Journal of Xi'an University of Architecture & Technology Issn No : 1006-7930

US Dollar, Gold and NSE Nifty 50 Interdependencies: Evidence from Dr. Sarika Lohana Post-Doctoral Research Fellow, State Bank Institute of Innovation and Technology, Hyderabad. Email: [email protected]

Abstract:

The purpose of this paper is to analyze the interdependencies among US Dollar (USD) Exchange rates, Gold prices and NSE Nifty 50 prices in Indian context on volatility measures. Forex market determines the relative values of different currencies; It is backbone of international trade practices and supports currency conversion to mobilize the same, hence its volatility is totally based on the international trade practices. USD being the dominating currency, to check its influence on Gold Prices and NSE Nifty 50 and vice versa and to estimates their volatility measures from last 10 years extracts of NSE Nifty50 Index Prices, USD exchange rates, and Gold prices i.e. 1st Jan 2007 to 31st Dec 2016. In the study tools used were ADF test to check the stationarity of time series data, cointegration and Granger – Causality test for VAR to test the hypothesis. The Granger causality test confirms the NSE Nifty 50 prices and Gold prices are not causal in bidirectional ways, NSE Nifty 50 prices was observed casual with USD exchange rates. USD exchange rates was found no influence on NSE Nifty 50 prices, USD exchange rates and Gold prices are causal in bidirectional ways.

Keywords: NSE-Nifty50, Volatility, Gold Prices, USD Exchange Rates, Ganger Causality, VAR, ADF test, Forex market.

JEL Classification: C32, F31, G10, G11

I. INTRODUCTION

The stock market being part and parcel of the capital market has an extraordinary role for encouraging deposits or investments in dissimilar sectors of the economy. In fact, stock market being the major source of finance in long run, its conditions and volatility is immensely reflection of sectorial performance of the economy. One of the foremost tasks of these marketplaces is to provide reasonable stock values then accelerate the trades to generate flow of funds in the market. This enables investment and liquidity in long term projects especially in private sector. There is need of brief study specific stock fairs which stay not merely influences of domestic economy but also world economy. Late in the year 1930 the great depression had been commenced in New York (NYSE), furthermore in the year 1997 crunch of southeastern of Asia affected global economy included Iran and ended with deteriorated in demand for crude oil plus sharp cutoff in its rate. Global Economic Crisis in the year 2008 initiated in July 2007 created credit crux, US investors lost their confidence and investment due to reducing in the value of sub-prime mortgages that results in liquidity crisis in all the major economics. Sri Ram. P (2017) pointed out many pivotal issues

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in global context and research are witnessed focusing on the inter connection between stock market prices and USD exchange rate. Ang and Chen (2002) contend that volatility remains not an issue which is derived from the market dependence in a crisis period which is a correlation is asymmetric for up-markets and down-markets of the economy. As stated overhead close is a significant relation flanked by stock exchange instability and an economy sequence of any nation. Certain discussions which address on these platforms are whether stock markets prices experiences impact of foreign exchange rate risk. The traditional Capital Asset Pricing Model (CAPM) talks about the USD exchange rate risk presence a firm-centric also henceforth it is unsystematic risk that can be diversifiable in addition that is which is not been derived from the market. This implies for a business's currency exposure management judgments which cannot be neglected. It shows the presence of USD exchange rate threat and its connection with stock flea market volatility as implication International Asset Pricing Model (IAPM). IAPM is a leeway of the customary CAPM in a multi- country situation with a presupposition of cohesive capital structure combination intended for multinational companies, cannot have optimize capital structure or capital budgeting without analyzing dollar exchange rate threat and the situation of covariance i.e. international market capital structure or diversified portfolio. The risk-return tradeoff of international diversified capital budgeting and managing of multi-currency equity portfolios highlights the importance of by what means USD exchange rate risk and stock market rate risk interconnected through each other. With a significant rise in cross-border equity investments, Foreign Direct Investments and Foreign Institutions Investors, etc. In emerging markets; this includes proper study of currency exposure risk and very tough for fund managers to analyze the risk level associate with it. The asset market tactic to dollar exchange rate risk fortitude Branson (1983) and Frankel (1983) discussed on the symmetry conversion rate of a currency due to the interaction of the demand aimed at and supply of financial assets such for instance stocks and bonds denominated in that particular coinage. The demand aimed at these assets would noticeably be determined by other factors too; vary from domestic to foreign investors for different time zones. Modern portfolio theory of Markowitz (1952) and Black-Scholes pricing model (1973) discuss on the relationship of volatility and expected returns with an assumption of constant volatility for a specific period of time. Bollerslev et al (1992) studied volatilities is not perpetual so far away and the conditional simulations of ARCH and GARCH should be cast-off in the case the volatilities are neither accurate and nor dependable. Factors affecting the volatility are external in nature and also affect the firm somewhat. Majority are political besides economic factors, further economic factors separated into two elements one is real and another is monetary. Arbitrage Pricing Theory (APT) reflects the actual return of securities measured based on economic factor and this allows one efficient factor as a contrary to CAPM. In a portfolio management, every stock has a specific risk and error rapports of particular stocks remain not so significant. These situations show variables risk is a crucial and systematic jeopardy in nature i.e. unavoidable meanwhile a non-systematic risk that can be diversifiable. It can be considerable that USD exchange rates and its volatility are two secondary variables which disturb the stock market. Because USD exchange rates and its volatility are beyond firms’ superior and operation authorities according to the sharp model, they are the modest exterior macroeconomic factors. Gold prices are also found as one of the most essential dissimilarities in global monetary and finance. Although the role of gold prices is diminished from time to

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time but it cannot be neglected as, being one of the key elements in international portfolio. Hence, for policy-maker impact of gold prices on the economy and vice versa cannot be left untouched. Ascent of gold price would engross part of the stock market fluidity or volatility. II REVIEW OF LITERATURE Many researchers have addressed the question in their research about the relation among the prices of stock market, USD exchange rates variations and gold prices influence. 2.1 Related to study Geete (2016) studied the Nifty, Dollar rates and Gold prices for weekly data duration of 3 years (2011–2014). Regression analysis was used to examine relationships among the variables. It was concluded that there was a positive correlation in between gold and Nifty, and a negative correlation in between dollar price and Nifty. Tripathy, Naliniprava. (2016). Explore the integration between Gold price and Stock market price (Nifty) by using monthly time series data from period July 1990 to April 2016. Used Unit root test, Correlation test, Granger causality test and Johansson’s co-integration test to assess their relationship. The study conclude that no causal relationship exists in between Gold Price and Stock market price in the short run. Whereas both are co-integrated indicating long- run equilibrium relationship, and they move together i.e. Stock market price can be used to forecast the Gold price. CUSUM test also confirms to check long run relationship in- between Gold and Stock market price and results reveals stability in coefficient. 2.2 Stock Market prices Vs USD exchange rates Bodnar and Gentry (1993) authors reported that they were unsuccessful to learn an important connection between stock returns and foreign exchange rate changes neither at a heap near nor at the level of firms specific. Ajayi and Mougoue (1996) studied active associations among stock returns and dollar exchange rate changes used co- integration. Engle (1982) and later continued by Engle and Bollerslev (1987) and Nelson (1991) documented focuses on the rapport between mean stock market returns also foreign conversion rate returns. The strongly focuses on performance of volatility of stock returns has been measured by use of the ARCH-GARCH. Bollerslev (1987), French, Schwert and Stambaugh (1987), Schwert (1989), Akgiray (1989) and Black (1976) developed that the instability in the prices of stock changes over a period of interval besides major finding were that the responds were asymmetrically based on the wax and wane newscast. Kanas (2000) observed that the volatility deluges in between stock earnings and dollar exchange rate changes. He studied variance of returns related to multi-currency portfolio is totally depending upon the changes of specific stock market prices, changes of the dollar exchange rate and further their combine covariance of it. In inclusion to this, the stock market prices and foreign exchange rate changes are found linked with each other; this would perhaps affect the non-systematic i.e. unavoidable peril of multi-currency equity portfolios. Abdalla and Murinde (1997) observed dollar conversion rates also stock prices interactions in the growing financial market places i.e. India, Korea, Pakistan and the Philippines. The data was tested with the help of granger causality test and concluded unidirectional interconnection in dollar conversion rates to stock market prices was observed in all the countries taken for the study, excluding Philippines. Bhattacharya et. al. (2001) studied as an output of the case study in reference to the Indian stock market contextual, especially with respect to the dollar conversion rate, foreign exchange funds and trade stability. As an upshot of the learning suggested that there stood no pivotal relationship in

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between stock market prices and other three variables in the study under deliberation. Aydemir and Demirhan (2009) find an effect of causative relationship on stock values and dollar conversion rates for the statistics sample period from 23 February 2001 to 11 January 2008 of Turkey city. This factual investigation results in bifacial factorial relationship between dollar conversion rate and all securities market indices. Li and Chiu (2011) elaborated single variation GARCH model in his paper to assess the S&P 500 index and WTI crude oil prices association. Summed up with major instabilities in crude oil prices must have an adverse impact on S&P 500 outcome, but their upshots need not been accepted at the low value variables scenario in their study. Mensi et al. (2013) applied VAR-GARCH model to inspect the returns associations and volatility spillover between commodity and stock markets. They identify an important link and instability across commodity and equity markets. Apte P. (2001) explored relationship in between Stock market volatility and foreign exchange rates for the period 1991 to 2000 and concluded that April 1993, the exchange rate was not really market determined. 2.3 Gold prices Vs Stock Market prices Kumar (2014) uses Vector Autoregressive (VAR) Asymmetric Dynamic Conditional Correlation Bivariate GARCH (VAR-ADCC-BVGARCH) model to probe the reoccurrence besides instability transmission between the gold market and the securities market. But the learning outcome is unsuccessful to identify any crucial evidence of instability spillover from gold toward Indian securities market. However, the nature of time-varying association, results shows a pessimistic link at the wheel of crunches besides optimistic for the rest of the period. Angi and Harald S (2012) studied impact festival seasons on gold prices and stock market volatility in china and India. They used regression and GARCH model and concluded that there is increase in gold prices as well as stock market volatility too. Chakrapani R and Kannaiah. P examines the gold prices volatility for the period of 1997 to 2007. Use of technical analysis and Holt-Winters Model method to analyses the data. Kaur S and Kaur D (2017) analyse the effect of Gold prices on Indian stock market i.e. BSE-SENSEX. The secondary data was taken from April 2007 to March 2016 for the study. The statistical tools were used Econometric regression analysis indicated that Gold prices had a significant influence on Indian Stock Market represented by BSE-SENSEX. They concluded that positive correlation in between Gold prices and BSE – SENSEX. Kothari A and Gulati D (2015) documented study to the data gold prices and SENSEX index from 1979 to 2013. It was observed during the period high degree of positive correlation is between gold prices and SENSEX index and Granger causality test find confirmation of unidirectional causality from SENSEX to Gold Prices. 2.4 Gold prices Vs USD exchange rates Kiohos & Sariannidis (2010) discussed on the influence of during short run of financial market and energy on the gold market. The study used crude oil prices as an indicator for energy market and equity, currency exchange rate of USD to yen as financial market. GJRGARCH model to study the relationship among the variables for daily data for 10 years (1/01/1999 to 31/08/2009) and concluded that the USD exchange rate has an impact on gold prices. Sjaastad, (2008) the is theoretical and empirical in nature. The study was analyzed by using the technique of forecasting error data to check relationship between price of gold and exchange rate of major currencies. It was concluded that the appreciation or depreciation of US Dollar has been a very vital factor in influencing the gold prices. Tully & Lucey (2007) examined the relationship between gold prices and USD exchange rate. Six different models of GARCH family

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were used in this study. The data for various economic variables over a period of year from 1983–2003 and both spot and futures prices of gold were collected. The study approves that the however few macro-economic variables have a statistically significant influence on gold prices, the USD exchange rate is vital factor which has a strong impact on gold prices. Capie, Mills, & Wood (2004) the covered weekly data for thirty years for spot prices of gold, exchange rates of Great Britain Pound and Japanese Yen to US Dollar to understand up to what extent gold has acted as a hedge against exchange rate adversities. The study used GARCH model for the analysis. They determined that the relationship between gold and exchange rates of the currencies to USD is inelastic and negative. But the nature and the strength of this relationship has shifted over time. The hedging property of yellow metal is highly dependent on political events and political uncertainty. Capie, Mills, & Wood, (2005) this study covers Gold as a Hedge against the US Dollar the data was collected from London Market. The study concluded that gold is a very good hedge against the exchange value of dollar to home currency. However, in the recent past few studies have engrossed on the unpredictability and shockwave spread between the Crude oil prices, USD exchange rates, gold prices, stock market prices and commodity market prices, using the different database and various econometric techniques. III RESEARCH METHODOLOGY The research is empirical in nature and is constructed on quantitative facts. For the purpose of statistical analysis, the financial data of USD conversion rates i.e. US Dollar Prices against , Gold Prices and National Stock Exchange (NSE) - Nifty50 Index Prices- Closing Prices for the time period Jan 2007 to Dec 2016 on daily basis were taken. Data have been collected from secondary sources i.e. websites, journals, annual report of RBI, RBI data bank beside this data was also collected from NSE - Nifty50 Index Prices. 3.1 Research steps taken in research 1. First step is to follow the presence of a unit root in each variable in data under investigation, using Augmented Dickey – Fuller (ADF) at level which is non stationary for all the variables in this study. 2. 1st Differencing the data in the presence of unit root and conduct the (ADF) test again on the differenced data for all the variables in the study. (neglect + or – sign for comparing the results) 3. Estimate Cointegration using the same order of integrated variables. 4. Granger – Causality test for VAR, thus the first step is to apply unit root test to the preformed data, a stationary series is generally characterized by a time invariant mean and a time invariant variance. The stationary of each variable can be tested by the following unit root test. 3.2 Objectives of the study 1. To examine interdependencies / causal relationship among USD exchange rates, Gold Prices and NSE- Nifty50 Index Prices. 2. To acknowledge the bidirectional connection among USD exchange rates, Gold Prices and NSE-Nifty50 Index Price. 3.

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IV EMPIRICAL ANALYSIS AND FINDINGS

4.1 Augmented Dickey-fuller Test (ADF).

The ADF tests the null hypothesis that time series data is unit root. The alternative hypothesis is different depending state that data in the time series is stationary. The augmented Dickey–Fuller (ADF) statistic, used in the test, is a negative number. The more negative it is, the stronger the rejection of the hypothesis that there is a unit root at some level of confidence. The computed value is higher than critical value hence the null hypothesis is rejected and data is stationary . If the test statistic is less than critical value null hypothesis is accepted i.e. the data is non stationary. For any analysis in time series the data first has to stationary. Here the NSE Nifty prices, Gold prices and USD Exchange rates were found non stationary at level. But when the 1st difference data was considered to calculate the ADF calculation for all the three variables.

Table – 1 Results Unit Root Test Using ADF TEST

Null Hypothesis T- statistics P-Value Remark NSE Nifty 50 ADF -46.39 0.0001 Variables are Returns are not 1% level -3.432 Stationary stationary 5 % Level -2.862 10% Level -2.567 USD exchange ADF -48.86 0.0001 Variables are Rates are not 1% level -3.432 Stationary stationary 5 % Level -2.862

10% Level -2.567 Gold Prices are not ADF --37.29 0.0000 Variables are stationary 1% level -3.432 Stationary 5 % Level -2.862 10% Level -2.567

Source: Complied with E views software

From the above table no. 1 reflects that the result of ADF implies that the ADF value of is more than that of critical value i.e. -46.3897 is greater than critical value 1% (-3.432), 5% (-2.862) and 10% (-2.56), Hence H0 is reject for the series NSE Nifty 50 Returns is found to be stationary and possess no unit root. In case of USD exchange rates that the result of ADF implies that the ADF value is more than that of critical value i.e. -0.48.86672 is greater than 1% (- 3.432), 5% (-2.862) and 10% (-2.56), Hence H0 is rejected for the series USD exchange Rates is found to be stationary and possess no unit root. The ADF value of Gold prices is more that critical value i.e. -37.29832 is greater than 1% (- 3.432), 5% (-2.862) and 10% (-2.56), Hence H0 is rejected for the series Gold Prices is found to be stationary and

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possess no unit root. As all the series i.e. Gold Prices, Dollar exchange rates and NSE Nifty 50 Returns are stationary. hence, we need to apply co -integration to the series. To check do together they are stationary or not.

4.2 Cointegration

The data set used for this study is more than two series (in time series sense) to check the linear combination of then has a lower order of integration is check through cointegration. To check the vector of coefficient, exist to form a stationary linear combination in the time series. Testing the hypothesis that there is significant connection among the variables. The cointegration test i.e. The Max and Trace Test was applied at level data and the output is shown in table no 4.

Table- 2 The Max and Trace Test of Gold prices, USD exchange rates and NSE Nifty 50 prices

Date: 09/06/19 Time: 11:55 Sample (adjusted): 3 2465 Included observations: 2463 after adjustments Trend assumption: Linear deterministic trend Series: GOLD_PRICES NSE USD_PRICE Lags interval (in first differences): 1 to 1

Unrestricted Cointegration Rank Test (Trace)

Hypothesized Trace 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.003369 14.53834 29.79707 0.8091 At most 1 0.002267 6.226931 15.49471 0.6687 At most 2 0.000259 0.637064 3.841466 0.4248

Trace test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

Hypothesized Max-Eigen 0.05 No. of CE(s) Eigenvalue Statistic Critical Value Prob.**

None 0.003369 8.311409 21.13162 0.8837 At most 1 0.002267 5.589867 14.26460 0.6662 At most 2 0.000259 0.637064 3.841466 0.4248

Max-eigenvalue test indicates no cointegration at the 0.05 level * denotes rejection of the hypothesis at the 0.05 level **MacKinnon-Haug-Michelis (1999) p-values

Unrestricted Cointegrating Coefficients (normalized by b'*S11*b=I):

GOLD_PRICES NSE USD_PRICE -2.59E-06 0.000951 -0.157122 6.37E-05 0.000132 -0.075081 3.54E-05 -0.000295 -0.112985

Unrestricted Adjustment Coefficients (alpha):

D(GOLD_PR... 10.97298 -21.25580 9.895032 D(NSE) -4.343099 -0.201837 0.104290 D(USD_PRICE) 0.005216 0.009425 0.002632

1 Cointegrating Equation(s): Log likelihood -34161.18

Normalized cointegrating coefficients (standard error in parentheses) GOLD_PRICES NSE USD_PRICE 1.000000 -367.4420 60721.18 (134.399) (23183.3)

Adjustment coefficients (standard error in parentheses) D(GOLD_PR... -2.84E-05 (4.1E-05) D(NSE) 1.12E-05 (3.9E-06) D(USD_PRICE) -1.35E-08 (1.4E-08)

2 Cointegrating Equation(s): Log likelihood -34158.38

Normalized cointegrating coefficients (standard error in parentheses) GOLD_PRICES NSE USD_PRICE 1.000000 0.000000 -831.3920 (737.016) 0.000000 1.000000 -167.5165 (40.2744)

Adjustment coefficients (standard error in parentheses) D(GOLD_PR... -0.001382 0.007626 (0.00101) (0.01516) D(NSE) -1.62E-06 -0.004156 (9.7E-05) (0.00145) D(USD_PRICE) 5.87E-07 6.20E-06 (3.5E-07) (5.3E-06)

H0: the time series has no cointegration

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H1: the time series has cointegration

From the above table 4 reflects that the result of cointegration test implies that the Calculated value of is more that P- value 0.05 level, Hence H0 is accepted for the time series possess no cointegration.

4.3 Granger causality test at VAR

The variables in the study i.e. Gold Prices, USD exchange rates and NSE Nifty 50 Prices were observed having no cointegration among them, hence to check the bidirectional relationship among the variables I have used Granger Causality Test at VAR environment. Equation formed are stated below:

Equation 1: GOLD_PRICES = C(1)*GOLD_PRICES(-1) + C(2) *GOLD_PRICES(-2) + C(3)*NSE(-1) + C(4)*NSE(-2) +... *USD_PRICE(-1) + C(6)*USD_PRICE(-2) + C(7)

Equation 3: USD_PRICE = C(15)*GOLD_PRICES(-1) + C(16) *GOLD_PRICES(-2) + C(17)*NSE(-1) + C(18)*NSE(-2) + C(19) *USD_PRICE(-1) + C(20)*USD_PRICE(-2) + C(21)

Table – 3 Ganger Causality test at VAR

VAR Granger Causality/Block Exogeneity Wald Tests Date: 09/06/19 Time: 17:33 Sample: 1 2465 Included observations: 2462

Dependent variable: D(USD_PRICE)

Excluded Chi-sq df Prob.

D(NSE) 108.1076 2 0.0000 D(GOLD_PRICES) 13.15666 2 0.0014

All 130.0125 4 0.0000

Dependent variable: D(NSE)

Excluded Chi-sq df Prob.

D(USD_PRICE) 2.526669 2 0.2827 D(GOLD_PRICES) 4.149960 2 0.1256

All 6.518028 4 0.1637

Dependent variable: D(GOLD_PRICES)

Excluded Chi-sq df Prob.

D(USD_PRICE) 7.295775 2 0.0260 D(NSE) 0.989472 2 0.6097 All 7.601849 4 0.1073

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Table – 6 Ganger Causality Hypothesis Testing

Sr. Ganger Causality Hypothesis Testing P- value (0.05) Output Accept / Reject Causality Direction no. H0

01 H0: NSE Nifty 50 doesn’t have Ganger cause USD 0.000 Reject H0 Unidirectional exchange rates

H0: USD exchange rates don’t have Ganger cause NSE 0.2827 Accept H0 No causality Nifty 50

02 H0: Gold Prices doesn’t have Ganger cause USD 0.0014 Reject H0 Bi- directional exchange rates causality

H0: USD exchange rates don’t have Ganger cause Gold 0.02650 Reject H0 Bi- directional Prices causality

03 H0: Gold Prices doesn’t have Ganger cause NSE Nifty 0.1256 Accept H0 No causality 50

H0: NSE Nifty 50 doesn’t have Ganger cause Gold 0.6097 Accept H0 No causality Prices

Source: From the analysis of secondary data

Note. We fail to reject H0 when the p-value is >0.05.

From the above table no. 5 reflects that the result of Ganger Causality test at VAR environment concluded that the NSE Nifty 50 prices and Gold Prices no causality in bidirectional ways. Further USD Exchange rate and Gold Prices are bi-directional casual. Last but not the least Nifty 50 have an influence on USD exchange rates but shows non causality in the reverse case.

V. CONCLUSION

The stock market is a mirror index of an Indian economy. This study investigated interdependencies among NSE Nifty 50 prices, Gold prices and USD exchange rates in Indian context. The results concluded that USD exchange rates Apte P. (2001), Geete (2016) and Gold prices shows no causality with NSE Nifty 50 prices Tripathy, Naliniprava. (2016). NSE Nifty 50 prices has no impact on gold prices. NSE Nifty 50 prices is casual with USD exchange rates Abdalla and Murinde (1997). Further Gold prices and USD exchange rates are causality in bidirectional way were observed.

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Graph – 1 Gold Prices

Gold Prices

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Graph – 2 USD Prices

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Graph – 1 NSE

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[2] Apte, Prakash (2001) The Interrelationship between the Stock Markets and the Foreign Exchange Market. IIM Bangalore Research Paper No. 169. Available at SSRN: https://ssrn.com/abstract=2161245 or http://dx.doi.org/10.2139/ssrn.2161245

[3] Ang and Chen (2002) Asymmetric correlations of equity portfolios. Journal of Financial Economic, vol. 63 (3), pp 443-494.

[4] Anil Kothari and Deepti Gulati (2015) Investment in Gold and Stock Market: An Analytical Comparison, Pacific Business Review International Vol 7 (9), pp. 62-68. [5] Aydemir, O., and Demirhan, E. (2009), The relationship between stock prices and exchange rates: Evidence from Turkey. International Research Journal of Finance and Economics, (23), pp. 207–215. [6] Bartov, Eli & Bodnar, Gordon. (1994). Firm Valuation, Earnings Expectations, and the Exchange-Rate Exposure Effect. Journal of Finance. 49. 1755-85. 10.1111/j.1540-6261.1994.tb04780.x. [7] Bahmani-Oskooee, M. and A. Sohrabian (1992). Stock Prices and the Effective Exchange Rate of the Dollar, Applied Economics, Vol. 24 (4), pp- 459-464.

[8] Bhattacharya, B., and Mukherjee, J. (2002), Causal relationship between stock market and exchange rate, foreign exchange reserves and value of trade balance: A case study for India, www.igidr.ac.in. [9] Bhunia, A. and Ganguly, S. (2015). Cointegration Influence of Macroeconomic Indicators on in India. American Journal of Theoretical and Applied Business, Vol.1 (1), pp.1-5. [10] Bhunia, D.A. (2013). Cointegration and causal relationship among crude oil, Domestic Gold price and financial variables- An evidence of BSE and NSE. Journal of Contemporary Issues in Business Research. [11] Bodnar, G. M., & Gentry, W . M. (1993). Exchange rate exposure and industry characteristics: Evidence from Canade, Japan and the USA. Journal of International Money and Finance, vol 12, pp 29-45. [12] Branson (1983) A Model of Exchange-Rate Determination with Policy Reaction: Evidence from Monthly Data, online access http://www.nber.org/papers/w1135.pdf [13] Capie, F., Mills, T. C., & Wood, G. (2005). Gold as a hedge against the dollar. International Financial Markets, Institutions and Money, Vol 15, pp 343– 352. [14] Capie, F., Mills, T. C., & Wood, G. (2004). Gold as a Hedge against the US Dollar. World Gold Council, Research Study No 30. [15] Choi, Jongmoo & Rajan, Murli. (1997). A Joint Test of Market Segmentation and Exchange Risk Factor in International Capital Market. Journal of International Business Studies. Vol 28. pp 29-49. 10.1057/palgrave.jibs.8490092. [16] Daniel B Nelson, (1991), Conditional Heteroskedasticity in Asset Returns: A New Approach, Econometrica, Vol 59, (2), pp 347-370.

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[17] Dilip Kumar, (2014) Return and volatility transmission between gold and stock sectors: Application of portfolio management and hedging effectiveness, IIMB Management Review, Vol 26 (1), pp 5-16. [18] Doong, S.-Ch., Yang, Sh.-Y., Wang, A., (2005) The dynamic relationship and pricing of stocks and exchange rates: Empirical evidence from Asian emerging markets. Journal of American Academy of Business, Cambridge, Vol.7 (1), pp.118-23.

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[37] Shaminder Kaur and Deepinder Kaur DYNAMIC RELATIONSHIP BETWEEN GOLD PRICES AND INDIAN STOCK MARKET- AN EMPIRICAL ANALYSIS, International conference on Recent Innovation in sciences, Agriculture, Engineering and management, Guru kashi University- Punjab ISBN – 978-93-86171-86-1. [38] Schwert, G. William, (1989), Tests for unit roots: A Monte Carlo investigation, Journal of Business and Economic Statistics, Vol 7, pp 147–159. [39] Sjaastad, L. A. (2008). The Price of Gold and the Exchange Rates: Once Again. Resources Policy , Vol 33 (2), pp 118–124. [40] Tim Bollerslev, (1987), A Conditionally Heteroskedastic Time Series Model for Speculative Prices and Rates of Return, The Review of Economics and Statistics, Vol 69 (3), pp 542-47 [41] Tripathy, Naliniprava. (2016). A Study on Dynamic Relationship between Gold Price and Stock Market Price in India. European Journal of Economics, Finance and Administrative Sciences. Online access : https://www.researchgate.net/publication/306262190_A_Study_on_Dynamic_Relationship_between_Gold_Price_ and_Stock_Market_Price_in_India/citation/download.

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[48] Yahyazadehfar, M. and Babaie, A. (2012). Macroeconomic variables and stock price: New evidence from Iran. Middle East Journal of Scientific Research, Vol. 11(4), pp- 408–415.

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