Research Paper Titled Causal Relationship between Foreign Exchange Rate and Gold Prices, BSE Index, NSE Index and Oil & Gas Prices in India. Author: Dr. Vinod K. Bhatnagar Assistant Professor Prestige Institute of Management, Gwalior, MP, India Email: dr.vinodbhatnagar@gmail.com Mob.: 9329082151 Co-authors Ms. Parul Tomar Student (BBA-Finance) Prestige Institute of Management, Gwalior, MP. India Email: parultomar98@gmail.com And Ms. Pooja Jain Assistant Professor, Vikrant Institute of Management, Gwalior, MP. India Published in Edited Book, Sustainability Management and the power of Innovation, Prestige Institute of Management Gwalior, Bloomsbury Publication, New Delhi, 2014.
ABSTRACT The foreign exchange market determines the relative values of different currencies. The foreign exchange market assists international trade and investment by enabling currency conversion now a days. The objectives of the study are to find out causal relationship between Forex and Gold price in India, Forex and NSE Index in India, Forex and BSE Index in India, Forex and Oil & Gas price in India. The study is empirical in nature. Four variables Gold Prices, BSE Index, NSE Index and Oil & Gas Prices in India were used as sample. Data was collected from X- rate.com, BSE, NSE and other related websites. Gold Prices, BSE Index, NSE Index and Oil & Gas Prices in India were used as independent variable and Foreign Exchange Rate was used as dependent variable. As a technique we have used linear regression analysis. Our study revealed that there is linear relationship among Forex, Gold Prices, BSE Index, NSE Index and Oil & Gas Prices in India. Key words: Forex, Gold Price, BSE Index, NSE Index, and Oil & Gas Prices in India. 1. Dr. Vinod K. Bhatnagar, Assistant Professor, Prestige Institute of Management, Gwalior,MP. 2. Ms. Parul Tomar, Student (BBA-Finance), Prestige Institute of Management, Gwalior,MP. 3. Ms. Pooja Jain, Assistant Professor, Vikrant Institute of Management, Gwalior,MP.
Introduction Foreign Exchange Market The foreign exchange market is a form of exchange for the global decentralized trading of international currencies. The foreign exchange market determines the relative values of different currencies. The foreign exchange market assists international trade and investment by enabling currency conversion now a days. It also supports direct speculation in the value of currencies. In a typical foreign exchange transaction, a party purchases a quantity of one currency by paying a quantity of another currency. The market in which participants are able to buy, sell, exchange and speculate on currencies. Foreign exchange markets are made up of banks, commercial companies, central banks, investment management firms, hedge funds, and retail forex brokers and investors. The forex market is considered to be the largest financial market in the world. The foreign exchange market is the most liquid financial market in the world. Traders include large banks, central banks, institutional investors, currency speculators, corporations, governments, other financial institutions, and retail investors. Bombay Stock Exchange The first and largest securities market in India, the Bombay Stock Exchange (BSE) was established in 1875 as the Native Share and Stock Brokers' Association. Based in Mumbai, India. The BSE has helped develop the country's capital markets, including the retail debt market, and helped grow the Indian corporate sector. Securities listed by the BSE include stocks, stock futures, stock options, index futures, index options and weekly options. The BSE's overall performance is measured by the Sensex, an index of 30 of the BSE's largest stocks covering 12 sectors. National stock exchange The National Stock Exchange is India's largest financial market. Established in 1992, the NSE has developed into a sophisticated, electronic market, which ranks third in the world for
transacted volume. The NSE conducts transactions in the wholesale debt, equity and derivative markets. The exchange's supports more than 3,000 VSAT terminals, making the NSE the largest private wide-area network in the country. Gold Price The price at which gold is being traded on the gold market. It is the commodity prices expressed in gold. Oil and Gas Price A naturally occurring, unrefined petroleum product composed of hydrocarbon deposits. Crude oil can be refined to produce usable products such as gasoline, diesel and various forms of petrochemicals. The price at which crude oil is being sold on oil market. Gas is a substance like air that is neither liquid nor solid and burns easily. It is used as a fuel for cooking and heating. The price at which gas is being traded in the gas market is known as gas price. Review of Literature Abdalla and Murinde (1996) examined, interactions between exchange rates and stock prices in the emerging financial markets of India, Korea, Pakistan and the Philippines. The granger causality test was applied and the results show unidirectional causality from exchange rates to stock prices in all the sample countries, except the Philippines. Bhattacharya et. al.(2001) analyzed Causal Relationship between Stock Market and Exchange Rate, Foreign Exchange Reserves and Value of Trade Balance. The analysis revealed interesting results in the context of the Indian stock market, particularly with respect to exchange rate, foreign exchange reserves and trade balance. The results suggest that there is no causal relationship between stock prices and the three variables under consideration. Mishra (2004) examined the relationship between stock market and foreign exchange markets by applying Granger causality test and Vector Auto Regression technique study suggested that there is no Granger causality between the exchange rate Return and stock return. Bahmani and Sohrabian (1992) found a bi-directional causality between stock prices rated by the Standard & Poor's 500 index and the effective exchange rate of the dollar, particularly in the short run. The co-integration analysis revealed that there is no long run relationship between the two variables i.e stock prices and exchange rate. Dimitrova (2005)
explored the relationship between stock prices and exchange rates by using multivariate model. The main focus was on the stock markets of United States and the United Kingdom over the period from January 1990 to August 2004. This study developed the hypothesis that there is a relationship between the foreign exchange and stock markets. He found that relationship is positive when stock prices are the lead variable and likely to negative when exchange rates are the lead variable. Doong et al (2005) investigated the dynamic relationship between stocks and exchange rates for six Asian countries viz. Indonesia, Malaysia, Philippines, South Korea, Thailand, and Taiwan. For the period of 1989 to 2003. He found that there is a significantly negative relation between the stock returns and the contemporaneous change in the exchange rates for all countries except Thailand. Kilian et. al (2007) show the presence of large and systematic valuation effects in response to oil shocks, not only for the United States, but also for other oil-importing economies and for oil exporters. Their estimates suggest that increased international financial integration will tend to cushion the effect of oil shocks on NFA positions for major oil exporters and for the United States, but may amplify it for other oil importers. Robert Gay (2008) investigated the time-series relationship between stock market index prices and the macroeconomic variables of exchange rate and oil price for Brazil, Russia, India, and China (BRIC) using the Box-Jenkins ARIMA model. He found that there is no significant relationship between respective exchange rate and oil price on the stock market index prices of either BRIC country and also there was no significant relationship found between present and past stock market returns. Hussain et al. (2009) examined the Impact of Macroeconomics Variables on Stock Prices: Empirical Evidence in Case of KSE they took the quarterly data of various macro economic variables such as foreign exchange rate, foreign exchange reserve, industrial production index, whole sale price index, gross fixed capital formation, and broad money M2, of the period from 1986 to 2008 period. The results shows that after the reforms in 1991 the influence of foreign exchange rate and reserve effects significantly to stock market whiles other variables like IIP and GFCF have no impact on stock prices. The result also stated that internal factors of firms like increase production and capital formation does not effects significantly while external factors like exchange rate and reserve effects significantly the stock prices. Rafiq et al (2009) summarized previous studies and revealed that oil price shocks have significant asymmetric impact on
macroeconomic fundamentals; the negative shocks having much larger impact than the positive ones. The study employs extended Vector Auto-regression (VAR) model proposed by Toda and Yamamoto (1995) hereafter TY. The two main advantages of TY method are; first it can be used irrespective of order of integration of underlying variables and second irrespective of whether or not the variables are cointegrated. Sohail et al( 2009) conducted a research on LSE, the intention of this study was to examine long-run an short-run relationships between Lahore Stock Exchange and macroeconomic variables in Pakistan. Monthly data from December 2002 to June 2008 was used in this study. The results revealed that there was a negative impact of consumer price index on stock returns, while, industrial production index, real effective exchange rate, money supply had a significant positive effect on the stock returns in the longrun. Aydemir and Demirhan (2009) studied the causal relationship between stock prices and exchange rates,using data from 23 February 2001 to 11 January 2008 for Turkey. Their empirical research found thebidirectional causal relationship between exchange rate and all stock market indices. While the negativecausality exists from national 100 services, financial and industrial indices to exchange rate, there exists apositive causal relationship from technology sector indices to exchange rate. On the other hand, negative causal relationship from exchange rate to all stock market indices is determined. Objectives of the study 1. To find out cause and effect relationship between Forex and Gold price in India. 2. To find out cause and effect relationship between Forex and NSE Index in India. 3. To find out cause and effect relationship between Forex and BSE Index in India. 4. To find out cause and effect relationship between Forex and Oil and Gas price in India. Research Methodology The research is empirical in nature and is based on quantitative data. For the purpose of statistical analysis the financial data of Forex, Gold Prices, NSE Index, BSE Index and Oil and gas prices in India for the time period July 2003 to June 2013 on monthly 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 BSE, NSE and MCX. Linear regression was used to find out the causal relationship between the sample variables. Regression analysis is used
dimension0 1 to infer causal relationships between the independent and dependent variables. As in our study, we have one dependant variable (i.e. foreign exchange rate) and four independent variables (i.e. Gold Prices, NSE Index, BSE Index and Oil and Gas Prices in India). Hypothesis: H 0 1: There is no relationship between foreign exchange rate and Gold Prices in India. H 0 2: There is no relationship between foreign exchange rate and NSE Index in India. H 0 3: There is no relationship between foreign exchange rate and BSE Index in India. H 0 4: There is no relationship between foreign exchange rate and oil & gas prices in India. Analysis H 0 1: There is no relationship between foreign exchange rate and Gold Prices in India. Table 1- Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson.602 a.363.358 3.40708.112 a. Predictors: (Constant), Gold.Prices The model fit output consist of a model summary. The correlation coefficient R is 0.602 indicates that independent variables i.e. Gold price is 60.2% variance in Forex. It indicates that there is a strong relationship between Forex and Gold prices in India. Table 2- ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 786.767 1 786.767 67.777.000 a Residual 1381.373 119 11.608 Total 2168.140 120 a. Predictors: (Constant), Gold.Prices
dimension0 1 Table 2- ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 786.767 1 786.767 67.777.000 a Residual 1381.373 119 11.608 Total 2168.140 120 a. Predictors: (Constant), Gold.Prices ANOVA table provides an F-test for the null hypothesis that there is no relationship between forex and gold prices in India. Here we can clearly reject this null hypothesis (F = 67.777, P < 0.005) and found that there is a relationship between forex and gold prices in India. Table 3- Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 1 (Constant) 41.244.711 58.009.000 Gold.Prices.006.001.602 8.233.000 a. Dependent Variable: Forex The beta value is positive in case of Gold prices in India, it indicates that there is positive relationship between Forex and Gold prices in India. The b regression coefficient, (β = 0.602) representing that there would be change of 60.2% if there is a change of 1 unit in corresponding variables. H 0 2: There is no relationship between foreign exchange rate and NSE Index in India. Table 4 - Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson.284 a.081.073 4.09277.079 a. Predictors: (Constant), NSE The model fit output consist of a model summary. The correlation coefficient R is 0.284 indicates that independent variables i.e. NSE index is 28.4 % variance in Forex. It indicates that there is a relationship between Forex and NSE index in India.
dimension0 1 Table 5-ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 174.794 1 174.794 10.435.002 a Residual 1993.345 119 16.751 Total 2168.140 120 a. Predictors: (Constant), NSE ANOVA table provides an F-test for the null hypothesis that there is no relationship between forex and NSE index in India. Here we can clearly reject this null hypothesis (F = 10.435, P < 0.005) and found that there is a relationship between forex and NSE index in India. Table 6-Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 1 (Constant) 43.254 1.075 40.232.000 NSE.001.000.284 3.230.002 a. Dependent Variable: Forex The beta value is positive in case of NSE index in India, it indicates that there is positive relationship between Forex and NSE index in India. The b regression coefficient, (β = 0.284) representing that there would be change of 28.4 % if there is a change of 1 unit in corresponding variables. H 0 3: There is no relationship between foreign exchange rate and BSE Index in India. Table 7-Model Summary b Model R R Square Adjusted R Square Std. Error of the Estimate Durbin-Watson.265 a.070.062 4.11583.076 a. Predictors: (Constant), BSE
The model fit output consist of a model summary. The correlation coefficient R is 0.265 indicates that independent variables i.e. BSE index is 26.5 % variance in Forex. It indicates that there is a relationship between Forex and BSE index in India. Table 8- ANOVA b Model Sum of Squares Df Mean Square F Sig. 1 Regression 152.273 1 152.273 8.989.003 a Residual 2015.867 119 16.940 Total 2168.140 120 a. Predictors: (Constant), BSE ANOVA table provides an F-test for the null hypothesis that there is no relationship between forex and BSE index in India. Here we can clearly reject this null hypothesis (F = 8.989, P < 0.005) and found that there is a relationship between forex and BSE index in India. Table 9- Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta T Sig. 1 (Constant) 43.525 1.065 40.883.000 BSE.000.000.265 2.998.003 a. Dependent Variable: Forex The beta value is positive in case of BSE index in India, it indicates that there is positive relationship between Forex and BSE index in India. The b regression coefficient, (β = 0.265) representing that there would be change of 26.5 % if there is a change of 1 unit in corresponding variables.
H 0 4: There is no relationship between foreign exchange rate and oil & gas prices in India. Table 10- Model Summary b Model R R SquareAdjusted R SquareStd. Error of the Estimate Durbin-Watson dimension01.603 a.364.359 3.40410.143 a. Predictors: (Constant), Oil.Gas.Price The model fit output consist of a model summary. The correlation coefficient R is 0.603 indicates that independent variables i.e. Oil & Gas price is 60.3 % variance in Forex. It indicates that there is a strong relationship between Forex and Oil & Gas price in India. Table 11ANOVA b Model Sum of Squares df Mean Square F Sig. 1 Regression 789.176 1 789.176 68.103.000 a Residual 1378.964 119 11.588 Total 2168.140 120 a. Predictors: (Constant), Oil.Gas.Price ANOVA table provides an F-test for the null hypothesis that there is no relationship between forex and Oil & Gold price in India. Here we can clearly reject this null hypothesis (F = 68.103, P < 0.005) and found that there is a relationship between forex and Oil & Gas price in India. Table 12-Coefficients a Model Unstandardized Coefficients Standardized Coefficients B Std. Error Beta t Sig. 1 (Constant) 52.359.773 67.725.000 Oil.Gas.Price -.007.001 -.603-8.252.000 a. Dependent Variable: Forex The beta value is negative in case of Oil and Gas price in India, it indicates that there is negative relationship between Forex and Oil and Gas price in India. The b regression coefficient, (β = -
0.603) representing that there would be change of 60.3 % if there is a change of 1 unit in corresponding variables. Findings 1. We found that correlation coefficient R is 0.602 indicates that independent variables i.e. Gold price is 60.2% variance in Forex. 2. ANOVA table - 2 provides an F-test (F = 67.777, P < 0.005) and found that there is a relationship between forex and gold prices in India. 3. We found that correlation coefficient R is 0.284 indicates that independent variables i.e. NSE index is 28.4 % variance in Forex. 4. ANOVA table - 5 provides an F-test (F = 10.435, P < 0.005) and found that there is a relationship between forex and NSE index in India. 5. We found that correlation coefficient R is 0.265 indicates that independent variables i.e. BSE index is 26.5 % variance in Forex. 6. ANOVA table -8 provides an F-test (F = 8.989, P < 0.005) and found that there is a relationship between forex and BSE index in India. 7. We found that correlation coefficient R is 0.603 indicates that independent variables i.e. Oil & Gas price is 60.3 % variance in Forex. 8. ANOVA table - 11 provides an F-test (F = 68.103, P < 0.005) and found that there is a relationship between forex and Oil & Gas price in India. Conclusion In present scenario Foreign Exchange plays very important role. Foreign Exchange Rate can predict or determine share prices, gold prices, GDP and other micro and macro economic variables in the nation. Our study was based on four variables i.e. Forex, Gold prices in India, NSE Index, BSE Index, Oil & Gas prices in India. We can conclude that there is a strong relationship between Forex and Gold prices in India & Oil and Gas prices in India while there is relationship between Forex and BSE & NSE Index. ****************
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