on the price index after first differencing. identification rules on time series, the corresponding model can be established. If a partial correlation function of a stationary sequence is truncated, and auto-correlation function is tailed, it can be concluded the sequences for AR model; if partial correlation function of a stationary sequence is tailed, and the auto-correlation function is truncated, it

can be strong that the MA model can be fitted Figure 7ADFUnit root of NSE NIFTY 50 stock for the sequence. If the partial correlation price index after first differencing function of a stationary sequence and the

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 autocorrelation function are tailed, then the ARMA model is appropriate for the sequence.

Figure 9 The PACF diagram of first order differencing of NSE Nifty 50 closing stock Figure 8 The ACF of NSE Nifty 50 stock price price index after first differencing.

For the various correlations up to 24 lags are Partial Autocorrelations computed and the same along with their Series: DIFF(Price,1) significance which is tested by Box-Ljung (Q) Lag Partial Autocorrelation Std. Error test are provided in Table 1 and 2. 1 -.453 .067 2 -.421 .067 AutocorrelationsSeries: DIFF(Price,1) 3 -.275 .067 a Lag Autocorrelation Std. Error Box-Ljung Statistic 4 -.174 .067 b Value df Sig. 5 -.159 .067 1 -.453 .066 47.041 1 .000 6 -.180 .067 2 -.129 .066 50.897 2 .000 7 -.064 .067 3 .094 .066 52.960 3 .000 8 -.179 .067 4 .026 .066 53.112 4 .000 5 -.049 .065 53.670 5 .000 9 -.151 .067 6 -.019 .065 53.756 6 .000 10 -.014 .067 7 .085 .065 55.465 7 .000 11 .032 .067 8 -.113 .065 58.467 8 .000 12 -.017 .067 9 .046 .065 58.980 9 .000 13 .007 .067 10 .091 .065 60.965 10 .000 11 -.055 .065 61.688 11 .000 14 -.138 .067 12 -.064 .064 62.678 12 .000 15 -.131 .067 13 .058 .064 63.500 13 .000 16 -.024 .067 14 -.069 .064 64.673 14 .000 Table 2 The PACF value of first order 15 .034 .064 64.950 15 .000 differencing of NSE Nifty 50 closing stock 16 .077 .064 66.387 16 .000 price

Table 1 The ACF value of first order Table 3 shows the different parameters of differencing of NSE Nifty 50 closing stock autoregressive (p) and moving average (q) price among the several ARIMA Model

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 experimented upon, ARIMA (0, 1,1 ) is (1,1,1) (5,1,0) ARIMA(1,1,2) 11.539 ARIMA 11.614 considered the best for NSE Nifty 50 closing (5,1,1) ARIMA 11.564 ARIMA 11.642 stock price. The model gives the smallest BIC (0,1,2) (6,1,0) 11.484. . ARIMA 11.558 ARIMA 11.738 (3,1,1) (6,1,0) The model verification is done by checking the residuals of the model to observe whether they contain any systematic pattern which still can be removed to get better on the chosen ARIMA. This is done through examining the autocorrelations and partial autocorrelations of the residuals of various orders.

Table no 4 Model Statistics of Nifty 50 closing Figure 10Correlogram of Residuals of NSE stock price Nifty 50 closing stock price Model Model Ljung-Box Q(18) Numbe Figure 10 is the residual of the series. If the Fit r of model is good, the residuals (difference statistic Outlier s s between actual and predicted values) of the R- Statistic D Sig model are series of random errors. Since there squared s F . are no significant spikes of ACFs and PACFs, Price- .25 Model_ 0.991 20.303 17 0 it means that the residual of the selected 9 1 ARIMA model are white noise, no other significant patterns left in the time series. Therefore, there is no need to consider any Table no 5 ARIMA Model Parameters AR(p) and MA(q) further. Esti SE t Si mat g.

Table 3 Normalized BIC Values of Nifty 50 e closing stock price 20. 2. .0 Pric Const 47.5 ARIMA Normalized ARIMA Normalized Pr No 09 36 1 (p,d,q) BIC (p,d,q) BIC e- ant 63 ic Transfor 5 7 9 ARIMA(0,1,0) 12.161 ARIMA 11.740 Mod (3,1,0) e mation Diffe ARIMA(1,1,0) 11.959 ARIMA 11.738 el_1 1 (4,1,0) rence ARIMA 11.484 ARIMA 11.586 (0,1,1) (4,1,1) ARIMA 11.516 ARIMA 11.742

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

SPSS Forecasting is used. Table 6 and Figure 12 present the results of the NSE Nifty 50 share price obtained by applying ARIMA Model (0,1,1) for the next 7 months from January 2020to July 2020. Figure 11 Residual ACF and PACF diagram of actual Nifty 50 CONCLUSION The ACF and PACF of the residuals (Figure

11) indicate `good fit' of the model. Forecasting Share price index is of vital

importance and utility to stock market

investors. The investment decision depends on

the future share prices. In this context , an

ARIMA model to NIFTY 50 index is index is developed by using Box-Jenkins Time series Table no 6 Forecast NSE Nifty 50 share price index January 2020 to July 2020 approach. The historical share price data were Forecast used to develop several models and the Mod el 229 230 231 232 233 234 235 adequate one was selected according to For 122 122 123 123 124 124 125 ecas 16.0 63.5 11.1 58.7 06.2 53.8 01.3 t 1 8 4 0 6 3 9 performance criteria SBC,AIC, Standard Error Pric UC 128 131 133 135 137 139 140 e- L 12.6 07.3 44.5 51.9 40.3 15.2 79.8 and Maximum Likelihood. In the process of Mod 2 1 0 2 2 2 7 el_1 LC 116 114 112 111 110 109 109 model building , the original Nifty 50 data is found L 19.4 19.8 77.7 65.4 72.2 92.4 22.9 0 4 8 8 0 4 1 to be Non stationary. But the first order differencing of Original Nifty 50 is stationery. In the study ARIMA (0,1,1) model is developed for analyzing and forecasting Nifty 50 closing stock price among all of various tentative models having lowest BIC values. The study highlights that influence R square is 99% high

Figure 12 NSE Nifty 50 share price and mean absolute percentage error is very index, Fit,LCL,UCL and forecasting small for the fitted model. Thus it can be seen

Forecast : After defining the most appropriate that the prediction accuracy is more in fitting model of share price , forecasting is to be done of Nifty 50. and to predict trends and develop forecast IBM

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Acknowledgment: The author expresses Series ARIMA Models, International heartfelt gratitude to the authors of articles Journal of Engineering Research & cited in references(2,3,9,10) for depending to Technology (IJERT) Vol. 4 Issue 03, great extent on the literature and methodology March-2015. framework in analyzing and forecasting the 9.Mohammed Ashik& S Kannan K (2017), Nifty Fifty share price. “Forecasting National stock price ARIMA REFERENCES Model”,Global and Stochastic analysis,

1.D. Banerjee, “Forecasting of Indian stock Vol 4, No 1, January 2017,77-81 market using time-series ARIMA model,” in Proc. Conference Paper, ICBIM-14, 2014. 10.A A Adebiyi and Charles Ayo (2014), 2.BanhiGuha and Gautam Bandyopadhyay,” Stock Price prediction using the ARIMA Gold Price Forecasting Using ARIMA Model,2014 UKSim-AMSS 16th Model”, Journal of Advanced Management International Conference on Computer Science Vol. 4, No. 2, March 2016, p117- Modelling and Simulation Research Gate 121 3.Jamal Fattah, et al , Forecasting of demand using ARIMA model, International Journal of Engineering Business Management, Volume 10: 1–9 4.Shen S and Shen Y. ARIMA model in the application of Shanghai and Shenzhen stock index. Appl Math 2016; 7:171–176. 5. Hanke JE and Reitsch AG.Business forecasting, 5th ed. Englewood Cliffs. 1995. 6.Brockwell PJ and Davis RA. Time series: theory and method. Berlin: Springer-Verlag,

1987 7.Hamilton JD. Time series analysis.

Princeton: Princeton University Press, 1994.

8. Dr. (Ms.) ShaliniBhawanaMasih, et al , Modeling and Forecasting by using Time

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

FINANCIAL DEEPENING AND ECONOMIC DEVELOPMENT OF INDIA

Dr. PRADEEP KUMAR.N Assistant Professor of Commerce, ,

Abstract

A high level of financial deepening is a necessary condition for accelerating growth in an economy. This is because of the central role of the financial system in mobilizing savings and allocating same for the development process. This study examined financial deepening and economic development in India between 1995 and 2017. The study made use of secondary data, sourced for a period of 22 years. The two stages least squares analytical framework was used in the analysis. The study found that financial deepening index is low in India over the years. It was also found that the nine explanatory variables, as a whole were useful and had a statistical relationship with financial deepening. But four of the variables; lending rates, financial savings ratio, cheques cleared/GDP ratio and the deposit money banks/ GDP ratio had a significant relationship with financial deepening. The study concluded that the financial system has not sustained an effective financial intermediation, especially credit allocation and a high level of monetization of the economy. Thus the regulatory framework should be restructured to ensure good risk management and corporate governance in the system.

Key Words; Financial Sector, Corporate Governance, Financial Reforms, Financial Savings, Financial Market, Gross Domestic Product, Financial Deepening

Introduction According to the Reserve Bank of India the financial system refers to the set of The reforms in the financial system in rules and regulations and the aggregation India which heightened with the 1991 of financial arrangements, institutions, deregulation, affected the level of financial agents, that interact with each other and deepening of the country and the level the rest of the world to foster economic relevance of the financial system to economic growth and development of a nation. The development. However, the rapid financial system serve as a catalyst to globalization of the financial markets since economic development through various then and the increased level of integration of institutional structures. The system the Indian financial system to the global vigorously seek out and attract the reservoir system have generated interest on the level of of savings and idle funds and allocate same financial deepening that has occurred. to entrepreneurs, businesses, households The financial system comprises various and government for investments projects institutions, instruments and regulators. and other purposes with a view of returns.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

This forms the basis for economic The regulatory institutions in the development. financial system are the Ministry of The financial system play a key role in the Finance, the Reserve Bank of India as the mobilization and allocation of savings for apex institution in the money market and productive, use provide structures for the SEBI as the apex institution in the monetary management, the basis for capital market managing liquidity in the system. It also assists in the reduction of risks faced by The process of financial sector firms and businesses in their productive reform consists of the movement from an processes, improvement of portfolio initial situation of controlled interest rates, diversification and the insulation of the poorly developed money and securities economy from the international economic market and under-developed banking system, changes. The system provides the necessary towards a situation of flexible interest environment for the implementation of rates, an expanded role for market forces in various economic policies of the government resource allocation, increased autonomy for which is intended to achieve non- the central bank and a deepening of the inflationary growth, exchange rate stability, money and capital markets. The link balance of payments equilibrium foreign between financial sector stability and growth exchange management and high levels of is, explained by increased market depth, employment. which potentially increases market efficiency. The Indian financial system can It also reduces risks through the elimination be broadly divided into two sub-sectors, the of weak institutions. informal and formal sectors. The informal sector has no formalized institutional Need and Significance of the study framework, no formal structure of rates and Financial sector reforms seek to comprises the local money lenders, thrifts, develop an efficient framework for monetary savingsand loans associations.This sector is management. This encompasses efforts to poorly developed, limited in reach and not strengthen operational capacities of the banking integrated into the formal financial system. system, foster efficiency in the money and Its exact size and effect on the economy securities markets, over-haul the payments system remain unknown and a matter of and ensure greater autonomy to the central bank speculation. The formal sector, on the in formulating and implementing macroeconomic other hand, could be clearly distinguished policies. Thus, there is the need to deepen the into the money and capital market financial sector and reposition it for growth and institutions. The money market is the integration into the global financial system in short-term end of the market and conformity with international best practices. institutions here deal on short term This study is important at this level of instruments and funds. The capital market economic development when efforts are being encompasses the institutions that deal on made to reposition the financial system to enable long-term funds and securities. it play key roles in economic development of

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

India. The study essentially seeks to examine in sourced from the Reserve Bank of India an empirical manner, the nature of financial publications and those of the Bureau of deepening in India since the onset of financial statistics. The data was for the period1995– reforms in 1995up to 2017 when the banking 2017. The period chosen for the study consolidation took root in India. The study seek encompasses the phases of the major to ascertain the critical factors that have affected reforms in the financial system and the the level of financial deepening in India and to period of consolidation of the banking and ascertain if there is observable growth in the insurance systems in India. financial deepening index (money supply to In the present study, financial GDP) ratio in India. deepening defined as the ratio of money supply to GDP, is a function of the value of The MODEL Specification cheques cleared to GDP, value of cheques to money supply, ratio of private sector A model is identified if it is in a unique credit to GDP, financial savings to GDP, statistical form enabling unique estimates of rate of inflation, real lending rates, deposit the parameters to be subsequently estimated money bank assets to GDP, Currency from a sample data. In this study, the model outside Banks to money supply .and the used by Gosselin and Parent in their study of Dummy. the financial deepening function in pre and The equation specified for the study post financial reform periods in India. In was estimated using the stepwise least their specifications, six explanatory variables squares regression method. The model were used in investigating financial assists us to determine the T values and deepening. In this study, ninevariables were theFvalueswhichwereusedtotestthesignifica used. In this model Financial nceoftheequationspecified. Deepening(M/GDP)depends on, Financial The data used in the regression runs Savings/GDP ratio (FS/GDP) Private Sector are as shown in Tables 1.Theseareabsolute Credit/GDP (PSC/GDP) value of Cheques aggregates for each variable obtained for Cleared to GDP ratio (CHQ/GDP), value of theperiod1995–2017 (22years). The Cheques Cleared to Money Supply (CHQ/M) inflation rates are expressed in percentages, the Rate of Inflation (INFLAT), Prime while the savings rates are used as a proxy lending rates(PLR) the intermediation ratio for interest rates. These rates are also in i.e. Currency outside Banks to Money Supply percentages. The private sector (COB/M) and the Dummy. credits(PSC) are aggregate values and so to This model is given as financial savings (FS). The introduction of M/GDPit=f(PLRit),FS/GDPit,CHQ/GDPit,C the dummy variable seeks to capture the HQ/MINFLATit,PSC/GDPit, DMBA/GDPit, influence of political instability on the COB/MS2 + DUM. operations of financial institutions and this to a large extent influences financial Methodology deepening. Values of 0 to 1 are assigned to The data used in this study were the various years: 0 representing mild

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 instability, while 1 represent high levels of The overall fit of the regression model instability. The data were subsequently measured by the F- statistic, is statistically converted to the relevant ratios as shown in significant at this level. The Durbin Watson table 1. (DW) statistic of 1.551 indicates that there is no To test for stationarity and co- problem of serial correlation in the regression integration, the Durbin – Watson (SBDW) model. This is a case of positive serial test was adopted. It is important to note that correlation. Also, multicolinearity which often the present of co-integration in a model present in cross-sectional data seems to be non means that long-run equilibrium existent in the model. relationship exists among the non- In Table 2 the estimation results using stationery variables. the nine explanatory variables are presented at Results alpha equal to 0.05 level of significance and Regression Results also at 0.10. It was found that the financial The summary of financial deepening result savings ratio, interest rates, cheques from the Two stage regression analysis is cleared to GDP ratio and, Deposit Money shown in the model summary below. Banks Assets to GDP ratio are very useful Model Summary explanatory variables. Political instability is R = 0.972 not significant at both the 5 percent and 10 R2 = 0.946 percent levels. Adj R2 =0.906 Std error of estimate = 0.88808 The implication of the findings is that although Durbin-Watson = 1.551 the financial structure had enhanced the level F value = 23.62 of financial savings and thus affected the level d.f. = 22 of financial deepening positively, the financial system has not been efficient in resource The coefficient of correlation R and allocation evidently. Here, the process of Coefficient of determination R2 measure the intermediation in the system is not efficiently explanatory power of multiple regression done. Although the financial system has not grown tremendously in size and structure this models. From the results, there is a high has not been translated in the provision of coefficient of correlation (97.2percent). The implication is that the variables in the loans and credits especially to the real equation are useful for explaining the level sector of the economy. of financial deepening that has occurred between 1995 and 2017. There is also a highly significant coefficient of determination (94.6 percent). The standard error of the estimates also known as residual standard deviation has a value of 1.77708. The F- statistic value is found to be 23.62. The F value is significant at the 5 percent level.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

management in the financial system. Finally Table2: the supervision and regulation of banks EstimationResults should be strengthened, with a focus on risk Variable Means Std T Stat Remarks management. These shall be very useful in Dev. enhancing the level of financial deepening in V1 COB/M 24.97 5.154 .668 Not Sig India. V2 RO1 22.82 19.942 .330 Not Sig

V3 PLA 20.38 5.502 2.076* Sig V4 FS/EDO 12.47 7.439 3.453** Sig References: V5 57.33 47.764 2.107** Sig CHQ/GDP V6 CHQ/MS2 216.53 192.268 (2.492) Not Sig 1. Apergis, Nicholas; Filippidis, Ioannis; V7 PSC/GDP 17.30 8.008 (2.232) Not Sig Economidou, Claire (1 April 2007). "Financial Deepening and Economic V8 DMBA 37.07 12.425 6.565** Sig Growth Linkages: A Panel Data /EDP Analysis". Review of World Economics V9 DUM .27 .456 .606 Not Sig *Significant at 10 per cent level 2. Raghuram G. Rajan; Luigi Zingales **Significant at % per cent level (2003). "The great reversals: the politics of financial development in the twentieth Conclusion century" (PDF). Journal of Financial Economics. The study was concluded that the level of 3. (King and Levine, 1993; Levine and Zervos, 1998) financial deepening in India has remained 4. Raghuram G. Rajan; Luigi Zingales (June relatively low in spite of the various reforms 2016). "Financial Dependence and and institutional changes put in place by the: Growth"(PDF). The American Economic monetary authorities. It is also evident that the Review. low level of monetization of the economy, 5. "Deepening Rural Financial Markets: the high rate of inflation and the level of Macroeconomic, Policy and Political private sector credits have negatively affected Dimensions"(PDF). Researchgate.net. the level of financial deepening in India. Retrieved 5 November 2017. 6. Mohan, Rakesh (2006-11-03). "Economic Although the level of interest rates have Growth, Financial Deepening and remained very high, the level of private sector Financial Inclusion". Retrieved 6 credits have not sustained the desired level November 2017. of new investments necessary to facilitate 7. Reserve Bank of India-Various growth in the economy. However, there is an Publications urgent need to sustain a higher level of 8. www.rbi.ac.in macroeconomic stability in India, reduce the high incidence of non performing credits ensure that private sector credits are channeled to the real sector of the economy, enhance the level of corporate governance in the financial system and also strengthen risk

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

ANALYSIS OF TRENDS AND GROWTH OF DIGITAL RETAIL PAYMENTS SYSTEM IN INDIA

SUNIL S. Assistant Professor Government College, Attingal

Abstract

India with its unique rich payment ecosystem is now emerging as a global one in innovative digital payment systems. The Reserve Bank of India and the Government have expressed a vision of a less cash civilization and guided its evolution with feet stick on the ground. The growth of financial services in India has largely been driven by the banks. The regulator as well as the banks has led the initial push, development and support of digital payments infrastructure. Non-banks have entered the market and expanded the range of payment services available to the Indian consumer backed by their strength in technology and customer oriented innovation. Banks and non-banks are partnering to offer the combination of trust and innovation to the Indian consumer. This will resulted in a recent growth in the number of digital payments, should continue. Keywords: Digital Banking, Digital Payments, Retail Banking,

Introduction the middle class, the businesses and the nation. India is significantly behind peers on digital India remains a largely cash based transactions, and digitization will create a economy with cash accounting for more than multiplier effect on efficiency of capital and 78% of all retail payments before 2014-15. resource allocation through greater Compared to some other countries, like China, transparency, traceability of transactions, Mexico and Brazil, India ranks very low enforce ability of law and significantly buoyed relating to Non-cash transactions by non-banks tax revenues which will make bigger State‟s per capita per annum as well as number of pay resources for social welfare. points (for digital payments) per million Why this study is important? people. The cash dependence, in turn, has With a view to encouraging digital impacted government‟s ability to widen tax payments and enhancing financial inclusion compliance and increase tax revenue. through digitalization, the Reserve Bank of Digitisations of transactions become an India decided to constitute a High-Level obligation for India; it will benefit the poor, Committee on Deepening of Digital Payments

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 to assess the existing status of digital payments of instruction, authorization or order to a bank and level of digital payments in financial to debit or credit an account maintained with inclusion, identify best practices that can be that bank through electronic means and adopted, recommend initiatives to strengthen includes point of sale transfers; automated safety and security of digital payments, lay teller machine transactions, direct deposits or down a plan of action to increase customer withdrawal of funds, transfers initiated by confidence in digital financial services, and telephone, internet and, card payment. suggest a Medium-Term strategy for Digital Payment Systems deepening of digital payments. The payment system in India is classified into two main segments: Objectives of the Study 1. Instruments which are covered under 1.To study the retail payment systems existing Systemically Important Financial Market in India Infrastructure (SIFMIs), and 2.To analyse the performance evaluation of 2. Retail Payments. growth and trend of digital retail payments in 1. Systemically Important Financial India Market Infrastructure (SI-FMI): Digital Payment Payments Systems in India Financial Market Infrastructure (FMI): It is The RBI Ombudsman scheme for defined as a multilateral system among digital transactions defines a „Digital participating institutions, consist of the Transaction‟ as “Digital Transaction‟ means a operator of the system, used for the purposes payment transaction in a seamless system of clearing, settling, or recording payments, affected without the need for cash at least in securities, derivatives, or other financial one of the two legs, if not in both. This dealings. Under SIFMI, new standards or includes transactions made through digital / principles are intended to ensure that the vital electronic modes wherein both the originator financial market infrastructure (FMI) and the beneficiary use digital / electronic sustaining global financial markets is even medium to send or receive money.” more dynamic and thus even better suited to endure financial shocks than at present. The Payment and Settlement Act, 2007 Under this segment (SIFMI) there are four has defined Digital Payments, as any instruments of payments: "electronic funds transfer" that is any transfer of funds which is initiated by a person by way

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

RTGS: Real Time Gross Settlement is defined Forex transactions is done by CCIL which was as the continuous, real-time settlement of fund started in 2002. transfers individually on an order by order basis without netting. 'Real Time‟ means the 2. Digital Retail Payments: processing of instructions at the time they are Under the Retail Payments segment which has acknowledged rather than at some later time; a large user base, there are three broad 'Gross Settlement' means the settlement of categories of instruments. They are Paper fund transfer instructions occurs individually Clearing, Retail Electronic Clearing, and Card on an instruction by instruction basis. This Payments. The instruments under these three system is primarily intended to large value categories are described below: transactions. The minimum amount to be Cheque Truncation System (CTS): CTS or remitted through RTGS is ` 2 lakh. For inter- online image-based cheque clearing system is bank fund transfer there is no minimum. a cheque clearing system undertaken by the CBLO: Collateralised Borrowing and Lending Reserve Bank of India (RBI) for faster clearing Obligation (CBLO) is a money market of cheques. It eliminates the cost associated instrument introduced by Clearing Corporation with the movement of physical cheques. of India Ltd. (CCIL), in 2003. This represents Non-MICR: The Non-MICR (Non-Magnetic an obligation between a borrower and a lender Ink Character Recognition) clearing refers to to the terms and conditions of a loan. It also the process of manual clearing of cheques does not entail physical transfer of respective where the cheque is physically moved between securities from borrower to lender or vice the bank branches/banks for clearing. MICR is versa. a technology used to verify the legitimacy or Government Securities: A Government originality of paper documents, especially Security (G-Sec) is a tradable instrument checks. issued by the Central Government or the State ECS DR/CR: ECS (Electronic Clearing Governments. System) is an electronic mode of payment / Forex Clearing: The term „Forex‟ stands for receipt for transactions that are repetitive and Foreign Exchange. In simple terms it is the periodic in nature. DR/CR is „Debit Record or trading in currencies from different countries Credit Record‟. ECS facilitates bulk transfer of against each other. In India the settlement of monies from one bank account to many bank accounts or vice versa. ECS includes

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 transactions processed under National Understanding the importance of mobile Automated Clearing House (NACH) operated banking in financial inclusion, *99# was by National Payments Corporation of India dedicated to the nation by Hon‟ble Prime (NPCI). minister on 28th August 2014, as part of NEFT: National Electronic Funds Transfer „Pradhan Manti Jan Dhan Yojna‟. (NEFT) is a nation-wide payment system USSD (Unstructured Supplementary Service facilitating one-to-one funds transfer. Under Data) is a Global System for Mobile (GSM) this scheme, individuals, firms and corporate communication technology that is used to send can electronically transfer funds from any bank text between a mobile phone and an branch to any individual, firm or corporate application program in the network. having an account with any other bank branch NACH: “National Automated Clearing House in the country participating in the scheme. (NACH)” is a service offered by NPCI to IMPS: Immediate Payment Service (IMPS) banks which aims at facilitating interbank high offers an instant 24X7 interbank electronic volume, low value debit/credit transactions, fund transfer service through mobile phones. which are repetitive and electronic in nature. It IMPS are an emphatic tool to transfer money allows participating banks for centralized instantly within banks across India through posting of inward debit/credit transactions and mobile, internet and ATM. It is offered by is run by NPCI. National Payments Corporation of India Credit Card: A credit card is a card issued by (NPCI). a financial company which enables the UPI: Unified Payments Interface (UPI) is a cardholder to borrow funds. The issuer pre-sets system that powers multiple bank accounts borrowing limits which have a basis on the into a single mobile application (of any individual's credit rating. These cards can be participating bank), merging several banking used domestically and internationally and can features, seamless fund routing & merchant also be used to withdraw cash from an ATM payments into one hood. and for transferring funds to bank accounts, *99#: USSD based mobile banking service of debit cards and prepaid cards within the NPCI was initially launched in November country. 2012. The service had limited reach and only Debit Cards: A debit card is a payment card two TSPs (Telecom Service Provider) were that deducts money directly from a consumer‟s offering this service i.e. MTNL & BSNL. bank account to pay for a purchase and

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 eliminate the need to carry cash or physical Banks issuing such PPIs shall also facilitate checks to make purchases. In addition, they cash withdrawal at ATMs/Point of Sale offer the convenience of credit cards for small (PoS)/Business Correspondents (BCs). negative balances that might be incurred if the Analysis and Discussion account holder has signed up for overdraft India‟s payment system - particularly, coverage. However, debit cards usually have its digital payments system - has been evolving daily purchase limits. impotently for the past many years, due to the Pre-Paid Instruments (PPIs): PPIs are developments in information and payment instruments that facilitate purchase of communication technology (ICT), and fostered goods and services, including financial and in line with the path envisaged by the services, remittance facilities, etc., against the Reserve Bank of India. The National Payments value stored on such instruments. PPIs are Corporation of India (NPCI) was established classified under three types: in 2008 with the aim of achieving the vision by Closed System PPIs: These PPIs are issued by the RBI and Government of India. Important an entity for facilitating the purchase of goods milestones attained in this overall process of and services from that entity only and do not development of the payments system permit cash withdrawal. comprises: Semi-closed System PPIs: These PPIs are a. The introduction of MICR clearing in used for purchase of goods and services, the early 1980s, including financial services, remittance b. Electronic Clearing Service and facilities, etc., at a group of clearly identified Electronic Funds Transfer in the 1990s, merchant locations/establishments which have c. Issuance of credit and debit cards by a specific contract with the issuer (or contract banks in the 1990s, through a payment aggregator/payment d. The National Financial Switch in 2003 gateway) to accept the PPIs as payment that brought about interconnectivity of instruments. These instruments do not ATMs across the country, permit cash withdrawal. e. The RTGS and NEFT in 2004, Open System PPIs: These PPIs are issued only f. The Cheque Truncation System (CTS) by banks and are used at any merchant for in 2008, purchase of goods and services, including financial services, remittance facilities, etc.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

g. The second factor authentication for 1.1. Overall Growth Performance of digital the „card not present‟ transaction in retail payments (Volume) 2009, and Table: 1.1. Overall Growth Performance of h. The new RTGS with enhanced digital retail payments (Volume)

facilities and features in 2013 [Mundra Volume Growth in Year (2015)]. (in Millions) Volume (%) 166.95 0 Moreover, non-bank entities have been 2003-04 2004-05 228.9 37.11 permitted to issue of pre-paid instruments 2005-06 285.03 24.52 (PPI), including mobile and digital wallets. 2006-07 378.72 32.87 535.32 41.35 These have been supported by significant 2007-08 2008-09 667.81 24.75 initiatives of the NPCI including the launching 2009-10 718.16 7.54 of grid-wise operations of CTS, 2010-11 908.58 26.51 2011-12 2532.5 178.73 interoperability on NACH, IMPS, NFS, RuPay 2012-13 2939.5 16.07 (a domestic card payment network), APBS and 2013-14 3627.7 23.41 AEPS (which are an important part of the 2014-15 4620.9 27.38 2015-16 6945.2 50.3 financial inclusion process), National Unified 2016-17 10879.7 56.65 USSD. 2017-18 15760.6 44.86 Here, the growth trends in Digital Source: Reserve Bank of India (2019)

Retail Payments over the past years are Graph: 1.1. Trend in Retail Digital discussed. The narrative on the growth trends Payments – Volume which covers the period from 2003-04 to 18000 200 2017-18 is presented. The analysis covers the 16000 14000 150 trends over the years 2003-04 to 2015-16 ie., 12000 10000 100 the years preceding demonetization and 8000 compares the growth trends over the last two 6000 4000 50 years ie. 2016-17 and 2017-18 this is the post 2000 0 0 demonetization period. The analysis of trend and growth of digital retail payment is made on the basis of data provided by reserve Bank Volume (in Millions) of India during the respective periods. Growth in Volume (%)

1.Overall Growth Trend of Retail Payment:

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

The values of volume of overall digital retail 2016-17 220634 24.12 payment are measured in the primary axis and 2017-18 285612 29.45 Source: Reserve Bank of India (2019) growth of volume on the secondary axis. From the above table and figure the volume of Graph: 1.2. Trend of Overall Growth overall retail payments steadily increased over Performance of Retail Payments – Value the period from 2011-12 to 2017-18, recording 300000 900 a compound average annual growth rate 800 250000 700 (CAGR) of over 39.47 per cent. The rate of 200000 600 growth in volume of overall retail payments 500 150000 400 further accelerated to 44.86% per cent in 2017- 300 100000 200 18. Graph 1 indicates the trends in Retail 50000 100 0 Digital Payments over the period of 2003-04 to 0 -100 2017-18. The growth in 2011-12 is spectacular and could be attributed to development of innovative digital payments platform. But Value (in Billion) Growth in Value (%) during 2017 -18 the growth rate is declining Source: Reserve Bank of India (2019) from 56.65% (2016-17) to 44.86% (2017-18). The values of value of overall digital retail 1.1. Overall Growth Performance of Retail payment are measured in the primary axis and Payments (Value): growth of value on the secondary axis. The Table: 1.2. Overall Growth Performance of above figure shows that the nominal value of Retail Payments (Value) retail payments has a cyclical movement over

Year Value (in Billion) Growth in Value( %) 2003-04 to 2014-15, though it has a steady 2003-04 521.44 0 growth of 825.74% in the year 2011-12, and 2004-05 1087.49 108.56 2005-06 1463.81 34.6 a decline in the growth from 2012-13 to 2014- 2006-07 2356.93 61.01 15. Thereafter, an increase in value of retail 2007-08 10419.91 342.1 payments records a CAGR of 102.8 per cent. 2008-09 5003.22 -51.98 2009-10 6848.86 36.89 But the annual growth has increased to 29.45% 2010-11 13086.88 91.08 in 2017-18 due to demonetization. 2011-12 121149.9 825.74 2. Instrument Wise Growth Trends of 2012-13 134114.4 10.7 2013-14 143447.4 6.96 Retail Payments: 2014-15 154129.3 7.45 2.1. ECS Debit (Volume and Value): 2015-16 177753 15.33

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

The following table (table 2.1) and graph

(graph 2.1) shows the trend of both volume and value of ECS Debit transactions during the Graph: 2.1. ECS Debit (Volume and Value) period from 2003-04 to 2017-18. 250 2000 From the Graph 2.1 below, the values of 1800 200 1600 volume of ECS Debit is measured in the 1400 primary axis and value of ECS Debit on the 150 1200 1000 secondary axis. It is observed that both the 100 800 600 volume and value of ECS Debit increases from 50 400 2003-04 to 2008-09 at an increasing rate. From 200 0 0 2009-10 onwards up to 2014-15 there is a linear rate of increase. During the period 2014- 15 and 2015-16 it get stabilizes and start declining at a higher rate during 2016-17 and Volume (in … Source: Reserve Bank of India (2019) 2017-18. 1.1. ECS Credit (Volume and Value)

Table: 2.1. ECS Debit (Volume and Value) Table: 2.2. ECS Credit (Volume and Value) Volume Value Year Volume Value (in Millions) (in Billions) Year (in Millions) (in Billions) 2003-04 20.32 102.28 2003-04 7.87 22.54 2004-05 40.05 201.80 2004-05 15.30 29.21 2005-06 44.22 323.24 2005-06 35.96 129.86 2006-07 69.02 832.73 2006-07 75.20 254.41 2007-08 78.37 7822.22 2007-08 127.12 489.37 2008-09 88.39 974.87 2008-09 160.05 669.76 2009-10 98.13 1176.13 2009-10 149.28 695.24 2010-11 117.30 1816.86 2010-11 156.74 736.46 2011-12 121.50 1837.80 2011-12 164.70 833.60 2012-13 122.20 1771.30 2012-13 176.50 1083.10 2013-14 152.50 2492.20 2013-14 192.90 1268.00 2014-15 115.30 2019.10 2014-15 226.00 1739.80 2015-16 39.00 1059.00 2015-16 224.80 1652.00 2016-17 10.10 144.00 2016-17 8.80 39.00 2017-18 6.10 115.00 2017-18 1.50 10.00 Source: Reserve Bank of India (2019) Source: Reserve Bank of India (2019)

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

2010-11 132.34 9391.49

2011-12 226.10 17903.50 Graph: 2.2. Trend in ECS Credit Volume 2012-13 394.10 29022.40 and Value 2013-14 661.00 43785.50 2014-15 927.60 59803.80 180 9000 160 8000 2015-16 1252.90 83273.00 140 7000 2016-17 1622.10 120040.00 120 6000 2017-18 1946.40 172229.00 100 5000 Source: Reserve Bank of India (2019) 80 4000 60 3000 Graph: 2.3. EFT/NEFT volume and Value 40 2000 20 1000 2500 200000 0 0 180000 2000 160000 140000 1500 120000 100000 1000 80000 Volume (in Millions) Value (in Billions) 60000 500 40000 Source: Reserve Bank of India (2019) 20000 0 0 The value of volume of ECS Credit is measured in the primary axis and value of ECS Credit on the secondary axis. From the above, both Volume (in Millions) Value (in Billions) volume and value of ECS credit increase from 2003-04 to 2013-14 with an exceptional increase Source: Reserve Bank of India (2019) The following table and graph describe the in the value of ECS credit during the year 2007- trend and growth of volume and value of 08. From 2014-15 to 2017-18 it shows that both EFT/NEFT for the period from 2003-04 to the volume and value declining at an increasing 2017-18. Both volume and value move on the rate. same direction at an increasing rate especially 1.1.EFT/NEFT volume and Value: after 2009-10 up to 2017-18. The value of Table: 2.3. EFT/NEFT volume and Value volume of EFT/NEFT is measured in the Volume Value Year (in Millions) (in Billions) primary axis and value of EFT/NEFT on the 2003-04 0.82 171.25 secondary axis. From the chart and graph 2004-05 2.55 546.01 below, the volume and value of EFT/NEFT 2005-06 3.07 612.88 2006-07 4.78 774.46 from 2003-04 to 2009-10 shows stagnation 2007-08 13.32 1403.26 and afterwards an increase in both up to 2014- 2008-09 32.16 2519.56 15. The rate of growth increases from 2015-16 2009-10 66.34 4095.07

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 to 2017-18, due to increased penetration of digital payments and demonetisation. 1.1. Credit (CR) Card Graph: 2.4. Credit (CR) Card

The value of volume of credit card transactions 1600 5000 is measured in the primary axis and value of 1400 4500 4000 1200 credit card transactions on the secondary axis. 3500 From the following graph, the volume and 1000 3000 800 2500 value of credit card transactions shows a 600 2000 1500 constant growth from 2003-04 to 2008-09. 400 1000 During the year the there is a negative growth 200 500 in both volume and value on 2009-10. From 0 0 2010-11 onwards the rate of growth in credit cards declining up to 2014-15 and thereafter an increase in growth rate of credit card Volume (in Millions) Value (in Billions) transactions from 2015-16 to 2017-18. Source: Reserve Bank of India (2019) 1.1.Debit (DR) Card Table: 2.4. Credit (CR) Card Table: 2.5. Debit (DR) Card Volume Value Year (in Millions) (in Billions) Volume Value Year (in Millions) (in Billions) 2003-04 37.76 48.74 2004-05 41.53 53.61 2003-04 100.18 176.63 2005-06 45.69 58.97 2004-05 129.47 256.86 2006-07 60.18 81.72 2005-06 156.09 338.86 2007-08 88.31 125.21 2006-07 169.54 413.61 2008-09 127.65 185.47 2007-08 228.20 579.85 2009-10 170.17 264.18 2008-09 259.56 653.56 2010-11 237.06 386.91 2009-10 234.24 618.24 2011-12 327.50 534.30 2010-11 265.14 755.16 2012-13 469.10 743.40 2011-12 320.00 966.10 2013-14 619.10 954.10 2012-13 396.60 1229.50 2014-15 808.10 1213.40 2013-14 509.10 1539.90 2015-16 1173.50 1589.00 2014-15 615.10 1899.20 2016-17 2399.30 3299.00 2015-16 785.70 2407.00 2017-18 3343.40 4601.00 2016-17 1087.10 3284.00 Source: Reserve Bank of India (2019) 2017-18 1405.20 4590.00 Source: Reserve Bank of India (2019)

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

2017-18, recording the rate of growth in Graph: 2.5. Debit (DR) Card volume of overall retail payments further accelerated to 44.86% per cent in 2017-18. 4000 5000 3500 c. The growth in 2011-12 is spectacular and 3000 4000 2500 3000 could be attributed to development of 2000 innovative digital payments platform. But 1500 2000 1000 1000 during 2017 -18 the growth rate is 500 0 0 declining from 56.65% (2016-17) to 44.86% (2017-18). 2. Value of Overall Digital Retail Payments

Volume (in Millions) Value (in Billions) a. The value of retail payments has a cyclical

movement over 2003-04 to 2014-15, Source: Reserve Bank of India (2019) The value of volume of debit card transactions though it has a steady growth of 825.74% is measured in the primary axis and value of in the year 2011-12, debit card transactions on the secondary axis. b. There was a decline in the growth of value Graph 2.5 above states that the growth of from 2012-13 to 2014-15. volume and value of debit card transactions c. An increase in value of retail payments increases from 2003-04 to 2009-10, but the records a CAGR of 102.8 per cent between pace of growth is less. From 2011-12 to 2014- 2003-04 to 2017-18, but the annual growth 15 the growth of debit card transactions in has increased to 29.45% in 2017-18 due to terms of volume and value decreases and then demonetization. shows a steep increase in both in 2016-17 and 3. It is observed that both the volume and it shows a slow pace during 2017-18. value of ECS Debit increases from 2003- Results 04 to 2008-09 at an increasing rate. From 1. Volume of Overall Digital Retail 2009-10 onwards up to 2014-15 there is a payments linear rate of increase. During the period a. In India, compound average annual growth 2014-15 and 2015-16 it get stabilizes and rate (CAGR) of total digital retail start declining at a higher rate during 2016- payments (in volume) is 39.47 per cent. 17 and 2017-18. b. Volume of overall retail payments steadily 4. Both volume and value of ECS credit increased over the period from 2011-12 to increase from 2003-04 to 2013-14 with an

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

exceptional increase in the value of ECS Reference: credit during the year 2007-08. From 2014- 1. Digital payment: Trends, Issues, and 15 to 2017-18 it shows that both the Opportunities, NITI Aayog, July, 2018 volume and value declining at an 2. Aarti Sharma and Nidhi Piplani, 2017, International Research Journal increasing rate. of Management Science & Technology, 5. The volume and value of EFT/NEFT from IRJMST Vol 8 Issue 1 [Year 2017] ISSN 2250 – 1959, http://www.irjmst.com 2003-04 to 2009-10 shows stagnation and 3. RBI Report on Trend And Progress of afterwards an increase in both up to 2014- Banking In India 2009-10, 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, 15. The rate of growth increases from 2015-16, 2016-17, 2017-18. 2015-16 to 2017-18, due to increased 4. RBI Monthly Bulletin March 2013, 2014,2015, 2016,2017, & 2018 penetration of digital payments and 5. CURRENT STATISTICS: Money & demonetisation. Banking, No. 9A: Retail Electronic Payment Systems, RBI Monthly

6. The volume and value of credit card Bulletin, November, 2012 transactions shows a constant growth from 6. BCG Google Digital Payments 2020: The Making of a $500 Billion 2003-04 to 2008-09. During the year the ecosystem in India, July, 2016_tcm21- there is a negative growth in both volume 39245 http://image-src.bcg.com/Indian- Banking-2020-Sep-2010_tcm21- and value on 2009-10. From 2010-11 28897. onwards the rate of growth in credit cards 7. NITI Aayog, Government of India, Interim Report of the Committee of declining up to 2014-15 and thereafter an Chief Ministers on #Digital Payments, increase in growth rate of credit card January, 2017 8. Rajasekhara v Maiya, 2017 6 transactions from 2015-16 to 2017-18. Technology Trends That Will 7. The growth of volume and value of debit Transform Banking In 2017. (2017, January 2). Retrieved from card transactions increases from 2003-04 Huffington Post India website: to 2009-10, but the pace of growth is less. https://www.huffingtonpost.in/rajas From 2011-12 to 2014-15 the growth of hekara-v-maiya/6-technology- trends-that-will-transform-banking- debit card transactions in terms of volume in-2017_a_21645614/ and value decreases and then shows a steep

increase in both in 2016-17 and it shows a slow pace during 2017-18.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

KILLING THE GOLDEN GOOSE- THE CASE OF PRIVATE BUSES IN KERALA

PRAGEETH.P Dr. ANZER R.N Research Scholar Assistant Professor Government College, Attingal Government College,

ABSTRACT

Public Transportation services are integral to societies and are vital for civic life. Recently, many countries have twisted their attention towards emerging and refining their public transport system. But, here in our country especially in the state of Kerala, the government is doing all the obligatory procedures towards Public transport system (KSRTC) and Private transport services. Frequently changing laws and implementing strict rules which are against the industry. This study tries to find out the problems been faced by the Private bus sector in the State of Kerala. This study is an innovative one and will help to understand the reason for the downfall of the industry during the recent times.

Key Words: Public Transportation, Private Bus

Introduction: population, and hence the growth of the sector in India requires major requirements. Transportation has been the key to mobility. Transportation helps to move Public Transportation services are peoples and goods from one place to another. integral to societies. Countries need effective Realising the huge potential of public public transport services for transit users, who transport, countries around the world have need and value different modes of public been investing huge amounts of money into transport. Public Transportation is defined as the transportation sector every year. transportation by means of conveyance that Transportation is essentially a derived demand provides continuing general or specific depending upon the size and structure of the transportation to the public, which includes economy and the demographic profile of the school buses, Charter and sightseeing services

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 which includes various modes such as buses, Private Bus Operation in Kerala- A subways, rails, trolleys and `ferry boats [ Train Present Scenario and Weiner]. In an urbanized society, an Private –Operated buses are efficient efficient transportation system is one of the and add value to government‟s exchequer basic components of the social, economic and saving it from the liability of providing physical structure. transport. The Government is earning high through taxes on private-operated buses, with Development of public transportation estimated earnings of ₹ 120000 from each system is costly. Thus, private investment is private bus annually by way of road tax. The often critical and considered effective in government earns around ₹750 crores from delivering the required products and services. private bus operators every year. For instance, incentives and competition have enabled private players to provide highly Nowadays, private bus operations are efficient transport systems. Private sector in a state of roadblock. Government are involvement in building and facilitating public imposing and changing laws regularly, which transportation has generated positive outcome affects the proper functioning of this sector. around the world. Earlier private buses were seen as a dignity of power, but now this sector is finding difficult Kerala is one of the highly urbanised to earn its working capital. states in India (47.72 per cent as per census 2011) and has a significant number of people As the government is focusing to bring covering long distances between 20 and 300 KSRTC in the way of making profit, more kilometres. Cities in Kerala rank high in Public restriction isbeing imposed on the opponents. Transport Accessibility Index and City Bus Renewal of permit are not being made, Transport Supply Index with a high takeover of route permit by KSRTC, heavy penetration of public transportation buses. The road tax and Insurance etc. made this sector in composition of public bus system is one of the the state of diminishing. Many owners are highest in the country. Kerala is unique for its surrendering their permit to the RTO because high public transport model share, Thanks to on the heavy loss in this sector. As per reports, the role played by private-operated buses. The non-renewal or shutting down of private bus major share being held by private sector but sector would affect five to six employees of now being captured by KSRTC. private bus adversely.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Assessing, rural to urban trips, we can The hike in fuel price, increase in tax see that people rely more on private operated and insurance hasbrought in various challenges bus. However, now KSRTC took over major for the private bus sector. Drastic changes permits and after few operations, they are were proposed by private bus owners to stopping their services, now people are finding overcome this challenge. Government had difficult for their journey. Increase in use of responded to the demands of the bus industry two-wheelers, private owned cars paved the and agreed for a fare hike. The implications of way for collapse of this private bus sector. the above mentioned changes are to be analysed in depth. Therefore, the present study Driving private bus operations out of is entitled as “Killing the Golden Goose- the the market will lead thousands jobless. A huge Case of Private Buses in Kerala” part of income from private bus goes to low- income and self-employed populations. Limitation of the Study: Government policies not only disrupt a normal The study is limited to only 14 private buses in and healthy market mechanism but also kill the the state incentive for private and public operators to provide better service to passengers Only buses from district were taken as part of this study Objectives of the Study: Analysis and Interpretation 1.To study the performance Evaluation of private buses 1. To study the performance 2.To understand the trend in Evaluation of private buses in Kerala differentexpenditures related Comparison of Receipt and Payment of 14 Private Buses Research Methodology: Collection of Total Profit

Primary data was essential to Year The Year Expenses understand the performance of private buses. 2008-09 2,51,59,576 2,27,73,294 23,86,282

The Primary information was collected from 2009-10 2,44,31,584 2,20,58,246 23,73,338 trip sheet of various private bus operations. 2010-11 2,58,40,422 2,37,25,331 21,15,091

Research Problem: 2011-12 2,82,50,601 2,58,61,492 23,89,109

2012-13 2,99,65,419 2,76,37,177 23,28,242

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

11 years. From the table it is clear that the 2013-14 2,86,00,144 2,72,83,723 13,16,421 collection is increasing, the collection 2014-15 3,00,99,594 2,77,20,652 23,78,942 increased due to the increase in the minimum 2015-16 3,26,30,614 3,00,43,151 25,87,463 fare charge from year to year. At the same time 3,16,29,985 2,99,54,633 16,75,352 2016-17 the total expenses are increasing at a higher

2017-18 3,18,66,460 3,12,83,367 5,83,093 rate. This resulted in decrease of profit during

2018-19 3,27,74,294 3,22,81,533 4,92,761 the recent years from 2016 to 2019. To analyse Source: Compiled from Bus owners the trend in these three variables graphical The above table shows the collection, total presentation by fitting trend line is used. The expense and the profit of 14 private buses over results are given below.

Receipt and Payment 40,000,000

35,000,000 y = 85523x + 2E+07 R² = 0.888 30,000,000

y = 1E+06x + 2E+07 25,000,000 R² = 0.956

20,000,000

15,000,000

10,000,000

5,000,000 y = -15909x + 3E+06 R² = 0.485

0 2008-09 2009-10 2010-11 2011-12 2012-13 2013-14 2014-15 2015-16 2016-17 2017-18 2018-19

Collection Total Expenditure Profit

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

From the line chart for the time series Rs.85523 every year. Likely, for total of collection, total expenses and profit of expenditures also, an annual linear increase of selected private transport companies during the Rs1000000 is seen with an explanation of 95.6 last 11 years, it can be seen that both collection per cent (R2 =95.6). Thus it can be observed and total expenses are increasing over the that total expenditures are increasing at a years. But a slight decrease in trend is seen in higher rate than the total receipts i.e. the profit. An observation of linear regression Collections. As a result of this, the profit line fitted to all data gives a detailed seems to have an annual decrease of Rs15909 explanation of rate of change over time in all with an explanation of 48.5 per cent (R2 variables. Thus of collections, an annual linear =48.5). Thus the profit per year is decreasing increase of Rs.85523 is observed with an at the rate of Rs.15909 every year. explanation of 88.8 per cent (R2 =88.8). That is 2. To understand the trend in different collections are increasing at the rate of expenditures related

Various Expenditure Table

Year Accident Diesel and Oil Insurance Repairs and Wages and Damage and Tax Maintenance allowances 2008-09 26,528 1,34,70,033 15,60,186 29,56,665 47,59,882 2009-10 97,677 1,27,62,053 15,85,486 28,71,151 47,41,879 2010-11 35,677 1,35,85,084 16,84,060 29,54,793 54,65,717 2011-12 44,557 1,38,57,964 16,95,378 37,41,739 65,21,854 2012-13 38,495 1,45,14,292 20,80,382 37,13,337 72,90,671 2013-14 25,351 1,48,39,427 19,77,974 31,00,252 73,40,719 2014-15 24,341 1,50,29,056 20,12,376 30,98,689 75,56,190 2015-16 34,221 1,52,28,576 20,86,661 33,55,268 93,38,425 2016-17 10,199 1,59,12,819 18,38,745 25,79,373 96,13,497 2017-18 27,598 1,65,32,926 20,17,572 29,43,823 97,61,448 2018-19 3,740 1,84,86,763 19,76,757 29,77,682 88,36,591 Source : Compiled data from bus owners

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

The above table indicates the various table that expenses under each head during the expenditure incurred by private bus during the past few years are increasing. course of its operations. It is clear from the

Various Expenditure 20,000,000 18,000,000 y = 45823x + 1E+07 16,000,000 R² = 0.879 accident damage 14,000,000 Diesel and Oil 12,000,000 Insurance and Tax 10,000,000 y = 53458x + 4E+06 R² = 0.901 Repairs and maintenance Axis Title Axis 8,000,000 Wages and Allowances 6,000,000 y = -19255x + 3E+06 4,000,000 R² = 0.032 2,000,000 y = 45362x + 2E+06 y = -4595.6x + 61063 R² = 0.5673 R² = 0.3915 0

Axis Title

From the line chart for the time series regression line fitted to all data gives a detailed of various expenditure of selected private explanation of rate of change over time in all transport companies during the last 11 years, it variables. Thus for Diesel and oil expenditure, can be seen that the diesel and oil expenses are an annual linear increase of Rs 458239 is increasing over the years and a slight increase observed with an explanation of 87.97per cent in Insurance and Tax expense can also be seen. (R2 =87.97). That is collections are increasing But a decrease in trend is seen in other at the rate of Rs.458239 every year. Likely, for expenses such as repairs and maintenance., Wages and Allowances also, an annual linear wages and allowances can also have been seen. increase of Rs 534580 is seen with an However, accident expense shows a steady explanation of 90.12 per cent (R2 =90.12). trend over the years. An observation of linear Similarly, for Repairs and Maintenance also,

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 an annual linear decrease of Rs 19255is seen  During 2018-19 (Rs. 88,36,591) wages with an explanation of 3.12 per cent (R2 and allowances has decreased =3.12). Likely,for Insurance and Tax also, an comparing 2017-18 (Rs. 97,61,448). annual linear increase of Rs 445362 is seen This decrease occurred as the owners with an explanation of 56.73 per cent (R2 were forced to abridged down the =56.7). Like Wise, for Accident Damage also, number of employees an annual linear decrease of Rs 4595.6is seen with an explanation of 39.15 per cent (R2 Suggestions =39.15).  To overcome the decline in profit the Findings government should rise the minimum bus fare and should increase the The major findings drawn from the concession rate study are as follows  Reducing tax rates and insurance  Profits of the private bus industry amount by the government will be a lift during the past eleven years i.e. from to the economy [2008-09 to 2018-19] is decreasing  Instead of collecting tax in quarter the over the years i.e. in 2008-09 profit government should collect taxes half was Rs 23,86,282 and in 2018-19 it yearly came down to Rs 4,92,761, a  Subsidies for fuel for public diminution of Rs 18,93,521 occurred transportation should be made during the years  Strict laws should be framed by the  While analysing the per unit profit of government to use public fourteen buses over the past eleven transportation for e.g.: as made in years four buses are facing losses Delhi Single and Double number during the last three years permit should be implemented. If such  Increase in various expenditure related laws are made this will boost the with the industry such as diesel and oil, industry and also helps in reduces Spare parts, Road Tax and Insurance pollution. over the past few years are the major

reason for the doleful condition of the industry

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Conclusion: 2. Kadam B. S. (2012): “Work Life Balance: Dilemma of Modern Society- The private bus industry in the state is A Special Reference to Women Bus in the doldrums with the number of the buses Conductors in MSRTC”, Zenith on the road coming down from 34000 in 2011 International Journal of Business to 12500 by September 2018. Earlier, in the Economics & Management Research, state of Kerala, private buses were seen as a Vol.2, Issue 2, Feb. 2012. symbol of status. This industry provides more 3. V. Vijay and Durga Prasad (2011): revenue to the government directly and “Passenger Amenities of Andhra indirectly and also creates more employment Pradesh State Road Transport opportunities. But now this industry has Corporation (APSRTC)” Asian Journal become a headache to the owners and some are of Business Management Studies 2(2), still continuing services as part of Pp76-83. commitment. In September 25 2018 RTA 4. Mane K. H. (2010): “Commuters Kollam has faced surrendering of 14 route Satisfaction with Reference to permit in a single day. If this scenario ServiceProvide by MSRTC”, continues Kerala economy as well as the state International Referred Research will be facing many problems from this sector. Journal,Vol. II, Issue 18, July, 2010. Therefore, steps should be taken by the 5. Bishnoi N. K. Sujarat (2010), “An government and the authorities to find Analysis of Profitability solutions in this sector. andProductivity of Haryana State Road References: Transport Undertaking(HSRTU)”, ENVISION – Apeejay‟s Commerce 1. Gawali S.N. and Waghere Y.M. and ManagementJournal, Pp. 32-41 (2013): “Life Line of Maharashtra – 6. Jadav Chandra and Amar (2010), Maharashtra State Road Transport “Towards Sustainable Corporation (MSRTC)” Online Competitions”,Indian Economic International Interdisciplinary Research Review (Special Number) Vol.18. Journal, (Bi-Monthly), ISSN2249- 9598, Volume-III,Issue-II, Mar-Apr 2013

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

TRENDS AND GROWTH OF

THANSIYA N Research scholar (full time) Government college Attingal

Abstract

Tourism is a globally accepted industry because of its economic- social and cultural contributions. Kerala, the green gateway of India, has today found a niche for herself in the international tourism map, from the point of view of tourist inflow as well as investments in tourism related sectors. In the state of Kerala, both the domestic and foreign tourist arrivals are increasing day by day. There is an influence of changing season of Kerala towards the number of arrivals of tourists. Tourism industry of Kerala is an indicator of economic growth in terms of foreign exchange earnings; employment and infrastructure. Here, an attempt is made to analyze the trends and growth in this sector.

Introduction

Tourism is considered as one of the tourists from all over the world, especially driving elements to the progress of economy. from the UK, USA, France and Australia. Tourism is a globally accepted industry Kerala Tourism is to position itself as a global because of its economic- social and cultural destination for tourism, based on the advantage contributions. Tourism contributes towards of the local resources, thereby attracting complete growth and development of a investment and resulting in sustainable country: one, by bringing numerous economic development for the people of Kerala. An value & benefits; and, second, helping in build equable climate, a long shoreline with serene country's brand value, image & identity. beaches, tranquil stretches of emerald Tourism industry goes beyond attractive backwaters, lush green hill stations and exotic destinations, to being an important economic wildlife, waterfalls, sprawling plantations and growth contributor. Kerala has a noticeable paddy fields, Ayurvedic health holidays, role in the world tourism map and the enchanting art forms, magical festivals, opportunities are opened wider. Kerala historic and cultural monuments, and exotic Tourism is having a global presence and with cuisines, make Kerala a unique experience. its clear strategy for growth and sheer marketing activities, it has gained a lot of

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Objective Results and discussions

To understand the trends and growth of Table 1.1 TOURIST ARRIVALS 2007 – tourism industry of Kerala for the last ten 2018 Tourist arrival in Kerala for the last 12 years years.

Methodology No. of No. of Domestic % Foreign % Total no. % Yea Tourist Incre Tourist Incre of Incre The study is based on secondary data. r Visits ase Visits ase tourists ase 2007 6642941 5.92 515808 20.37 7158749 6.84 The data is collected from the annual 2008 7591250 14.28 598929 16.11 8190179 14.41 publication of Department of Tourism, Kerala. 2009 7913537 4.25 557258 -6.96 8470795 3.43 2010 8595075 8.61 659265 18.31 9254340 9.25 2011 9381455 9.15 732985 11.18 10114440 9.29 2012 10076854 7.41 793696 8.28 10870550 7.48 2013 10857811 7.75 858143 8.12 11715954 7.78 2014 11695411 7.71 923366 7.6 12618777 7.71 2015 12465571 6.59 977479 5.86 13443050 6.53 2016 13172535 5.67 1038419 6.23 14210954 5.71 2017 14673520 11.39 1091870 5.15 15765390 10.94 2018 15604661 6.35 1096407 0.42 16701068 5.94 Source tourism statistics 2018, Department of Tourism, Gvt of Kerala

Figure 1.1 TOURIST ARRIVALS 2007 – 2018 Tourist arrival in Kerala for the last 12 years

Toursist Arrivals in Kerala (2007-2018) 18000000 16000000 y = 84834x - 2E+09 R² = 0.989 14000000 12000000 y = 79093x - 2E+09 10000000 R² = 0.988 8000000 No. of Domestic Tourist Visits

Axis Title Axis No. of Foreign Tourist Visits 6000000 Total no. of tourists 4000000 y = 57405x - 1E+08 R² = 0.982 2000000 0 2006 2008 2010 2012 2014 2016 2018 2020 Axis Title

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

It is clear from the table; the number of The total number of tourist arrivals increased domestic tourist arrivals in Kerala in 2007 is from 7158749 in 2007 to 16701068 in 2018. It 6642941 which show 5.92 per cent of increase seems to be a linear trend with an annual linear from that of 2006. Number of foreign tourist growth of 84834 tourists. The R2 value is .989 arrivals in 2007 shows 20.37 percent increase which gives a good explanation to the model. from that of 2006. In 2008 there is a notable The number of foreign tourist arrivals increase in tourist arrivals of both domestic increased from 515808 in 2007 to 1096407 and foreign 14.28 per cent and 16.11 per cent in 2018. It seems to be a linear trend with an respectively. Domestic tourist arrivals are annual linear growth of 57405 tourists. The greater than the number of foreign tourists R2 value is .982 which gives a good visit. It is evident from the table that each year explanation to the model. The number of shows an increase in the number of domestic domestic tourist arrivals increased from and foreign tourists visiting the state of Kerala. 6642941 in 2007 to 15604661 in 2018. It In 2017, domestic tourist arrival is 11.39 per seems to be a linear trend with an annual linear cent and the foreign tourist arrivals are 5.15 growth of 79093 tourists. The R2 value is .988 per cent more than the year 2016. For the latest which gives a good explanation to the model. year 2018, domestic tourist arrivals are 6.35 per cent and .42 per cent increase in foreign tourist arrivals compared to 2017. TABLE 1.2 - FOREIGN TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 & 2018

FOREIGN 2014 2015 2016 2017 2018 I – Quarter 340193 363492 384719 393038 440694

II – Quarter 142641 151774 153461 175746 167666

III – Quarter 172731 184005 200335 200988 173758

IV – Quarter 267801 278208 299904 322098 314289

Total 923366 977479 1038419 1091870 109 Source tourism statistics 2018, Department of Tourism, Gvt of Kerala

Foreign tourist arrivals generate foreign quarter of the year 2018, constituting 40.19% exchange earnings of India.Kerala Tourism with 440694 tourists, followed by 4th quarter aiming to change Kerala into a 365 days constituting 28.67% with 314289 tourists, the tourist destination. During 2018, the maximum 3rd quarter constituting 15.85% with 173758 number of foreign tourists arrived in January tourists, and the 2nd quarter constituting followed by February. The maximum number 15.29% with 167666 tourists. of foreign tourists arrived during the 1st

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Figure- 1.2 FOREIGN TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 & 2018

1200000

1000000

800000 Series1 600000 Series2

400000 Series3 Series4 200000

0 FOREIGN I – Quarter II – Quarter III – Quarter IV – Quarter Total

Table 1.3 DOMESTIC TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 & 2018

DOMESTIC 2014 2015 2016 2017 2018 I – Quarter 2685048 2878897 3043809 3270514 3877712

II – Quarter 2776042 2976682 3110808 3578943 4149122

III – Quarter 2647557 2861813 3086508 3410654 3292016

IV – Quarter 3586764 3748179 3931410 4413409 4285811

Total 11695411 12465571 13172535 14673520 15604661

Source tourism statistics 2018, Department of Tourism, Gvt of Kerala Figure 1.3 DOMESTIC TOURISTS QUARTERLY COMPARISON – 2014, 2015, 2016, 2017 & 2018

Total IV – Quarter Series5 III – Quarter Series4 II – Quarter Series3 I – Quarter Series2 DOMESTIC Series1 0 5000000 10000000 15000000 20000000

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

From the table it is clear that during the year Figure 1.4 FOREIGN EXCHANGE 2014, domestic tourist arrival is higher in the EARNINGS FROM TOURISM FOR LAST 4th quarter. In 2015, 2016, 2017 and 2018 12 YEARS (` In Crores) show the same trend of increase for the 4th quarter which consists of October November and December. Foreign Exchange Earnings from For the year 2014 domestic tourist arrival is Tourists from 2007 to 2018 lower in the third quarter; in 2015 also the 10000 minimum is in the third quarter; for the year 9000 2016 and 2017 it is in the first quarter and for 8000 the year 2018 lowest domestic tourist arrival is y = 604.3x + 1485. 7000 in the third quarter. R² = 0.978 During 2018, the maximum number of 6000 domestic tourists arrived during the 4th quarter 5000 constituting 27.46% with 4285811 tourists Title Axis 4000 followed by 2nd quarter constituting 26.59 % 3000 with 4149122 tourists, the 1st quarter 2000 constituting 24.85% with 3877712 tourists and 1000 the 3rd quarter constituting 21.10% with 0 3292016 tourists. 0 5 10 15 Axis Title Table 1.4 FOREIGN EXCHANGE EARNINGS FROM TOURISM FOR LAST 12 YEARS (` In Crores) From the graphical inference, it is clear that Year Earnings % of variation over the amount of foreign exchange earnings previous year increases from 2640.94 crore in 2007 to 8764.46 crore in 2018. It seems to be a linear 2007 2640.94 32.82 trend with an annual linear growth of 604.3 2008 3066.52 16.11 crore. The R2 value is .978 which gives a good 2009 2853.16 -6.96 explanation to the model. 2010 3797.37 33.09 2011 4221.99 11.18 Foreign exchange earnings from tourism have 2012 4571.69 8.28 shown a steady growth over the years. In 2018, 2013 5560.77 21.63 Kerala has earned ` 8764.46 crores as foreign 2014 6398.93 15.07 exchange earnings from tourism against ` 2015 6949.88 8.61 8392.11 crores in the year 2017 showing a 2016 7749.51 11.51 growth of 4.44%. Table 3.7 and Graph 3.7 2017 8392.11 8.29 2018 8764.46 4.44 shows the estimates of earnings from foreign Source tourism statistics 2018, Department of Tourism, Gvt of Kerala tourists in the last ten years.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Table 1.5 MONTH WISE ARRIVAL DETAILS OF FOREIGN TOURISTS

Month 2012 2013 2014 2015 2016 2017 2018 % of variation January 106314 113627 119865 130463 136539 150808 167980 11.39 February 103220 115403 127153 132873 141143 135089 152003 12.52 March 75544 85953 93175 100156 107037 107141 120711 12.67 April 61335 66371 72441 76734 78099 82633 85493 3.46 May 30470 32600 36302 39583 37994 49073 45427 -7.43 June 28280 29758 33898 35457 37368 44040 36746 -16.56

July 42977 45786 48577 51722 56666 72552 68868 -5.08 August 59904 64518 69909 74710 81070 73736 60121 -18.46 Sep 47440 51032 54245 57573 62599 54700 44769 -18.16 Oct 63690 67702 71598 76119 82551 79957 73263 -8.37 Nov 78833 83484 87720 89883 96155 107028 99271 -7.25 Dec 95689 101909 108483 112206 121198 135113 141755 4.92 Total 7,93,696 8,58,143 9,23,366 9,77,479 10,38,419 10,91,870 10,96,407 0.42 Source: tourism statistics 2018, Department of Tourism, Gvt of Kerala

Figure 1.5 MONTH WISE ARRIVAL development of the State. Kerala is DETAILS OF FOREIGN TOURISTS showing an increasing trend in foreign tourist arrivals during the last few years. 1200000 According to the statistics for calendar year 1000000 2018, 0.42% growth in foreign tourist 800000 arrivals and 6.35% growth in domestic tourist arrivals was registered Inspite of the 600000 great flood of 2018. We could back the 400000 negative trend of tourist arrivals growth in 200000 just 4 months with sustained innovative 0 tourism promotion activities post-floods. -200000 During 2018, the foreign exchange earnings from tourism in the State were `8764.46 crores, which shows an increase of 4.44% Conclusion over the last year. The total revenue Kerala generated from tourism in the year 2018 is Kerala Tourism attracting international and worked out as `36258.01 crores. domestic tourists plays a significant role in the economy of the State by contributing to Reference 10% of the GDP and providing employment 1. „Kerala Tourism Statistics 2018‟, to 1.5 million people in the State. With its prepared by the Research and Statistics potential in creating employment and division of the Department of Tourism, enhancing production and productivity, 2. https://www.keralatourism.org/tourismst Kerala Tourism contributes to the atistics/tourist_statistics_2018_book201 91211065455.pdf

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

A CROSS-SECTIONAL ANALYSIS ON THE INFLUENCE OF VARIOUS COSTS OF AQUACULTURE ACTIVITIES ON REVENUE FROM AQUACULTURE

Dr. K. PRADEEP KUMAR Associate Professor Government College, Attingal

Kollam, Ernakulam and of Kerala, INTRODUCTION seelcted at random. The collection of quantitative

Commercial aquaculture refers to fish farming data regarding revenue details was highly operations, whose goal is to maximise profits, complicated due to the lack of availability of where profits are defined as revenue minus costs. records. The data regarding cost and revenue have Aquaculture is an economic activity that can been collected in different stages of aquaculture generate better returns to the farmers. Scientific practice from stocking to harvesting. Cross- aquaculture ensures better business with higher evaluation has been done to ensure the reliability growth opportunities. The role of aquaculture in of the quantitative data. producing high-grade animal protein for human TOOLS FOR DATA ANALYSIS consumption is widely known. An economic analysis into various aquaculture practices will be This study explores the extent of useful in understanding the viability of this variations in revenue due to the increased practice. This paper attempts to analyse the activities related to aquaculture. Regression influence of various costs of aquaculture on anlaysis is used to analyse the influence of revenue from aquaculture. various cost of aquaculture activities on revenue from aquaculture. Ordinary Least Squares (OLS) OBJECTIVE modelling using Gretl software is attempted to 1. To analyse the influence of various attain the objective of the study.

costs of aquaculture on revenue from THE VARIABLES USED aquaculture. The various aquaculture activities are METHODOLOGY quantified through Labour involved, Seeds stocked, Feeds used, and Fertiliser application. Since the study is based on the sample Also, there are extraneous features associated survey, the data has been collected from 300 fish with aquaculture included in the category of' and crustacean farmers located in the districts of „other expenses‟ like insurance premium, fuel

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019 and electricity, lease rentals, interest on loan Feed: Regular feeding ensures rapid growth of taken, etc. A small description of features species across the culture period. Some farmers specific to aquaculture is very useful in practising modified traditional culture depend understanding subsequent analysis. only on wild feeds to ensure species growth. But scientific aquaculture calls for regular feeding Labour: Labour is essential to aquaculture from using nutritional feeds available through stocking to harvesting. Small-sized farms use governmental agencies like Matsyafed and feeds family labour in all stages. Large-sized farms manufactured by corporate organisations. There depend on hired labour for aquaculture is a wide variation in the use of feeds by the activities. Regular labour is required for farmers. Thus, feeding is another activity activities like stocking, feeding, sampling, etc. considered here for measuring its impact on Likewise, at the time of harvesting also, hired revenue. labour is used in many farms. The intensity of culture in farms is also measured in terms of Fertilisers: Fertilisation of pond is important human involvement. In most of the farms in for natural growth of planktons in the water Kerala, farmers are following extensive to semi- body. Planktons are the natural feeds for the intensive culture. For extensive culture, more species cultured. Natural fertilisers like cow labour is required, while, in semi-intensive dung, coconut husks, etc., are used by farmers culture, labour involvement is comparatively practising aquaculture in Kerala. Some farmers less. Thus, labour cost is the most important even use manufactured fertilisers in the ponds to element of cost incurred for generating revenue ensure plankton growth. The use of fertilisers from aquaculture. helps the farmers to reduce the feed cost, as natural feed is available in the pond after Seed: The seed of the species to be cultured is fertilisation. important in practising aquaculture. Farmers depend on wild seeds and/or hatchery produced Other Expenses: Other expenses involved in seeds for farming. Now, due to a fall in natural aquaculture activities include fuel and power, availability of wild seeds, the farmers basically lease rentals, if any, insurance premium, interest depend on hatchery-produced seeds. The quality on the loan taken for practising aquaculture, of the seeds is very important for success in transportation cost, etc. The cost of these aquaculture. The governmental agencies, expenses varies in different farms. Thus, the through their own hatcheries, provide seeds at total of these costs, titled as other expenses, is subsidised rates to the farmers, to encourage considered for the purpose of anlysis to measure aquaculture in Kerala. So the second major cost its impact on revenue from aquaculture. incurred for aquaculture is the seed cost.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

RESULTS AND DISCUSSION Labour −0.146181 0.107945 −1.354 0.1779 Seed 0.143723 0.0585259 2.456 0.0153 ** It may be noted that in a particular case, it Feed 0.364909 0.0642427 5.680 <0.0001 *** Fertiliser −0.105608 0.0868359 −1.216 0.2260 is not necessary for all these factors (cost of OtherExpenses 0.250893 0.0444776 5.641 <0.0001 *** aquaculture activities) to exist together. From Source: Primary Data the data on these variables, it may be noted that From the primary model it is inferred these activities vary widely and only 142 cases that the variables labour and fertiliser with are reporting all the factors stated above. negative coefficients are found to be insignificant in predicting the dependent Table 1 Descriptive Statistics variable revenue. All other variables like Variables N seed, feed and other expenses are found to Revenue 300 Labour 300 be significant with positive coefficients Seed 300 showing the marginal effect of each variable Feed 249 on revenue from aquaculture. The Fertiliser 242 specification of the model is proved through Other expenses 200 the following table Valid N (list wise) 142 Source: Primary Data Table 3: Model Specification Summary Mean dependent 11.79056 S.D. dependent Ordinary Least Squares for Cross- var var 0.761716 sectional Data (Using Gretl Software) Sum squared 41.61905 S.E. of regression resid 0.553193 R-squared 0.491270 Adjusted R- To analyse the influence of various costs of squared 0.472567 F(5, 136) 26.26651 P-value(F) 1.68e-18 aquaculture to revenue from aquaculture a Log-likelihood −114.3532 Akaike criterion 240.7063 model of Ordinary Least Squares is fitted for Schwarz criterion 258.4413 Hannan-Quinn the valid cases using Gretl software for cross 247.9131 Source: Primary Data sectional data collected from sample. The The R2 and adjusted R2 are found to be results after analysis are given below. 49.127 and 47.2567 respectively. The F Model 6: OLS, using observations 1-300 (n value for the model is found as 26.26651 = 142) with a p value less than .001. Thus the Missing or incomplete observations dropped: 158 model suggests that the explanatory Dependent variable: Revenue variables are sufficiently explaining the Table 2. Regression Coefficients and its variance in total revenue. Ramsey RESET signifiance gives the following results Coefficient Std. Error t-ratio p-value const 7.65741 0.655990 11.67 <0.0001 ***

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

RESET test for specification - Reference Null hypothesis: specification is adequate Test statistic: F(2, 134) = 2.57791 1. Field, Andy, Discovering Statistics with p-value = P(F(2, 134) > 2.57791) = using SPSS, SAGE Publications, 0.0796949 2009 pp 187-263 Source: Primary Data From the RESET test for specification, the 2. Raju M.S., Economic Analysis of Different Aquaculture Systems in null hypothesis is accepted since the Kerala – A Production Function significance value is greater than 0.05. Thus Approach,Unpublished Ph.D thesis, Cochin University of Science and the fitted model‟s specification is seems to Technology (CUSAT),1997. be adequate. 3. Pradeep Kumar K., Production and Marketing of Aquaculture Products CONCLUSION in Kerala, Ph.D thesis published by Abhijeet Publications, New Delhi,2014 The special feature of aquaculture that discriminates itself from capture fisheries is that harvesting can be organised according to market demand in terms of quantity, size, etc. To explore the influence of various activities on revenue from aquaculture, OLS model using Gretl software was developed for the five important cost variables. The models provide significant R2 value in all cases with positive coefficients. From the regression coefficients the three most important variables determining revenue are identified as Seed, Feed and other expenses.

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

LIST OF PARTICIPANTS

Sl. No. Name, Designation and Institutional Address 1 Dr. LAKSHMANAN M.P Assistant Professor of Commerce Government College CHITTOOR Phone No. 9249214643 2 Dr. MOHANADASAN T Assistant Professor of Commerce Victoria Government College Palakkad Phone No.9249758697 3 Dr. JAYARAJU V. Associate Professor of Commerce Iqbal College, Phone No. 9447958248 4 AJEESH A. Assistant Professor of Commerce G.P.M. Government College Manjeswaram Phone No.9422441007 5 LEKSHMI PRAKASH Assistant Professor of Commerce G.P.M. Government College Manjeswarm 6 VIJAYAN K Assistant Professor of Commerce Government College, Nedumangad Phone No. 9496101019 7 BINOY S. Assistant Professor of Commerce S.N. College, Phone No. 9846151302 8 SIMU RAJENDRAN Assistant Professor of Commerce S.N. College, Kollam Phone No 9074491741 9 Dr. SUMESH G.S Assistant Professor of Commerce M.G. College, Thiruvananthapuram Phone No.9447855544 10 Dr. PRADEEP KUMAR N. Assistant Professor of Commerce M.G. College, Thiruvananthapuram Phone No. 9847888777 11 Dr. SREEDEVI S.R. Assistant Professor of Commerce Government Arts College Thiruvananthapuram

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Sl.No Name, Designation and Institutional Address 12 Dr. LEKSHMI P Assistant Professor of Commerce V.T.M. N.S.S. College 13 REJANI R. NAIR Assistant Professor of Commerce Government College Nedumangad 14 INDURAJANI R Assistant Professor of Commerce Government College Nedumangad 15 SARITHA G.S. Assistant Professor of Commerce N.S.S. College, Niamel 16 Dr. ASWATHY P. Assistant Professor of Commerce N.S.S. College, Neeramankara 17 Dr. S. KRISHNAVENI, Assistant Professor of Commerce Government College for Women Thiruvananthapuram 18 Dr. KALARANI T.G Assistant Professor of Commerce V.T.M. N.S.S. College Dhanuvachapuram 19 SALINI R.S Assistant Professor of Commerce UIT, 20 ANEETA VICTOR Assistant Professor of Commerce UIT, Vakkom 21 ARCHANA S Assistant Professor of Commerce TKM Institute of Management, Kollam 22 SUMAN S. Research Scholar Department of Commerce, University of Kerala

23 DEVI KRISHNA Research Scholar S.N.College, Kollam 24 SEREENA A Research Scholar M.G. College Thiruvananthapuram

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Sl.No Name, Designation and Institutional Address 25 ANCY JOHN Research Scholar M.G. College Thiruvananthapuram 26 MAYA BABU B.P Research Scholar M.G. College Thiruvananthapuram 27 ATHENA PRINCE Research Scholar M.G. College Thiruvananthapuram 28 SHIYAS I.S Research Scholar IMG, Thiruvananthapuram 29 REJITHA Y.S Research Scholar KSMDB College 30 ANU G.S Research Scholar University of Kerala 31 ILYAS P.C Research Scholar Department of Commerce University of Kerala 32 ANN MARY ALEXANDER Research Scholar Department of Commerce University of Kerala 33 VISHNU S. KUMAR Research Scholar Department of Commerce University of Kerala 34 RAHUL R. KURUP Research Scholar Department of Commerce University of Kerala 35 SRUTHI S.G Research Scholar Department of Commerce University of Kerala

36 ASEEM R. Research Scholar Govt. Arts College, Thiruvananthapuram 37 IRSHAD V. Research Scholar S.N. College, Kollam

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Sl.No Name, Designation and Institutional Address 38 VINITHA V.K Research Scholar M.G. College Thiruvananthapuram 39 SREEJI S.L Research Scholar Institute of Management in Government (IMG) Thiruvananthapuram 40 AFRA NAHAN M.T Research Scholar Department of Commerce and Management Studies University of Calicut 41 NAFEESATHUL THANSILA BEEVI Research Scholar Department of Commerce and Management Studies University of Calicut 42 RESMI R. Research Scholar M.G. College, Thiruvananthapuram 43 JINU L Research Scholar Institute of Management in Government (IMG) Thiruvananthapuram 44 GOPISH G. M.Phil. Scholar Department of Commerce University of Kerala 45 ANN MARY VARGHESE M.Phil. Scholar University College, Thiruvananthapuram

LIST OF PARTICIPANTS FROM GOVERNMENT COLLEGE, ATTINGAL Sl. No Name, Designation and Institution 46 SUNIL S, Head of the Department Assistant Professor of Commerce Government College, Attingal 47 Dr. K.PRADEEP KUMAR (Coordinator) Associate Professor of Commerce Government College, Attingal 48 Dr. SUNILRAJ N.V (Co-coordinator) Assistant Professor of Commerce Government College Attingal

Government College, Attingal

Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Name, Designation and Institutional Address 49 Dr. ANITHA S Assistnat Professor of Commerce Government College, Attingal 50 Dr. SAJEEV H Assistant Professor of Commerce Government College, Attingal 51 Dr. SARUN S.G Assistant Professor Government College, Attingal 52 MANIKANTAN G. Assistant Professor Government College, Attingal 53 SHANIMON S Assistant Professor Government College, Attingal 54 Dr. BINU R Assistant Professor of Commerce, Government College, Attingal 55 KRIPA M DAS Research Scholar Department of Commerce, Govt.College, Attingal 56 PRAGEETH P Research Scholar Department of Commerce, Govt. College, Attingal 57 ANSA S Research Scholar Department of Commerce, Govt. College, Attingal 58 KAVITHA S Research Scholar Department of Commerce, Govt. College, Attingal 59 JENCY S Research Scholar Department of Commerce, Govt. College, Attingal

60 ABHIRAMI S.R M.Com III Semester Govt. College, Attingal 61 AKHIL A.U. M.Com III Semester Govt. College, Attingal

62 AKHIL S.K M.Com III Semester Govt. College, Attingal

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

63 ANJALI RAJAN M.R. M.Com III Semester Govt. College, Attingal 64 ANOOP R. NAIR M.Com III Semester Govt. College, Attingal 65 ARYA KRISHNANA R.S M.Com III Semester Govt. College, Attingal 66 ATHIRA P.R M.Com III Semester Govt. College, Attingal 67 FATHIMA M. ASHRAF M.Com III Semester Govt. College, Attingal 68 KARTHIKA S.R. M.Com III Semester Govt. College, Attingal 69 MAHITHA M.S. M.Com III Semester Govt. College, Attingal 70 SANDHYA S. M.Com III Semester Govt. College, Attingal

71 SHEFNA S. M.Com III Semester Govt. College, Attingal

72 SREEJA M.N M.Com III Semester Govt. College, Attingal 73 SUBIMOL B.S M.Com III Semester Govt. College, Attingal 74 SWATHY K.S M.Com III Semester Govt. College, Attingal

75 VAISHNAVI V.S M.Com III Semester Govt. College, Attingal

76 G.S. SACHIN M.Com III Semester Govt. College, Attingal 77 GREESHMA G.P M.Com III Semester Govt. College, Attingal

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

78 ABHILA M. DAS M.Com I Semester Govt. College, Attingal

79 AMRITHA G.M. M.Com I Semester Govt. College, Attingal

80 ARYA A.J. M.Com I Semester Govt. College, Attingal

81 ARYA ASHOK M.Com I Semester Govt. College, Attingal

82 ASHA B M.Com I Semester Govt. College, Attingal

83 BHAVYA VIJAYAN M.Com I Semester Govt. College, Attingal

84 FATHIMA KALAM M.Com I Semester Govt. College, Attingal

85 FATHIMA S. M.Com I Semester Govt. College, Attingal

86 SHANI B. M.Com I Semester Govt. College, Attingal

87 SILPAMOL P.M M.Com I Semester Govt. College, Attingal

88 SNEHA K.N M.Com I Semester Govt. College, Attingal

89 THASNIM S. M.Com I Semester Govt. College, Attingal

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Proceedings of Five day Workshop on Financial Econometrics from 15th to 19th October, 2019

Research and Post Graduate Department of Commerce

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Government College, Attingal