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Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries

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Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-2, December 2019 Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries R.Sivarethinamohan, S.Sujatha Abstract; In general, stock market indices are widely Over the global MIST stock market are becoming integrated interrelated to the other global markets to detect the impact of and conform to the international standards.Cramming the diversification opportunities. The present research paper bondage of Indian stock market and other global empirically examines randomness and long term equilibrium stockmarkets, especiallyMIST county stock markets are a affiliation amongst the emerging stock market of India and vital one, because it will provide considerable motivation Mexico, Indonesia, South Korea and Turkey from the monthly time series data during February 2008 to October 2019. The for the Indian investors to look for the new investment researcher employs by the way, Run test, Pearson’s correlation opening across the globe for diversifying their portfolio for test, Johnsen’s multivariate cointegration test, VECM and getting higher returns. For understating of the volatility Granger causality test with reference to post-September 2008 estimation and forecasting, GARCH, EGARCH and Global financial crisis. The test results of the above finds that TARCH models are employed to estimate time-varying Nifty 50 and BSE Sensex is significantly cointegrated either correlation and leverage effects.In addition, the evidence of among themselves or with MIST countries particularly during cointegration and causality will enable to estimate long term the post-September Global financial crisis. No random walk is parameters or equilibrium with unit root variables and to found during the study period. The ADF (Augmented Dickey- check the direction of causality. Hence it is Fuller) and PP (Phillips Pearson) tests evidenced stationarity at the level, but converted into non-stationarity in first difference. necessitatestudying the rapport between MIST country‟s Symmetric and asymmetric volatility behaviors are studied using emerging stock markets and Indian stock markets to GARCH, EGARCH and TARCH models in order to test which understand the mechanism of comovement, causality, model has the best forecasting ability. Leverage effect was randomness and volatility. apparent during the study period. So the influx of bad news has a bigger shock or blow on the conditional variance than the influx II. THEORETICAL FRAMEWORK OF THE STUDY of good news. The residual diagnostic test (Correlogram-Squared residuals test, ARCH LM test and Jarque-Bera test) confirms A number of studies reviewed the previous literature to GARCH (1,1) as the best suited model for estimating volatility know how the proposed research is related to prior research andforecasting stock market index. and identify new ways to interpret prior research. It permits Key terms: equilibrium, leverage effects, volatility clustering, us to detect volatility and comovement amongstock markets asymmetric effect, cointegration, causality, random walk, wherever cointegration occurs and when if the stock market persistence rise with big swings in either direction. Arshanapalli, Doukas and Lang (1995) investigated a 1. INTRODUCTION feasible associationof the US and six most emerging Asian Mexico, Indonesia, South Korea and Turkey are coined as stock markets during pre and post period of October 1987. MIST acronym. They are gaining massive attention among The Asian equity markets were not as much of integrated the investment community at the present time because they with the Japanese stock market at that time they were with have a bit more upside potential in terms of favorable the US market. Hassan and Naka (1996) also ensuredwell demographics and rising productivity, sound fiscal policies, ahead the comovement of the stock market indices of US, sustainable balance of payments and a contributing financial Japan, UK and German inshortrun and long run.Masih and 1 sector however the total GDP is less than of the BRICS Masih (2001) devoted and attention to study the underlying 3 causal relationships of international stock markets using .i.e., $3.9 trillion, compared with $14 trillion. So the engine VAR and Vector Error Correction models (VECM). In their of MIST economies is currently sputtering. MXX are some subsequentresearch work (Masih and Masih, 2002), they of the next notable emerging markets in the MIST countries. examineand review the degree of globalization using the They rise up the global economic ladder and become a much similarmethod. more attractive destination for foreign investors, breaking a Naeem (2002) employed the Johansen methodology new record in the capital market after global financial crisis fortesting non stationary time series and examiningthe September 2008. correlated or similar movement of daily equity prices among Pakistan, India and Sri Lanka positioned in South Asia. He failed to proof of the long-run relationship during the study Revised Manuscript Received on December 05, 2019. * Correspondence Author period of 1994-1999. Dr. R. Sivarethinamohan, Chist Deemed To Be University, Bangalore (Karnataka), India. E-mail: [email protected] Dr. S. Sujatha, K. Ramakrishnan College of Technology, Tiruchirappalli (Tamil Nadu), India. E-mail: [email protected] Published By: Retrieval Number: A4946119119/2019©BEIESP Blue Eyes Intelligence Engineering DOI: 10.35940/ijitee.A4946.129219 1402 & Sciences Publication Econometric Modelling: Testing of Randomness,Volatility, Casualty and Cointegration of Emerging Stock Market Indices of India and MIST Countries Darrat and Benkato (2003) studied symmetric and autoregressive conditional heteroskedasticity test based on asymmetric volatilitybehaviorof the emerging stock markets standardized residuals from a fitted GARCH model. No of the US, the UK, Japan and Germany and ISE (Istanbul asymmetric effects were found in the dynamic volatility of stock Exchange).The two matured markets of the US and the Iranian stock market. the UK under their studyaccepted substantial commitmentto Gupta and Guidi (2012) valued the time-varying conditional the solidity and financial health of not bigger emerging associationamong the stock markets of India, Hong Kong, markets like theISE. Japan and Singapore. No strong long-runrelationship was The study of Mukherjee and Mishra (2005) exposed the found from the study.Darrat, Li and Wu (2012) investigated notion of integration of the Indian stock market with the the weekly, monthly and quarterly risk-return relationship evolving Asian markets of Indonesia, Malaysia, Philippines, during the period of 1973 to 2011. Three different Korea and Thailand. Yet, Bose and Mukherjee (2006) might generalized autoregressive conditional heteroskedasticity perhaps not find out the cointegration of the Indian stock (GARCH) models were employed besides a plain vanilla market and estimates of all cointegrating vectors with time time-series approach. The results failed to support a series data of Japan, Hong Kong, Malaysia, South Korea, significantly specifications. Emenike and Aleke(2012) Singapore, Taiwan and Thailand and the USA. Although examine daily closing prices of the Nigerian stock they used the Johansen cointegration test for the Asian exchanges (NSE) with the intention to knowing the group of countries, including India and excluding India. response of volatility to negative and positive news. When India was included in the data set, cointegration Asymmetric effects were explored in the NSE stock returns, equation vector (1) was found but then again India was but with an absence of leverage effect.As per our literature excluded, no cointegration was found. It evidences related to past research cointegration test is acknowledged distinctive role of Indian stock in the integration of Asian as an acceptable and suitable method for analysing markets. comovement. Symmetric and asymmetric model like Li (2007) investigated the associations of two developing GARCH, EGARCH and TARCH are considered as the best stock exchanges in mainland China along with the method for finding stock market volatility. In addition to established markets in Hong Kong and in the US. The this unit root test and run test are often used for finding results of a multivariate GARCH approachconfirm no sign random walk. Therefore, the present study is also interesting of a progressive relationship between the stock markets in to test randomness with run test, volatility with ARCH mainland China and the US market, then againmake certain family models and cointegration with Johansen the indication of uni-directional volatility spillovers from the Cointegration analysis, normalized cointegrating coefficient, stock markets in Hong Kong to those in Shanghai and VECM model and Pair-wise Granger Causality Tests in Shenzhen.Thus,overseas investors of the Chinese market addition to Karl Pearson‟s correlation analysis will benefit from the reduction of diversifiablerisk. More of late, the study of Cerny and Koblas (2008)gavea detail III. DATA AND METHODOLOGY report of stock market cointegration whose linear combination is stationary and the speed of information BSE Sensex, Nifty 50, IPC MEXICO (^MXX), Jakarta transmission. They matched Polish, Hungarian, and Czech Composite Index (^JKSE) Indonesia (JCI), KOSPI 200 stock market indices with those from developed markets.
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