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ISSN : 2288-4637(Print)
ISSN : 2288-4645(Online)
The Journal of Asian Finance, Economics and Business Vol.4 No.1 pp.25-38
DOI : http://dx.doi.org/10.13106/jafeb.2017.vol4.no1.25

Multivariate Causal Relationship between Stock Prices and Exchange Rates in the Middle East

Parham Parsva1, Hooi Hooi Lean2
1First Author and Corresponding Author. Ph.D. Student, Faculty of Economics, Allameh Tabataba'i University [Shahid Beheshti Ave. 15134, Tehran, Iran]
2Co-Author. Professor, Economics Program, School of Social Sciences, Universiti Sains Malaysia [11800 USM, Penang, Malaysia]
*Corresponding Author. Parham Parsva. E-mail: parham.parsva@gmail.com
December 28, 2016. February 2, 2017. February 5, 2017.

Abstract

This study investigates the causal relationship between stock prices and exchange rates for six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia before and during (after) the 2007 global financial crisis for the period between January 2004 and September 2015. The sample is divided into two sub-periods, that is, the period from January 1, 2004 to September 30, 2007 and the period from October 1, 2007 to September 30, 2015, to represent the pre-crisis period and the post-crisis period, respectively. Using Vector Autoregressive (VAR) model in a multivariate framework (including two control variables, inflation rates and oil prices) the results suggest that in the case of Jordan, Kuwait and Saudi Arabia, there exists bidirectional causalities after the crisis period but not the before. The opposite status is available for the case of Iran. In the case of Oman, there is bidirectional causality between the variables of interest in both periods. The results also reveal that the relationship between stock prices and exchange rates has become stronger after the 2007 global financial crisis. Overall, the results of this study indicate that fluctuations in foreign exchange markets can significantly affect stock markets in the Middle East.

JEL Classification code: C01, F31, G01, G12.

초록


 1. Introduction

In an open economy, the impact of unexpected changes in exchange rates on the present value of a firm’s assets, liabilities and cash flows exposes the economic value of the firm to exchange risk. This implies that exchange rates play a significant role in the movements of stock prices.

The results of the investigation on the relationship between stock prices and exchange rates are important because of two reasons: First, the relationship between exchange rates and stock prices is able to influence the development of capital markets, particularly in those emerging and newly industrializing countries which have recently attracted the attention of the international investors. In fact, the awareness of the relationship between these two financial markets would help the domestic and international investors to hedge and diversify their portfolios (Abdalla & Murinde, 1997). Second, it is believed that the 1997 Asian financial crisis, which started as an exchange rate crisis in Thailand and then led to the depreciation of other currencies in the region, resulted in the collapse of the stock markets (Hatemi & Roca, 2005). In such a scenario, understanding and foreseeing the relationship between stock prices and exchange rates might enable policymakers to formulate appropriate policies before the spread of the crisis. On one hand, "exchange rates are often the easy target for policy intervention and therefore it is crucial to know how exchange rates will affect other asset markets and on the other hand, when asset markets are under stress, volatility will be higher and returns will be lower"(Hatemi & Roca, 2005, p. 540). 

Furthermore, it is believed that the 2007-2008 global financial crisis influenced the Middle Eastern stock exchange markets through exchange rate fluctuations and oil price hike, as oil industry holds major share of the whole shares in the region. Thus this study investigates the causal relationship between stock prices and exchange rates, for six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia before and during the 2007 global financial crisis for the period between January 2004 and September 2015.

The rest of the paper is organized as follows. The next section explains the theories behind the study and reviews the literature of the study and some of the past studies. Section 3 describes the sources of data and types of econometrics methods are employed in the study. In section 4 the empirical results are presented and finally some of the concluding remarks and policy implications are provided in section 5.

"In spite of fast economic growth of the Middle East, less attention has been paid by researchers and investigators to the region compared with other emerging markets in the Europe, Asia, etc" (Parsva, 2012, p.31).For example, Saudi Arabia’s stock exchange had a market capitalization of 247 billion U.S. dollars in 2008 and ranked 20th in the world. The United Arab Emirates (UAE), Qatar, and Bahrain are among the top fifty major financial centers globally in terms of competitiveness. Dubai has been one of the important and stable places for financial investors to establish new companies over the past decade. Jordanian Shaheen Business & Investment Group is another example which benefits from Jordan’s free trade agreement with the U.S. (Cheng, Jahan-Parvar, & Rothman, 2010).

Geography, political conflicts, and direct effect of oil price changes on the economy of the Middle East are the most special characteristics of the region.

1. Geography: The simplest geographical definition of the Middle East is the region where Asia, Africa, and Europe meet (Roskin & Coyle, 2008). Most of the countries which are located in the Middle East have quite similar societies, cultures, economies, religions, politics, etc.

2. Political crisis: It is worth noting that high financial market returns have been realized while the Middle East has experienced major political and security instabilities such as the civil war in Iraq, high tension between Israel and Palestine, etc. (Cheng, Jahan-Parvar & Rothman, 2010).

3. Oil factor: The Middle East is the largest oil-producing region in the world and holds about two-thirds of the earth’s proven oil reserves. Thus, the area has been the center of attention over the past decades. The Persian Gulf states are among these countries; therefore the occurrence of any oil price shock has a crucial and vital effect on the economies of these countries.

2. Theoretical Framework and Literature Review

 There are two theoretical backgrounds on whether or not exchange rate impacts on stock prices and vice versa. They are traditional and portfolio-balance approaches that are explained below.

2.1. Traditional Approach

The microeconomic level suggests that exchange rates lead the stock prices (Dornbusch & Fischer, 1980; Aggarwal, 1981; Wu, 2000; Yau & Nieh, 2006), so the first perspective is the traditional approach which states any change in exchange rates would lead to changes in the stock prices.

Typically, the reason behind the occurrence of the traditional approach is the “flow-oriented” or “goods-market” model which has been developed by Dornbusch and Fischer (1980). The model emphasized the behavior of exchange rate and current account. The authors started by setting out a basic model for equilibrium of money market. When the value of the local currency appreciates, exporting firms lose profit and the value of equities of these firms will reduce. On the contrary, the status of importing firms is absolutely different since the value of them would benefit from an appreciation of the local currency. Stock prices would be affected by exchange rate (currency) movement since in general stock prices are the present value of future cash flows of the firms. The result of their study demonstrated that any change in exchange rates would have an influence on the international competitor firms and their trade balance.

Aggarwal (1981) investigated the relationship between trade-weighted value of the U.S. dollar and stock returns for the period starting from 1974 to 1978. He pointed out that there is a positive correlation between these variables. However, Soenen and Henningar (1988) found an inverse relationship between stock prices and exchange rates.

2.2. Portfolio-Balance Approach

The second theoretical framework refers to stock market performance (Mishra, 2004). The portfolio-balance approach, which also evaluates the linkage between stock prices and exchange rates, is one way and states that exchange rates are determined by the market mechanism. In other words, the "portfolio theory focuses on the important role of the capital account transaction which uses to determine exchange rate dynamics" (Parsva & Lean, 2011, p.157-158).Branson (1981) and Frankel (1983) developed the “stockoriented” model. The model presents the capital account as a main factor in determining exchange rate dynamics. As the stock prices diminish, domestic wealth is reduced and subsequently the demand for domestic money and interest rate decreases. In addition, foreign investors demand for local currency declines and finally the currency depreciates. Gavin (1989) also expressed that any change in stock prices could cause the realignments of exchange rates. 

The value of a firm’s equity increases while the prices of new company equipment remain unchanged in the short run. As a result, investment is relatively cheaper and companies tend to invest more. Thus,

 

where I is investment, SP is stock prices, and R is the borrowing/ lending interest rate, which has a negative impact on investment because it makes the investment funding more costly.

Dimitrova (2005) has explored theoretically that an increase in stock prices will positively affect the value of financial assets held by households, leading to an increase in household wealth and therefore consumption. As people associate higher wealth with lower probability of financial distress, they are likely to hold more illiquid assets. This is consistent with higher expenditure both domestically and internationally, which means imports rise and the current account balance worsens. Therefore, the government or monetary authorities would initiate to prevent further dependency on imports by depreciating the domestic currency.

This study aims at reducing the omitted variable bias in the literature through encompassing two relevant variables, oil price and inflation rate, into the model. Adding oil price to the model is due to the key and vital role of the crude oil in the economy and national income of most Middle Eastern nations. Inflation rate also is included into the model because the economy of the area has been suffered over the past decade by the high rate of inflation. Moreover, according to Wu's (2000) and Kim's (2003) findings, any changes in the inflation rate can lead to traditional approach occurrence, which has been explained earlier

As mentioned earlier, knowing any interaction between stock market and foreign exchange market is very important. It has captured the attention of economists so far and it is still ongoing. However, despite a growing theoretical and empirical literature, it still lacks details on this important issue.

Some of the past studies on this issue are listed as below: Frank and Young (1972) investigated the stock prices reactions of some multinational firms to the real realignments of exchange rate. They employed six major currency realignments and used two American equity indices such as the Dow Jones and S&P Industrials. They found no relationship between stock prices and exchange rates, and interestingly stated that a manager would be unwise if he makes decisions based on the exchange rate realignments.

Solnik (1987) was the first investigator who used financial prices such as stock prices, instead of the traditional macroeconomics data, for evaluating how stock returns can be used to test exchange rate models. He employed the monthly and quarterly data for the period from July 1973 to December 1983 and chose the stock markets of eight countries, Canada, France, Germany, Japan, the Netherlands, Switzerland, UK, and US. He finally found that there was a weak positive relationship between stockreturns differentials and changes in the real exchange rate. 

2.3. Oil Price Shocks, Exchange Rates and Stock Prices in the Oil-Exporting Countries

The situation in oil-exporting countries is quite different from oil-importing countries when oil price changes. Oil price changes can impact on the economy of oil-exporting countries in two ways. First, a higher oil price leads to an immediate transfer of wealth from oil importers to oil exporters. If oil exporting countries use this wealth to create more investment opportunities in the overall economy, the demand for labour and capital will increase and consequently, a higher level of activity and employment in these countries would be generated. Second, as the oilimporting countries suffer from an increase in oil price, their demand for imported goods and services from oil-exporting countries diminishes and this probably has a negative impact on the economy of oil-exporting countries (Bjornland, 2009).

 

Bjornland (2009) analyzed the effect of oil price shocks on stock returns in Norway, an oil-exporting country. She found that a higher oil price has a very positive effect on the Norwegian economy. She further concluded that an oil price hike can increase the value of equities and appreciate the value of the domestic currency. This would increase the wealth of the economy and help to decrease the rate of unemployment.

The Middle East plays a very important role in producing and exporting crude oil in the world. Moreover, most countries in the region have oil-based economies and their oil sectors provide most of their GDP and total exports. Therefore, several empirical studies have been done on Arab oil-exporting countries in the Middle East as well as Iran. Numerous studies investigated the effects of oil price on the macroeconomic variables in these countries.

Jahan-Parvar and Mohammadi (2008) evaluated the relationship between oil prices and real exchange rates in a sample of fourteen countries to test the validity of the “Dutch disease” hypothesis. The hypothesis states that "a higher oil price in an oil-exporting country may cause an appreciation of the real exchange rate, reduces the competitiveness in the non-exporting sector and limits its ability to build a diversified exports base" (Jahan-Parvar & Mohammadi,2008, P.2). In agreement with this hypothesis, their empirical study confirmed the existence of stable long-run linkages between oil prices and real exchange rates in all sample countries.

Rault and Arouri (2009) investigated the relations between oil prices and stock prices in the GCC countries1 .Their results indicated that there is cointegration of oil prices and stock prices in the GCC countries and the oil price increase has a positive effect on raising the value of equities.

2.4. Review of Previous Empirical Studies

Ma and Kao (1990) investigated the impact of appreciation of currency on the domestic stock market. They employed the monthly stock indices and exchange rates from January 1973 to December 1983 of six major industrialized countries, i.e. the UK, Canada, France, West Germany, Italy, and Japan. They pointed out that the relationship between exchange rates and stock prices depends on the export/import-dominant country. In other words, the appreciation of currency has a positive (negative) effect on the stock market for an import-dominant (exportdominant) country.

Bahmani-Oskooee and Sohrabian (1992) are among the first to use econometrics techniques such as Granger causality and cointegration to analyze the linkage between stock prices and exchange rates. Their sample included the monthly observations over the period of July 1973 to December 1980, and they also used effective exchange rates of the U.S. dollar and S&P 500 index to represent the stock price. According to their Granger causality result, there is a dual causal relationship between stock prices and exchange rates at least in the short-run. Based on cointegration result, they found no linkage between the two variables.

Bartov and Bodnar (1994) focused on empirical exploration of the relationship between abnormal stock performance and contemporaneous and lagged changes in the value of the U.S. dollar for a set of firms. They found that contemporaneous changes in the U.S. dollar have little impacton abnormal stock returns. However, a lagged change in the U.S. dollar is negatively associated with abnormal stock returns.

Nieh and Lee (2001) employed both the Engle-Granger test and the Johanson maximum likelihood cointegration test to investigate the dynamic relationships between stock prices and exchange rates for the G7 countries namely, Canada, France, Germany, Italy, Japan, the UK, and the US. They used the stock indices and foreign exchange rates for the sample period starting from October 1, 1993 to February 15, 1996. Their results supported Bahmani-Oskooee and Sohrabian’s (1992) findings that stock prices and exchange rates do not have any long-run equilibrium relationships.

Hatemi and Irandoust (2002) examined the linkage between exchange rates and stock prices in Sweden. Monthly nominal effective exchange rates and stock prices over the period of 1993-1998 were used. The results stated that an increase in Swedish stock prices reduces the exchange rates of the country.

Farooq and Wong (2004) tried to complement the existing literature by investigating the relationship between stock prices and exchange rates in Pakistan. They used the monthly time series data of four stock indices of Pakistan and Rs/USD exchange rates from January 1994 to December 2003. Based on the results of one stock index (KSE General), there is a unidirectional causality from stock prices to exchange rates. However, according to the results of another stock index (KSE Services), it was found that exchange rates have an effect on stock prices.

Ooi et al. (2009) analyzed the causal relationship between exchange rates and stock prices of Thailand and Malaysia before and post Asian financial crisis by using daily data from 1993 to 2003. Based on the estimated results, they found a unidirectional causality relationship from stock prices to exchange rates in the case of Thailand both before and post crisis. Furthermore, their findings show that there is a unidirectional causal relationship from the Malaysian stock market to Malaysian exchange rate in post-crisis. However the variables are not linked in the pre-crisis period in Malaysia.

Rahman and Uddin (2009) investigated the interactions between stock prices and exchange rates in three emerging countries of South Asia, Bangladesh, India and Pakistan. They used monthly average nominal exchange rates of the U.S. dollar in terms of three currencies namely, Bangladeshi Taka, Indian Rupee, and Pakistani Rupee. Moreover, the monthly closing stock indices for all three countries in the period of January 2003 to June 2008 were employed. Their results indicated that there is no cointegrating relationship between stock prices and exchange rates. Furthermore, there is no causal relationship between the above variables.

2.5. Review of the Related Literature in the Middle East

In the articles related to the present study, Abdelaziz, Chortareas and Cipollini (2008) concentrated on the relationship between stock prices and exchange rates in four Middle Eastern countries, Egypt, Kuwait, Oman, and Saudi Arabia. Monthly data and different sample periods for each country were employed by the authors. Finding no evidence of cointegration between stock prices and exchange rates in a bivariate model, they included oil prices into the model. The results of their cointegration test illustrated that there is a long-run relationship among all the variables in three out of four sample countries. 

Parsva and Lean (2011) employed Johansen cointegration and Granger causality tests to analyze the relationship between stock prices and exchange rates in six Middle-East economies from January 2004 to September 2010. They found that the causality results in the pre-crisis and during crisis periods are inconsistent among the MiddleEast economics, except for Egypt and Oman.

3. Data and Methodology

To investigate the causal relationship between stock prices and exchange rates, this study focuses on six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia. To achieve this purpose, monthly data has been employed from 2004:1 to 2007:9 and from 2007:10 to 2015:9 to represent the pre-crisis period and post-crisis period, respectively. The most important fluctuation after financial crisis has been selected as the breakpoints to form the sub-periods for each currency.2 All variables are transformed into natural logarithm except inflation rate. The data for stock prices, exchange rates and crude oil price (U.S. dollar/BBL) are obtained from DataStream, except the stock prices in Iran which is taken from Tehran Stock Exchange’s website (http://new.tse.ir/). Inflation rate data is extracted from the IMF's database. The closing spot exchange rates for the Middle East countries are used in terms of Euro, since most of the sample countries’ currency are pegging against the U.S. dollar.

The estimated models can be expressed in the following two equations:

 

where

and t H is the error term.

The lag length is determined by the minimum value of Akaike Information Criteria (AIC). The Johansen-Juselius co-integration process based on the Vector Autoregressive (VAR) model is as follows:

Where Yt is a co-integrating vector of I (1) variables, O is a vector of constants, and t H is a vector of white noise residuals (Johansen, 2000). In our model, Y (SP, EX , INF ,OP) t { , if Yt is cointegrated, it can be generated by a Vector Error Correction Model (VECM) and in subsequent the Equation 3 can be written in the first difference form:

where O is a 4u1vector of drift, As are 4 u 4 matrices of parameters, and t H is a 4u1 white noise vector. Johansen-Juselius derived trace test( Otrace ) and maximum eigenvalues test ( Omax ) for testing the numbers of cointegrating rank in the system as follows:

 

where O j is the estimated eigenvalue, r is the number of co-integration vectors, k represents the number of variables and N is the sample size. If the variables are cointegrated, Granger causality test based on the VECM is used to include an error correction term as follows:

where ' is the first difference operator, O1 and O2 are the constant, 1 2 D ,D , 1 2 E ,E , 1 2 F , F as well as 1 2 U , U are the long run parameters, ECT is the lagged error correction term derived from the long-run co-integration model, SP H and EX H are two white noise residuals. 

4. Empirical Results

4.1. Results of Unit Root Test

Table 1 and 2 summarize the results of unit root tests for the series of stock prices in terms of the ADF and PP tests respectively. Table 3 and 4 summarize the results of unit root tests for the series of exchange rates in terms of the ADF and PP tests respectively. Table 5 and 6 summarize the results of unit root tests for the series of inflation rates in terms of the ADF and PP tests respectively. Moreover, Table 7 indicates the results of the same unit root tests for oil price. The first test equation in each Table (1-7) involves only the intercept, while the second test equation includes the intercept and trend.

As can be seen clearly in Tables 1 to 7 the results of both the ADF test and PP test or at least the PP test indicate that null hypothesis of a unit root is rejected in all variables and all countries when the data series are first differenced.

Thus the first difference of the data series of the variables is stationary and all variables are I (1). As noted earlier, when the variables are I(1), they will correspond to cointegrating relations (Pesaran & Smith, 2006). Therefore, the study can proceed to the next step of the process which is the JJ cointegration test.

<Table 1>: Results of the ADF Unit Root Test (Stock Price)

Notes: Numbers in brackets represent Newey-west Bandwidth (as determined by Bartlett-Kernel). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 2> Results of the PP Unit Root Test (Stock Price)

Notes: Numbers in brackets represent Newey-west Bandwidth (as determined by Bartlett-Kernel). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 3> Results of the ADF Unit Root Test (Exchange Rate)

Notes: Numbers in brackets are lag lengths used in the ADF test (as determined by minimum AIC). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 4> Results of the PP Unit Root Test (Exchange Rate)

 

Notes: Numbers in brackets represent Newey-west Bandwidth (as determined by Bartlett-Kernel). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 5> Results of the ADF Unit Root Test (Inflation Rate)

Notes: Numbers in brackets are lag lengths used in the ADF test (as determined by minimum AIC). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 6> Results of the PP Unit Root Test (Inflation Rate)

Notes: Numbers in brackets represent Newey-west Bandwidth (as determined by Bartlett-Kernel). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

<Table 7> Results of the Unit Root Test for Oil Price (All Countries)

 

Notes: Numbers in brackets are lag lengths used in the ADF test (as determined by minimum AIC). When using the PP test, numbers in brackets represent Newey-west Bandwidth (as determined by Bartlett-Kernel). The asterisks *, **, *** indicate rejection of the null hypothesis of non-stationary at the 10%, 5%, and 1% levels respectively.

4.2. Results of Cointegration Test

As noted earlier, determining the optimal lag length should be carried out before running the cointegration test because the cointegration results are sensitive to lag length. Therefore, the study runs the VAR model first in order to determine the optimal lag length based on the minimum AIC. The results of the cointegration test are provided in Tables 8 and 9.

The results of both trace and maximum eigenvalue statistics suggest that at least one long-run equilibrium relationship can be detected between the variables, stock prices, exchange rates, oil prices, and inflation rates.3 In other words, there is a long-term co-movement between the variables, and the results of the JJ cointegration test reject the null hypothesis of no cointegration in all the sample countries. These results are consistent with the findings of Jorion (1990), Abdalla and Murinde (1997), and Rahman and Uddin (2009) who have employed the same cointegration test in their studies.4

In all cases, there is one cointegrating vector in the equations. Table 10 and Table 11 illustrate the normalized cointegration results which represent the long-run relationship between all the variables, namely stock prices, exchange rates, oil price and inflation rate.

<Table 8> Results of the Johansen-Juselius Multivariate Co-Integration Test (Pre-Crisis Period)

Note: * indicates rejection of null hypothesis of no co-integration at 5%, using MacKinnon-Haug-Michelis (1999) p-values. The values in the brackets are the optimal lag order selections.

 

<Table 9> Results of the Johansen-Juselius Multivariate Co-Integration Test (Crisis Period)

Note: * indicates rejection of null hypothesis of no co-integration at 5%, using MacKinnon-Haug-Michelis (1999) p-values. The values in the brackets are the optimal lag order selections.

<Table 10> Normalized Cointegrating Coefficients with respect to Log Exchange Rate

Note: the values in the brackets are standard errors.* denotes statistically significant at 5% level.

<Table 11> Normalized Cointegrating Coefficients with respect to Log Stock Price

Note: the values in the brackets are standard errors. * denotes statistically significant at 5% level.

The cointegrating equation which is given in Table 10 shows that there is a significant effect of stock price, oil price and inflation rate on exchange rate, with the exception of a few cases. The exchange rates in all the sample countries are negatively related to the stock prices (stock prices have effect on exchange rates) in both sub-periods, while it is positively related to the oil prices. This result is in accordance with a priori theoretical expectations. The impact of inflation rate on the exchange rate of the Middle Eastern countries is mostly positive.

A negative long-run linkage between stock price and exchange rate means that an increase in stock price will lead to decrease in exchange rate or appreciation of the Middle Eastern currencies. This is consistent with the theory behind the linkage between stock price and exchange rate which noted in the section of theoretical framework.

The cointegrating equation which is given in Table 11 shows that there is a significant effect of exchange rate, oil price and inflation rate on stock price, with the exception of a few cases. Stock price in most countries is negatively related to the exchange rate (exchange rate has effect on stock price) in both sub-periods, while it is positively related to the oil prices. However, the table indicates mixed (both positive and negative) long-run relationship results between inflation rate and stock price.

4.3. Results of Granger Causality Test

Tables 12 and 13 present the results of Granger causality tests. The findings for the pre-crisis period suggest that Iran and Oman are the countries that have bidirectional causality between stock prices and exchange rates in the short-run. Oman has the same causality in the long-run too. The direction of causality in Egypt runs from stock prices to exchange rates in both the short-run and long-run. On the other hand, there exists no interaction among the markets in Jordan, Kuwait and Saudi Arabia in the short-run, while a two-way linkage exists in Jordan and Saudi Arabia in the long-run. However, in the crisis period all the sample countries have bidirectional causality among stock prices and exchange rates in the short-run with the exception of Iran which there is no causal relationship among its stock price and exchange rate. 

Based on the results of the ECM model, stock market and exchange rate market significantly have effect on each other and there is bidirectional causality between them in all the sample countries, with the exception of Iran which follows the portfolio-balance approach. Hence, considering the theoretical framework behind the study all the sample countries (except Iran) obey both traditional and portfolio approaches. These results lent support to BahmaniOskooee and Sohrabian (1992), Ajayi and Mougoue (1996), Granger et al. (2000), Muhammad and Rasheed (2002), Phylaktis and Ravazzollo (2005), Aydemir and Demirhan (2009) and Kumar (2009), who provided evidence for the presence of both approaches.

According to Granger (1998), when markets are influenced by both portfolio-balance and traditional approaches simultaneously, a feedback loop is expected to be found via the strength of two approaches. To determine the stronger approach, the signs of the relationships between the variables should be obtained, while the Granger causality test does not provide it. 

The results suggest that the relationship between stock prices and exchange rates have been totally influenced by the global financial crisis in the case of Jordan, Kuwait and Saudi Arabia, since in spite of absence of any causal relationship between the variables before the crisis, there is bidirectional causality after the crisis. The results reveal no change in the direction of causality between share prices and exchange rates in the case of Oman after the crisis and its markets are still integrated in a two-way causality.

<Table 12> Granger Causality Test (Pre-crisis Period)*

<Table 13> Granger Causality Test (Crisis Period)*

* k is the optimal lag lengths. ĺ Implies Granger cause, e.g. EXĺSP implies exchange rate Granger causes stock index. a) F statistic for testing H0: Į21 = Į22 = …..= Į2m = 0 or H0: ȕ11 = ȕ12 = …..= ȕ1m = 0 b) t statistic for testing H0: į1 = 0 or H0: į2 = 0 in ECM model. ***, **, and * denotes statistical significance at the 1%, 5%, and 10%, respectively.

5. Conclusion

This study investigates the causal relationship between stock prices and exchange rates, for six Middle Eastern countries, namely, Egypt, Iran, Jordan, Kuwait, Oman, and Saudi Arabia before and during the 2007 global financial crisis for the period between January 2004 and September 2015. Using Vector Autoregressive (VAR) model, the results suggest that in the case of Jordan, Kuwait and Saudi Arabia, there exist bidirectional causalities after the crisis period but not before 2007. The opposite is true for the case of Iran. Thus, during the crisis period, market participants in Iran cannot use the foreign exchange market as a hedge for investments in the stock market and vice-versa (Hatemi & Roca, 2005). In the case of Oman, there is bidirectional causality between the variables of interest in both periods.

Our findings also indicate the new insight for the Middle East-global markets integration in recent years. Overall, the results of this study indicate that fluctuations in foreign exchange markets can significantly affect stock prices in the Middle Eastern financial markets. This suggests stock market crashes may be prevented by stabilizing the exchange rates through central bank interventions.

Endnotes

1. [Persian] Gulf Cooperation Council countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia and the United Arab Emirates)

2. The breakpoints which have been obtained by the first trend after the global financial crisis are the same for all countries except Egypt and Iran since they follow a floating exchange rate regime. Details on sub period estimation are available from the authors upon request.

3. However, trace and maximum eigenvalue statistic yielded conflicting results in the case of Iran and Saudi Arabia in the pre-crisis period and Kuwait in crisis period.

4. According to Cheung, Yin-Wong and K. S. Lai (1993), Johansen tests are biased toward finding cointegration more often than what asymptotic theory suggests. Therefore, we have used EViews By default; EViews will compute the critical values for the test using MacKinnonHaug-Michelis (1999) p-values. The (nonstandard distribution) critical values are taken from MacKinnonHaug-Michelis (1999) so they differ slightly from those reported in Johansen and Juselius (1990). (For more information refer to EViews 7 User’s Guide, Quantitative Micro Software, LLC, Irvine, CA.)

 

 

Figure

Table

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