Exchange Rates Nifty Return Stock Market Causality VECM This paper looks at the relationship between Nifty returns and US Dollar - Indian Rupee Exchange Rates. The study looks into the causal relationship between Nifty returns and exchange rate using Granger Causality test. It took daily data covering the period from January, 2009 to June, 2019. In this study, it was found that both variables were non–normally distributed. With the help of Unit Root Test, it was also verified that Nifty returns as well as Exchange rate, were stationary at the first difference form. Using Granger Causality test it is proved there was a bidirectional relationship between Nifty returns and Exchange rates. From the further investigation it is evident there is a causality running from exchange rate return to stock market return. Finally, employing impulse response function it found that there is a negative relationship among the variables.
## 1. INTRODUCTIONShare price refers to the market value of a firm’s single stock on the day it trades. Stock market volatility is the range of price change, a security experiences over a given period of time. Different factors like company performance, corporate image, GDP, exchange rate, interest rate, money supply etc. will influence the stock price. If the price of the stock stays relatively stable, the security has low volatility and vice versa. Due to globalization the world trade increased, as a result inflow of vast sum of funds between the nations and cross listing of securities also increased. As the world economies are interconnected exchange rates, especially the rates of domestic currencies against US dollar, is of utmost importance to the investor fraternity. Exchange rate is the rate at which one currency is exchanged for another or it may say that, it is the value of one currency for the purpose of conversion to another. Exchange rate volatility that is, unexpected movements in exchange rate have an influence on stock market. Because countries experiencing high volatility can attract only less foreign investment. Moreover, the exchange rate volatility affects the competitiveness of export market and import market, it may have positive or negative effect on the stock market. Here arises the relevance of this research. Talking in the Indian context, a positive move in the exchange rate, that is rupee gaining strength against US Dollar, would have substantial impact over the financials of companies with considerable import bills to be met with their overseas partner. The gain that they could realise from this exchange rate volatility will definitely be reflected by positive movements in their stock values. Likewise, rupee depreciating against dollar would create an affirmative setting for companies with huge export potential, which may result in their stock prices rallying. All the companies that forms the NIFTY 50 is involved in international trade in one way or the other. Inadvertently, they are all sensitive to the exchange rate volatility because such a rate fluctuation will have direct impact on their profitability and financial statements obviously. Thus in an interconnected global financial system, exchange rate is crucial. Over the years, the researchers have analysed whether there exist significant relationship between share price and exchange rate in India. Most of the study revealed that causality between exchange rate and share price can be identified using the econometric tool Granger-Causality Test. This study endeavour is to identify the effect of exchange rate on stock, by employing Granger-Causality test using the data from the past 10 years (from 2009 to 2019). ## 1.1. RESEARCH OBJECTIVEAn economy’s financial position is susceptible to its foreign exchange volatility. Exchange rate volatility has great impact on the financial system of a country especially the stock market. Over the years, a number of researchers have attempted to explain the impact of exchange rate fluctuation on stock market in Indian context as well as global context. But their varying findings have made it impossible to draw a conclusion that can explain the exact relationship between exchange rate and stock market. So, the objective of this study is: · To identify the effect of exchange rate changes on stock price. ## 1.2. RESEARCH HYPOTHESISH H ## 2. LITERATURE REVIEW
## 3. DATA AND METHODOLOGYThe present study is concentrated on the relationship between exchange rate changes and stock price movements. The data collected for the study were Nifty returns and US Dollar - Indian Rupee Exchange Rates. To make the result more precise and reliable, daily level data for a period of 10 years from January 2, 2009 to July 1, 2019 were taken. The data mainly includes: (i) daily closing price of the Nifty index, from which stock returns computed and (ii) US Dollar- Indian Rupee Exchange rates, used to calculate exchange rate returns. The required data were obtained mainly from secondary sources like Yahoo Finance (www.yahoofinance.com) and Investing.com (www.investing.com). Both the variables daily returns and exchange rated have been matched by calendar dates. For the purpose of study, the daily stock returns were
calculated by considering the natural logarithm of the daily closing prices of
Nifty index, i.e. r = ln P(t)/P(t-1), where P(t) is closing price of the t After the extensive literature review, hypotheses were formulated and in order to test those hypotheses various statistical tools/methods used. From the result obtained the inferences or conclusions about the behaviour of two variables under the study was made. The various tests applied for testing the hypotheses include – JB test, Unit root test and Granger Causality test, VECM and impulse response test which were conducted with the assistance of Eviews software (version10.0). ## 3.1. NORMALITY TESTThe Jarque – Bera Test is used to test whether the two variables namely stock returns and exchange rates individually follow the normal probability distribution. It checks that whether the data have the skewness and kurtosis matching a normal distribution. JB = n [S Where n = sample size, S = skewness coefficient, and K = Kurtosis coefficient. If the variables follow a normal distribution then the value of the skewness and kurtosis will be 0 and 3 respectively. Skewness coefficient, other than 0 indicates the asymmetry in the distribution and Kurtosis value other than 3 denotes leptokurtic or platykurtic. ## 3.2. UNIT ROOT TEST (STATIONARITY TEST)A unit root test has been used to test whether a series is stationary or not. The available data series is said to be stationary if its mean and variance are constant or fixed over time and the value of covariance between two time periods depends only on the distance or lag between the two time periods and not on the actual time at which the covariance is computed [Gujrati (2003)]. Augmented Dickey Fuller (ADF) was used for testing stationarity condition of stock returns and exchange rates [Dickey and Fuller (1979, 1981), Gujrati (2003), Enders (1995)]. The Augmented Dickey-Fuller test specification used here is as given below: ∆Y Y ## 3.3. GRANGER CAUSALITY TESTAs per the concept of Granger’s Causality Test (1969,1988),
a time series X The Granger Causality test is performed as given below: ∆SRTNt= α1 + β11∆ERTNt-1 + β12∆ERTNt-2 +…+ β1n∆ERTNt-n + γ11∆SRTNt-1 + γ12∆SRTNt-2+…+ γ1n∆SRTNt-n + ε1.t ∆ERTNt= α2 + β21∆SRTNt-1 + β22∆SRTNt-2 +…+ β2n∆SRTNt-n + γ21∆ERTNt-1 + γ22∆ERTNt- 2 +…+ γ2n∆ERTNt-n + ε2.t Where, ∆SRTNt is the first difference at time ‘t’ of stock market
return, ∆ERTNtis the first difference at time ‘t’ of exchange rate
return. ‘α’ is the constant, ‘n’ is a positive integer, βj and γjare parameters and
εtis
an error term. ## 3.4. VECTOR ERROR CORRECTION MODELThe VECM is a restricted vector autoregression (VAR) model
designed for use with variables that are cointegrated. The foremost advantage
of VECM is that it has nice interpretation with long term and short term
equations. Generally in a situation where there is cointegration, VECM is
required. If the variables are stationary, then one can claim that cointegration exist
between the variables Xt and Yt. According to Engle and Granger (1987), The
existence of the cointegration implies a causality among the set of variables. If
cointegration exist between Xt and Yt, an error correction term is required in testing
Granger causality as shown below: ∆Xt = α0 + δ1(Xt-1 – γYt-1 ) + Σ α1i ∆Xt-i+ Σ α2i ∆Yt-i+ ε1t ∆Yt = β0 + δ2( Xt-1 – γYt-1 ) + Σ β1i ∆Xt-i + Σ β2i ∆Yt-i + ε2t Where, δ1 and δ2 denote speed of adjustment. ## 3.5. IMPULSE RESPONSEThe impulse response function, as an econometric technique, has been employed to investigate the short run impact caused by the vector auto regression model when it received some impulses. The impulse response function describes the response to the error by the endogenous variables. In other words, it identify the responsiveness of the dependent variables (endogenousvariables) in theVAR when shock is put to the error term such as U1 and U2 at the equation given below. It depicts the current and future responses of endogenous variables to one standard deviation on the disturbing term. The following VAR system models have been framed to capture the responsiveness of SRTN (stock market return) and ERTN (exchange rate return) when the shock is given to the error terms. SRTN = b1 + β2*ERTNt-i + β3*SRTNt-i+ U1 ERTN = b4 + β5*SRTNt-i + β6*ERTNt-i + U2 Where b1 and b4 are the intercepts, β ## 4. EMPIRICAL ANALYSISFor the purpose of study, analysis of data was carried out in five stages. First, normality test was employed on the both the series to
identify the nature of their distribution. For that, Jarque-Bera statistics
were computed using Eviews, which is shown in Table 1.1. For a normal
distribution the value of skewness and kurtosis will be 0 and 3 respectively.
In our study, from the obtained statistics, it is clear that both the variables
namely Nifty returns and exchange rate returns are non-normally distributed.
The skewness values for Nifty returns and exchange rate returns are .968974 and
.082092 respectively and kurtosis values are 21.01739
and 8.942194 respectively.
Second, unit root test was
conducted to check whether stationarity exist in the series. A time series is
said to be stationary, if the mean and variance of the series are constant or
fixed over time. Simplest way to verify the stationarity is to plot time series
graph as shown in Fig 2.1 and Fig 2.2 and observe the fluctuations.
In addition to visual inspection,
a formal econometric test namely ADF test can also be execute to verify the
stationarity of both series. The results of ADF test are shown in Table 1.2.
The critical values for rejection
of hypothesis of existence of unit root is at 5% significance level i.e.
-2.862468. While taking the obtained ADF statistics for the two variables
namely Nifty returns and exchange rate returns, it is clear that the obtained
statistics are -47.84750 and -38.03328 respectively. Since the obtained
statistics fall behind the critical value or the probability value is equal to
0.00, the hypothesis of unit root for both the series can be rejected. Finally,
based on ADF test, it can be concluded that both Nifty returns and exchange
rate returns are said to be stationary at first difference form. Third,
Granger causality test was made with the objective of identifying the causality
of relationship between the variables under study. Results are shown in Table
1.3. From the obtained statistics we can conclude that the null hypothesis- “Exchange
rates do not Granger cause stock returns” should be rejected, since the
probability value obtained is 0.00. At the same time we also reject the null
hypothesis- “Stock returns do not Granger cause the exchange rates”, since the
probability value is 0.01.
Finally,
from the results obtained with help of Granger causality test it is clear that
stock returns direct the exchange rates and vice versa, i.e. there exist a
bidirectional causality only. Fourth, VECM is a restricted VAR which is used with the cointegrated time series data to distinguish the long run and short run relationship between the variables. The result of VECM is shown in Table 1.4. C(1) is the coefficient of the cointegrating model or equation [(SRTN(-1) +1.32702901033* ERTN(-1) – 0.000746982561707)]. C(1) shows the speed of adjustment towards long run equilibrium, but it must be significant and the sign must be negative. From the obtained result, the sign of coefficient is negative (-1.165847) and it is significant, because the probability values is 0% (0.0000) which is less than level of significance. So, there is a long run causality from the independent variable called exchange rate return. C (7), C(8), C(9), C(10) and C(11) are the short run coefficients of the independent variable exchange rate return. The H0 is C(7) = C(8) = C(9) = C(10) = C(11) = 0, meaning that there is no short run causality running from exchange rate return to stock market return. From the obtained result, the probability value is 0% (0.0000) which is less than the level of significance (5%). So we can reject the null hypothesis. Therefore, there is a short run causality from exchange rate return to stock market return. The result of Wald statistics shown in Table 1.5.
Finally, the impulse response function is employed to find the pattern of how a shock in exchange rate return affect stock market return and vice versa. The graph of impulse response function helps to trace out how typical shocks will affect a variable through time. Figure 5. 1 shows when one SD shock is given to exchange rate return(ERTN) how the stock market return(SRTN) is reacting. When the exchange rate return has a positive shock then stock market return becomes negative. From the figure it is possible to observe that exchange rate return has an immediate negative effect on stock market return. The shock effect troughs during the sixth month and remains negative throughout all ten months. Figure also shows when one SD shock is given to stock market return(SRTN) how the exchange rate return(ERTN) is reacting. When the stock market return has a positive shock then exchange rate return becomes negative. It could be identified that during the first month there is an immense increase in the value, which means the negativity of the value decreases and continues to be same for the further period.
## 5.
CONCLUSION
The
study is conducted to analyse the effect of exchange rate volatility on stock
market volatility. To check the normality of available data the series were
converted into log forms. With help of Jarque-Bera test it found that both the
series namely Nifty returns and exchange rates are non-normally distributed.
The stationarity of series is also conducted by the application of ADF test.
From the result it affirmed that both series are stationary at first difference
forms. The Granger causality test was applied to the two variables under study
and found that there is a causality running from stock returns to exchange
rates as well as exchange rates to stock return. This means a bidirectional
causality exist between exchange rate and share price. So each variable has an
influence on the other. Also it is found that exchange rate return have
significant impact on Nifty return in the long run as well as in the short run.
From the impulse response function, it can be inferred that there is a negative
relationship among the variables. In conclusion it can say that, the financial
analysts, economists and the investors can use the past exchange rate data to
forecast share price for making better policies and for creating good portfolios. ## SOURCES OF FUNDINGThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. ## CONFLICT OF INTERESTThe author have declared that no competing interests exist. ## ACKNOWLEDGMENTNone. ## REFERENCES
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