Relationship between Stock Prices and Exchange Rate ...conclude that both stock prices and exchange...

ISSN 2309-0081 Mohammed & Rostam (2016)

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www.irss.academyirmbr.com December 2016

International Review of Social Sciences Vol. 4 Issue.12

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Relationship between Stock Prices and Exchange Rate:

Evidence from BRICS Countries

BURHAN ALI MOHAMMED Department of Accounting, Darbandixan Technical Institute Sulaimani Polytechnic University

Email: [email protected]

BIABAN NWRI ROSTAM Department of Economics, Faculty of Commerce University of Sulaimania

Email: [email protected]

Abstract

In order to determine whether there exist interdependence between stock prices and real exchange rates in

the BRICS economics (Brazil, Russia, India, China and South Africa), this study examines the dynamic

relationship between the two by utilizing monthly data from January 2000 to Jun 2016. However, the data

for South Africa is taken from April 2004 to Jun 2016. For this aim, Unit root, Johansen co-integration,

Vector Error Correction Model (VECM) and Granger Causality tests are concluded to analyses the data.

The result indicates that all the series are integrated of order one, I (1) when they are differenced. India

represented bidirectional causality and there is unidirectional relation running from exchange rate to stock

price in case of Brazil but in the case of Russia and South Africa unidirectional causality moving from

stock prices to exchange rate. However, no relationship between variables was determined in China.

Further, from the VECM we find negative correlation between stock price and exchange rate in BRICS

countries, but the correlation was not negative in in month 1 for Russia and India; and in month 1 and 2 for

China. We additionally find that there is long term co-integration relationship between the Stock prices and

real exchange rate in BRICS economics.

Key Words: Stock Price, Exchange Rate & BRICS Countries.

Introduction

Stock market is a significant factor impacting a country's economic growth, and may have implication for

other macroeconomic factors to obtain desired results. Many macroeconomic variables for example,

inflation, interest rate and exchange rate effect on stock prices (Mgammal, 2012). Changes in exchange rate

precisely impact "the international competitiveness" of enterprises, given their effect on the prices of output

and input (Joseph, 2002). Exchange rate has become important factor of share price and business

profitability due to the ongoing rises in global trade and capital fluctuations, as stated by (Kim, 2003). For

empirical and theoretical reasons, correlation between exchange rates and stock markets has lately

preoccupied the minds of many academics and economists, since they both importantly play key roles in

affecting a country's economic growth.

The theoretical explanations for the interaction between stock prices and exchange rates can be concluded

from two widely utilized forms; namely "Flow-oriented" model of exchange rates (Dornbusch and Fischer,

1980) and "stock-oriented" model of exchange rates (Branson and Henderson 1985; Frankel 1983). The

former emphasizes how changes in exchange rate impact international competitiveness and trade balance,

mailto:[email protected]

mailto:[email protected]


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real income, output and stock price of a firm. The latter hypothesis the exchange rate movement might be

directed by adjustments in equity prices through the exchange rate changes to the demand and supply

changes of local and international financial assets in globally diversified portfolios. Hence, both theorems

conclude that both stock prices and exchange rate have implicit relationship.

This study conducts an empirical investigation into the relationship between stock prices and exchange

rates for BRICS2 (Brazil, Russia, India, China and South Africa) markets, which are regarded to have

similar level of economic growth. Over the last three decades, such nations have been significant players in

the world economy; this is because of their rapid economic development and geographical impact

(Morazan, 2012 and Naudé et al, 2016). After the United State and European Union, the combined

economies of the BRICS nations are now the third largest in the world. It is predicted that by 2050, these

countries can overtake both the US and Euro area, (Neill and Stupnytska, 2009; Goldman and Sachs 2009).

Additionally, it is forecasted that the BRICS economies will be as large as G7 by 2032. Even in the G-

203countries forum, after the recent financial crisis they play a formidable role in shaping macroeconomic

policy (Venkatesh, 2013).

The BRICs acronym was first formulated by economist Jim O'Neill of Goldman Sachs in 2001. With the

inclusion of South Africa, the acronym of such countries became BRICS in 2011. They together presents

important share of global production and population. This explained in the report of BRICS ministry

external relation. (brics.itamaraty.gov.br/about-brics/information-about-brics).

G-20 is the group of world’s largest 20 economies and this G20 includes BRICS countries. There are some

indications to support such an expectation: BRICS economies represent nearly more than 40 percent of

global population and about of a quarter of the land space of the world, which creating those countries the

drivers of worldwide demand and consumption. Moreover, despite becoming the global suppliers of goods

and services, most of these countries are provided natural resources. Since demand on such a resources has

risen in post crisis. China and India are the most populated in the world and are fastest growing economies

in the resent decade. For instance, between 1978 and 2009 the economy of China rise at nearly annual rate

of 9.9 percent, which is much greater than the universal average for same period. In addition, Brazil, Russia

and China supply natural resources and raw material to China and India, (Neill and Stupnytska 2009;

Goldman Sachs 2009, and Naudé et al, 2016). At present, these five nations contains more than 40 percent

of world population and has about 25 percent of World GDP, their role have increased particularly in the

post global financial crisis (The BRICS report, 2012). According to the report of Goldman Sachs (2009),

these countries have 47 percent of world GDP by 2050.

The developing financial market among BRICS nations entails that those counties universally becoming

significant center for " direct and portfolio investments". In the last decade, the BRICS's capital markets

have grown in a rapid manner together with economic development. The intensity of equity markets is

measured in terms of market capitalization to GDP in BRICS markets continuously expands over the times.

In addition, during the period 1999-2010, capital markets in such nations experienced movements and also

increased importantly; however, such growths are different within the union (Venkatesh, 2013). For

example, Brazil's share of world market capitalization was very low about 4 percent of GDP in 1999,

reached a high of 74 percent in 2010. The corresponding ratio in the same period with respect to India was

from 4 percent to 93 percent, Russia and China from almost nothing to 70 and 81 percent, respectively.

South Africa has the largest market capitalization to GDP; it was more than double from 123 percent to 278

percent (Moe et al, 2010).

The remaining of this paper is organized as follow. Review of literature is summarized in section 2. In

section 3, econometric mythology and the data are presented. Results and discussion are presented in

section 4. The last section concludes the paper.


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Literature Review

There are a number of literatures which investigates the relationship between stock market and exchange

rate. They supported a long-runing relation between both variables. However, there is also evidence

indicating the opposite. On the other hand, the finding of number researches has not relation between these

variables.

Hatem-J and Roca (2005) used daily data of the first seven months of 1997 before the Asian financial crisis

and also data from the rest of the year in countries such as Malaysia, Philippines, Indonesia and Thailand.

They used bootstrap causality tests with leverage adjustments for analysing data. They find that the relation

between both variables are highly strong before the Asian crisis. However, the relationship for both

financial variables stopped in each country during the Asian crisis.

Morales (2007) used Johansen co-integration, standard Granger causality and Vector Error Correction to

analyse exchange rates and stoke prices from four Eastern European countries, such as Czech Republic,

Poland, Hungary and Slovakia, also, concluded the stock prices from Germany, United Kingdom and the

United States. Morales employed daily data from 1999 to 2006, with analysing long run and short run

between exchange rates and stoke prices. It fund that there is no relation between either financial variables

in the short-run or long-run in Poland, Czech Republic and Hungary with exception of Slovakia. Another

study about Eastern European countries from Greece, Czech Republic and Hungary has different results. It

used multivariate co-integration for analysing weekly data from 1 January 1994, to 28 February 2000. This

study found evidence of relationship between stock markets and exchange rates in Hungary and Czech

Republic (Grambovas, 2003).

The 2015 report by Vanita and Khushboo is one of the few literatures about growth of countries in BRICS

and analysing the long-run co-integrating relationship between exchange rate and stock prices. They

employed Johansen co-integration to analyses daily data of 17 years from 1997 to 2014. They found that

exchange rate and stock price are very close in Russia and China. Furthermore, significant and negative

relationship between stock prices and exchange rate was also found in India, South Africa and Russia.

Smyth and Nandha (2003) investigate the bivariate causality between exchange rates and stock prices using

Johansen co-integration, Granger and Engle and taking the daily data from India, Bangladesh, Pakistan and

Sri Lanka into consideration focusing on the period between 1995 and 2001. They found that there is no

long run relationship between exchange rate and stock prices in any of the countries selected. Kutty (2010)

examined relationship between exchange rate and stock prices in Mexico. The Granger causality test was

used to analyses weekly data from January 1989 to December 2006. This study found that no long run

relation between both financial variables. Tabak (2006) examined the dynamic relationship between stock

price and exchange rate in Brazil. Co-integration of Engle and Granger is employed to analyse daily data

from August 1994 to May 2002. They found the negative relation from stock price to exchange rate and

that there is no long run relation between both variables.

Okpara and Odionye (2012) investigates the relationship between the state of the two variables mentioned

above using co-integration, Granger Causality and vector error correction model to interpret the data from

1990 to 2009. They found that the relation between both financial variables is long run, and the Granger

Causality finds strong unidirectional from stock price to exchange rate. Jamil and Naeem (2013) this

studies that investigated impact of foreign exchange rate on stock prices in Pakistan. They used the co-

integration technique to analyses monthly data from 1998 to 2009, obtained from Karachi Stock Exchange.

They found that the relation between exchange rate and stock price is short run.

Nieh and Lee (2001) examined the dynamic relationship between Stock Prices and Exchange Rates in

Canada, France, Italy, Japan, US and The UK. They employed co-integration test and Engle-Granger two

step to analyses daily data from 1 October 1993, to 15 February 1996. They found short run relationship


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between exchange rate and stock prices for one day. However, there is no long-run relationship between

both variables in the G-7 that is the same finding of another research conducted by Morley and Pentecost

(2000). Ali, Mukhater and Maniam (2015) investigated dynamic links between exchange rates and stock

prices in Malaysia. They employed co-integrate and Engle-granger to analyse monthly data from 1999 to

2014. The main finding that there is a long run relationship between exchange rate and stock prices in

Malaysia. Moreover, there are numbers of studies supported that no long relation between exchange rate

and stock markets such as Philaktis and Ravazzolo (2000), Zia and Rahman (2011) and Subair and Salihu,

(2004). Rahman and Uddin (2009) examined the relationship between exchange rate and stock price in

Bangladesh. They used Johansen, Granger causality test and co-integrated to analyse data from June 2003

to March 2008. They found that there is no any relationship between both financial variables.

Empirical strategy and Sources of Data

Data sources and time period

To establish the relationship between stock price and exchange rate, this study utilizes monthly data for

Stock Prices (SP) and Real Exchange Rate (RER) of BRICS (Brazil, Russia, India, China and South Africa)

countries. The sample period for BRIC starts from January 2000 to Jun 2016, total of 198 observations.

However, the data for South Africa is taken from April 2004 to Jun 2016, the yielding a total of 147

observations. The Stock price series for all nations are collected from (investing.com), and expressed in

national currency. Real Exchange rate data are sourced from (stats.oecd.org), and represent the amount of

US dollars per one unit of local currency. All variables have been converted in to natural logarithm. For the

purpose of this study, we define SP to be the stock price index, and RER to be the exchange rate for the all

countries. The data was examined through the STATA software. In addition, the below table provides the

equity market and benchmark index which was selected for each country.

Table 1: The selected BRICS Stock Exchange

Country Stock Market Stock index

Brazil Sao Paulo Stock Exchange Ibovespa

Russia Moscow Stock Exchange RTSI INDEX

India National Stock Exchange CNX NIFTY

China Shanghai Stock Exchange Shanghai SE Composite

South Africa Johannesburg Stock Exchange FTSE/JSE Top40

Methodology

In order to analyses and find out the causal relationships between variables, a methodological section is a

significant segment of the process for each study. Therefore, similar to other studies, empirical analysis is

conducted to estimate the dynamic link between stock prices and real exchange rates in BRICS economics.

Furthermore, several tests such as the Unit root, Johansen co-integration, Vector Error Correction Model

with the Granger Causality tests are performed for further investigation as follows:

Unit root test

When time series is not stationary there is tendency to observe problem with the data, therefore a unit root

test is significant to be used, in order to check the stationary of variables and the order of integration,

(Granger and Newbold, 1974). If a variable is non- stationary, its mean and variance are not fixed over a

period of time, and an observation is correlated with its more recent lags, (Gujrati, 2004).

Augmented Dickey-Fuller (ADF) (Dickey and Fuller 1981), Phillips Perron (PP) (Philips and Perron, 1988)

and Kwiatkowski, Philips, Schmidt, Shin (KPSS) (Kwiatkowski et al, 1992) - constant, without and with


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the trend are often used to examine the presence of a unit root test in the series. However both (ADF and

PP) tests are in somehow criticized that they are fail to reject a unit root might be attributed to their low

power. Therefore, (KPSS) test is added here since null hypothesis is stationary for each series in levels. .

This test is carried out as a complementary process to the ADF test. Hence, In order to check if the time

series is stationary or not, this study uses unit root tests on log levels and the first differences of variables.

Johansen Co-integration Test

Co-integration1 is a well-known methodology which is used to analysis the existence of an equilibrium

association between two data series, (Johansen, 1988). Two or more variables can be independently not

stationary, whereas, some "linear combination" of time series under consideration can have constant

properties and thus might be co-integrated. Furthermore, Johansen co-integration tool conduct two statistics

test; such as the maximal eigenvalue and the trace tests. These statistics are utilized to detect the number of

cointegrating vectors between two or more variables.

1. Johansen co-integration test titled after Søren Johansen. It is a technique for testing co-movement of

several data series.

There exist arguments about utilizing data series in level rather than the difference when performing

Johansen Co-integration test. For example, Plosser and Schwert (1978) argue that time series regression

may produce robust correlations with high R2

when computed using economic variables in level. But if

same model utilizes variable with differences the relationship may become insignificant. However, some

other discuses that when time series variables are not stationary, utilizing levels can result in non-constant

mean over a period of time and residuals which are highly auto-correlated with low Durbin-Watson

statistics, (Granger and Newbold 1974; Plosser and Schwert 1978; and Griffiths, Hill and Carter 1993).

Therefore, they recommend performing difference of each economic variable until they become stationary

before using econometric analysis. Accordingly this paper implements last suggestion. Once the co-

integration is determined in examining the Johansen integration; the Vector Error Correction Model

(VECM) will be conducted to check the dynamic association between selected variables. Mainly, the

VECM reveals that the current period's change depends on the previous period's change, hence supplying a

measure of how far the procedure is out of "long-term equilibrium". Further, based on Akaike Information

Criteria (AIC), the optimal lag length of 4 was chosen.

Granger Causality

The Granger causality test (Granger, 1969) was developed as a more efficient approach to identify whether

historical information of a variable could help with forecasting the effects on some other variables. It is also

increasingly used as an approach for determining directed functional connectivity in neutral time-series

data.

The test involves estimating the following simple vector auto-regressions (VAR):

Where is difference operator, refers for natural logarithm are the constant,

are coefficient for the lagged regressors of the dependent variable, are coefficient for the

lagged regressors of the independent variable; are error random terms. and are


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Stock prices and Real Exchange Rate respectively. Akaike Information Criteria (AIC) are utilized in

selecting the optimal lag length of 4. In this study Granger causality test applied at first difference for all

variables, we perform such a test in order to determine the direction of causality (unidirectional causality:

causes or causes ; bilateral causality: both and causes each other).

Empirical Results and Discussion

In this section, initially begin by analyzing the behavior of the data series. Table 2 presents the summary

statistics of stock prices and exchange rates of BRICS countries. The returns are investigated by taking the

first difference of the natural log of the series. The analyses reveal that Brazil stock market represents mean

monthly return of (10.4551%) that records the highest mean monthly return while Russian stock exchange

indicate the lowest mean return among the determined stock indices, which is (6.738121) %. Further, the

India has the largest average exchange rate (3.894151%); followed by Russia and South Africa.

Conversely, the Brazil has the lowest average exchange rate among the countries over the sample period.

The Russia's stock market is highly volatile (0.7533815%) followed by India (0.7062275%) while China

has least volatile equity markets and exchange rates (0.3670446%) and (0.1206579%), respectively among

the sock markets and exchange rates of BRICS. The analyses show evidence that except for China all the

equity markets data is negatively skewed.

Table 2: Summary Statistics

Panel A: Stock Prices

Kurtosis Skewness Variance Std. Dev. mean Observation Country

1.979508 -0.6792687 0.3778838 0.6147225 10.4551 198 Brazil

2.464675 -0.7689939 0.5675837 0.7533815 6.738121 198 Russia

1.722072 -0.4141736 0.4987573 0.7062275 8.083807 198 India

2.727493 0.3076722 0.1347217 0.3670446 7.676874 198 China

2.700964 -0.5761936 0.1887726 0.4344797 10.16465 147 South Africa

Panel B: Real Exchange Rates

Kurtosis Skewness Variance Std. Dev. mean Observation Country

5.145948 1.039174 0.0647279 0.2544168 0.8270425 198 Brazil

7.011437 2.146841 0.0608843 0.2467473 3.456944 198 Russia

2.752511 0.8910182 0.0192859 0.1388737 3.894151 198 India

1.296702 -0.0736967 0.0145583 0.1206579 1.979601 198 China

2.936387 0.8708247 0.0621891 0.2493775 2.119615 147 South Africa

Unite Root Test Results

Table 3 reports the result of the unit root tests. It shows that apart from exchange rate of Brazil which its p-

value in ADF and PP tests is less than 5 percent, both real exchange rate (RER) and stock prices (SP) are

non-stationary in level. This explains that we fail to reject the null hypothesis in level. However, when the

ADF, PP, and KPSS tests are implemented to the first difference of the variables; the result shows that all

the variables are stationary. In another word, the null hypothesis of no unit root was accepted for all the

series. Thus, according to the unite root analysis we conclude that all the series are integrated of order one,

I (1) when they are differenced.


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Table 3: Unit Root Test Results for the series

ADF PPERRON KPSS

Log Level Intercept

Countries Variables t-statistics p-value t-statistics p-value t-statistics

Brazil Stock price -1.141 0.6985 -1.141 0.6985 15.5

Exchange Rate -4.202 0.0007 -4.202 0.0007 2.41

Russia Stock price -2.127 0.2337 -2.127 0.2337 11.7

Exchange Rate 0.981 0.9941 0.981 0.9941 7.85

India Stock price -0.535 0.8850 -0.535 0.8850 18.1

Exchange Rate 0.374 0.9805 0.374 0.9805 11.3

China Stock price -1.591 0.4882 -1.591 0.4882 6.78

Exchange Rate -0.875 0.7960 -0.875 0.7960 19.1

South Africa Stock price -1.872 0.3455 -1.872 0.3455 12.2

Exchange Rate 0.351 0.9796 0.351 0.9796 10.5

Intercept and trend

Brazil Stock price -1.277 0.8934 -1.277 0.8934 3.45

Exchange Rate -4.190 0.0046 -4.190 0.0046 2.25


Exchange Rate -0.242 0.9907 -0.242 0.9907 3.03


Exchange Rate -0.818 0.9642 -0.818 0.9642 3.92


Exchange Rate 0.611 0.9970 0.611 0.9970 2.05


Exchange Rate -1.513 0.8247 -1.513 0.8247 1.76

First Differences

Intercept

Brazil

Stock price -13.327 0.0000 -13.327 0.0000 0.154

Exchange Rate -22.563 0.0000 -22.563 0.0000 0.0252


Exchange Rate -8.441 0.0000 -8.441 0.0000 0.533

India Stock price -13.510 0.0000 -13.510 0.0000 .102

Exchange Rate -10.138 0.0000 -10.138 0.0000 0.336


Exchange Rate -7.871 0.0000 -7.871 0.0000 1.07


Exchange Rate -9.555 0.0000 -9.555 0.0000 0.229

Intercept and trend

Brazil

Stock price -13.301 0.0000 -13.301 0.0000 0.106

Exchange Rate -22.509 0.0000 -22.509 0.0000 0.022


Exchange Rate -8.554 0.0000 -8.554 0.0000 0.104


Exchange Rate -10.189 0.0000 -10.189 0.0000 0.0909


Exchange Rate -7.903 0.0000 -7.903 0.0000 1.01


Exchange Rate -9.640 0.0000 -9.640 0.0000 0.0573


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Johansen Co-integration Results

Co-integration between stock prices and real exchange rates for all BRICS countries has been examined

performing Johansen Method. The result reported in table (4), based on Johansen maximum eigenvalue and

trace statistics tests. Both indicate two co-integrating vector between the variables at 5% significance level,

which explains that stock prices and exchange rates are co-integrated. This consistent with the finding of

(Jamil and Ullah, 2013), who also find the evidence of two co-integration when they analyze the "impact of

foreign exchange rate on Pakistan stock price". These indicate rejection of null hypothesis of no co-

integration and acceptance of the alternative of co-integration at 5% significance level. Hence, the

consequence reveals that there exist stable long-run correlation between the stock prices and real exchange

rates for all nations.

Table 4: The Johansen Test Result

Country Hypothesized No

of (E(s

Trace statistics Critical value Max

eigenvalue

Critical value

Brazil None 132.6960 15.41 94.2317 14.07

At most 1 38.4643 3.76 38.4643 3.76

Russia None 95.1424 15.41 58.5846 14.07

At most 1 36.5578 3.76 36.5578 3.76

India None 95.1776 15.41 60.0974 14.07

At most 1 35.0802 3.76 35.0802 3.76

China None 37.0377 15.41 25.0861 14.07

At most 1 11.9516 3.76 11.9516 3.76

South

Africa

None 53.0980 15.41 30.7017 14.07

At most 1 22.3963 3.76 22.3963 3.76

Vector Error Correction Model (VECM) Result

Since the variables are co-integrated, the vector error correction model (VECM) is analyzed. The VECM

allows long-term behavior of dependent variables to gather long-run association when allowing a large

range of short-term movement.

The coefficient of BRICS's exchange rate is negatively and insignificantly influence on their stock prices;

however the effect is significant with lag 1 for Russia, India and South Africa; and also with lag 2 for China

(see table 4a). This explains that the short-run effect of exchange rate movement in the past 1 and 2 month

respectively has significant effect on the BRICS's stock prices. However, the impact is positive with lag 1

and 2 for Brazil and Russia, respectively and with lag 3 for India, China and South Africa. Similarly results

obtained in table 4(b) show that equity prices of BRICS negatively impact on real exchange rate but the

effect is positive with lag 1 for Russia and India; and with lag 1 and 2 for China. Further, stock price of

Brazil and South Africa with lag 2; and India with Lag 3 can explain exchange rates. Furthermore, the

VECM result shows that there exisit a significant negative long-term association between stock prices and

exchange rate of Russia. The effect is positive in lag2. Nonetheless, exchange rates of Brazil, India, China

and South Africa positively but insignificantly impact on their Stock prices (see table 5a). Besides, the

VECM result reveals that there is an insignificant negative long-run linkage between equity prices and

exchange rate of China. Yet, share prices of Brazil, India, Russia and South Africa positively impact on

their exchange rate. The consequence for India and South Africa is significant.

The coefficient of the error correction term of Brazil's stock price has positive sign. It implies that due to

any disturbance in the system, difference will take place from equilibrium and the procedure will be

unstable. The vector error correction term for share price of Russia is (-0.00019). It explains the stability

of the system and converges towards equilibrium in the case of any disturbance in the system. The vector


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error correction term for SP of India is (-0.005). This speed of adjustment suggests that about 0.5% of the

previous period’s disequilibrium in stock market corrected every month. It confirms the stability of the

system. For China's stock price the speeds of adjustment are (-0.051). For SP of South Africa, the speeds of

adjustment are (-0.031).

Table 5a: Variables included the VECM: Stock prices and Exchange rates.

Country Variable in log Coefficient Std. Err. Probability

Brazil

Cointeg. 0.0041902 0.0022271 0.060

∆SP-1 0.092673 0.0769512 0.228

∆SP-2 0.0510365 0.0777795 0.512

∆SP-3 -0.0372913 0.0798655 0.641

∆RER-1 0.0503221 0.049078 0.305

∆RER-2 -0.028582 0.0509655 0.575

∆RER-3 -0.0727299 0.0453654 0.109

cons 0.009347 0.0057025 0.101

Russia

Cointeg. -0.0001904 0.0024168 0.937

∆SP-1 0.1192382 0.0730689 0.103

∆SP-2 -0.0347968 0.071639 0.627

∆SP-3 -0.0068766 0.0693295 0.921

∆RER-1 -1.213042 0.219621 0.000

∆RER-2 0.4031482 0.2537145 0.112

∆RER-3 -0.460676 0.2375717 0.052

cons -0.0000442 0.0085573 0.996

India Cointeg. -0.0046998 0.0076542 0.539

∆SP-1 -0.0776933 0.07354 0.291

∆SP-2 -0.0103687 0.0735827 0.888

∆SP-3 0.106327 0.070108 0.129

∆RER-1 -1.531634 0.2875092 0.000

∆RER-2 -0.3000119 0.3186372 0.346

∆RER-3 0.3882547 0.3120073 0.213

cons 0.0053615 0.0119045 0.652

China Cointeg. -0.0507641 0.0203482 0.013

∆SP-1 0.1206278 0.0723048 0.095

∆SP-2 0.1659467 0.0722813 0.022

∆SP-3 0.0424463 0.072874 0.560

∆RER-1 -0.1872342 1.482828 0.900

∆RER-2 -3.691063 1.660953 0.026

∆RER-3 1.897617 1.52219 0.213

cons 0.000015 0.0061215 0.998

South Africa Cointeg. -0.031057 0.0156618 0.047

∆SP-1 -0.133628 0.0872493 0.126

∆SP-2 0.1035423 0.0884869 0.242

∆SP-3 0.1604462 0.0882699 0.069

∆RER-1 -0.2373977 0.1119737 0.034

∆RER-2 -0.0984212 0.112555 0.382

∆RER-3 0.1592034 0.1147316 0.165

cons 0.0110203 0.0046558 0.018

In addition, the coefficient of the variables is shown when exchange rate is dependent variable (For

exchange rate), the speed adjustment is (-0.074), (-0.011), (-0.0033) and (-0.049) for Brazil, India, China


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and South Africa, respectively (see table 5b). These error correction terms suggest high speed of

adjustment. This implies that exchange rate and stock prices adjust to their stable long run equilibrium

relationship. However, the error correction term for Exchange rate of Russia is (0.0005). This is wrongly

signed implying that exchange rate is neutral in the short run.

Table 5b: Variables included the VECM: Exchange rate and Stock prices.

Country Variable in log Coefficient Std. Err. Probability

Brazil

Cointeg -0.07387 0.043896 0.092

∆RER-1 -0.5952401 0.0842591 0.000

∆RER-2 -0.3827075 0.0874997 0.000

∆RER-3 -0.1559582 0.0778853 0.045

∆SP-1 -0.1232734 0.1321131 0.351

∆SP-2 -0.4569031 0.1335351 0.001

∆SP-3 -0.1868655 0.1371164 0.173

cons 0.0060867 0.0097903 0.534

Russia

Cointeg 0.000488 0.0061955 0.937

∆RER-1 0.5291937 0.0734218 0.000

∆RER-2 -0.1537557 0.0848196 0.070

∆RER-3 -0.2009386 0.0794229 0.011

∆SP-1 0.0035658 0.0244277 0.884

∆SP-2 -0.0342394 0.0239497 0.153

∆SP-3 -0.0459457 0.0231776 0.047

cons 0.0040435 0.0028608 0.158

India

Cointeg -0.0111062 0.0060456 0.066

∆RER-1 0.3317564 0.0724203 0.000

∆RER-2 -0.0501833 0.0802611 0.532

∆RER-3 -0.0662752 0.0785911 0.399

∆SP-1 0.0106461 0.0185239 0.565

∆SP-2 -0.0173666 0.0185346 0.349

∆SP-3 -0.0485503 0.0176594 0.006

cons 0.0071143 0.0029986 0.018

China

Cointeg -0.0490967 0.016484 0.003

∆RER-1 0.4909769 0.072764 0.000

∆RER-2 -0.107676 0.0815048 0.186

∆RER-3 0.1639513 0.0746955 0.028

∆SP-1 0.0030252 0.0035481 0.394

∆SP-2 0.0005443 0.0035469 0.878

∆SP-3 -0.0026517 0.003576 0.458

cons -0.000474 0.0003004 0.115

South Africa

Cointeg -0.0490967 0.016484 0.003

∆RER-1 0.2329403 0.0867872 0.007

∆RER-2 -0.2242467 0.0872378 0.010

∆RER-3 -0.0096526 0.0889248 0.914

∆SP-1 -0.0707816 0.0676242 0.295

∆SP-2 -0.1670061 0.0685833 0.015

∆SP-3 -0.0714258 0.0684152 0.296

cons 0.0094663 0.0036086 0.009


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Granger Causality test result

The direction of causality between BRICS's stock prices and foreign exchange rates was tested by utilizing

Granger causality test. The results are reported in table 6. The null hypothesis of no granger causality was

examined against the alternate that a direction of granger causality exists among the series. According to

the table, exchange rate of Brazil granger causes its stock price but not vise versa. This reveals an active

unidirectional relation running from exchange rate to stock price. In addition, exchange rates of Russia and

South Africa have been caused by their stock prices while real exchange rates of such countries do not

granger cause their stock prices. This demonstrates a robust unidirectional causality moving from stock

prices to exchange rate. This outcome supports the "Stock Oriented Model" which is talked about in section

1, additionally verified the study of Okpara and Odionye (2012), who also finds strong unidirectional

causality running from stock price to exchange rate in Nigeria. In China there is no granger causality

running from stock price to exchange rate, and not vice versa. However, there exist bidirectional causality

between stock price and exchange rate of India. This signifies that both stock price and exchange rate

influence each other in India. Thus, the change in real exchange rate could be a critical instrument to

forecast and/or effect future shifts in the share prices and real economic sector. Thus, the all results proof

bilateral and unilateral association between the series while during the whole period of the study, no

interaction between variables was determined in the case of China.

Table 6: Granger Causality Test

p- value F-statistics Independent Variable Dependent Variable Country

0.1037 1.9519 ∆lnRER ∆lnSP Brazil

0.0010 4.8059 ∆lnSP ∆lnRER

0.0000 8.9468 ∆lnRER ∆lnSP Russia

0.0939 2.0168 ∆lnSP ∆lnRER

0.0000 8.8284 ∆lnRER ∆lnSP India

0.0205 2.9794 ∆lnSP ∆lnRER

0.0606 2.2986 ∆lnRER ∆lnSP China

0.7610 0.46554 ∆lnSP ∆lnRER

0.0409 2.5696 ∆lnRER ∆lnSP South Africa

0.3540 1.1112 ∆lnSP ∆lnRER

Conclusion

This study investigates the dynamic relationship between stock market and exchange rate in BRICS

economics (Brazil, Russia, India, China and South Africa). We employed the secondary date from January

2000 to Jun 2016 www.investing.com . In addition, to analyse data based on Unit root, co-integration,

Vector Error Correction Model and Granger Causality test. The result from co-integration test there is long

run relationship between both variables. Some of studies are finding the long run relationship between

exchange rate and stock market such as Ali, Mukhater and Maniam (2015) and Okpara and Odionye

(2012). However, there are number of studies supported short run relationship between both financial

variables such as Jamil and Naeem (2013), Nieh and Lee (2001) and Kutty (2010). From VECM the

coefficients of BRICS’s exchange rate is negatively and insignificantly influence on their stock prices;

however the effect is significant with lag 1 for Russia, China and South Africa; and also with lag 2 for

china. Also, the result from Granger Causality result indicate that Real exchange rates of Russia and South

Africa have been caused by their stock prices while real exchange rates of such countries do not granger

cause their stock prices. This indicates a robust unidirectional causality running from stock prices to

exchange rate.

http://www.investing.com/


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