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Abstract

 

The economy of a nation
is driven by a robust securities market. The growth of a nation is indubitably
based on the strength and stability of its secondary market systems and
intermediaries. The mobilization of funds and its flow into diverse sectors of the
economy in a regulated manner signifies dynamism and progress. Egypt as an
economy has been in a trajectory of progress right since the establishment of
its Secondary Market and its Index EGX 30 in 2009. The Egyptian Pound (EGP) has
been at the centre of attention of Egyptian monetary policy due to undue stress
on it for quite some time. The Central Bank of Egypt (CBE) has been in the
forefront to stabilize the Pound from time to time.

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The changing economic
environment of a country has an indelible impact on its growth and development.
Any change in the foreign exchange market is sure to leave its footprint in the
secondary market. An intrigue that is plaguing all researchers in the field of
Foreign Exchange Management, is the query about the relationship between
Secondary Market and Forex Market. Many an investigation has been done to see
if there is a significant relation between Stock Prices and Exchange Rates. The
recent transition in Egyptian Economy to float its currency and its efforts to
stabilize the Pound has attracted researchers to find out the effects of such a
move. The relationship between securities market and forex market has to be
given a serious thought before any decision in the forex market policy and
regulation. Hence this study analyses the dynamic relationship between stock
market and exchange rate in Egypt using Engle-Granger cointegration method.

 

Keywords: Changing
Economic Environment; Egyptian Economy; Exchange Rate; Stock Prices;
Cointegration; Granger Causality; Econometrics

 

 

1.     
Introduction

Egyptian Economy stands
tall in the African Continent. However the Exchange rate instability in the
last year is a matter of Economic concern. The economy went in through a
dynamic change in its policies and processes. There were sudden strategies and
decisions by the government that took the Egyptians through a vortex of change.

The role of Capital
Market in the development of a nation cannot undermined. Exchange rate
movements have a significant role in determining the direction of growth of any
country. These two important economic variables have been at the forefront of
economic observation by researchers in all countries. This dynamic relationship
has been utilized by policy makers, due to their predictive efficacy.

The recent Exchange
Rate crisis in a few nations, have reiterated that the relationship between
stock prices and exchange rates is a matter of contention even today. Empirical
investigation into their relationship might lead to meaningful discoveries.
This paper seeks to study the dynamic relationship between stock prices and
exchange rate in Egypt. The context of the study is the instability of the Egyptian
Pound (EGP). Egyptian Stock Index (EGX) has seen its own share of ups and
downs. Firstly unit root tests were employed to test stationarity of the given
series. Secondly Cointegration was tested using and Engle – Granger’s (1987)
two – step methodology. A long-run relationship between stock market and forex
market in Egypt was observed in this study. A unidirectional causality from
forex market to stock market was also witnessed.

2.      Literature Review

Academic research has
been intrigued by the relation between exchange rates and stock prices. This
interest spiked ever since nations have moved towards flexible exchange rate
system. The researches have evolved around two theoretical models:
flow-oriented and stock-oriented. The flow oriented models, reiterate that the
stock prices and exchange rates are positively related since, exchange rate is
determined by a country’s current account and trade balance (Dornbush and
Fisher, 1980). While the stock-oriented models, hold that the stock prices push
up interest rates and subsequently have a downward pressure on exchange rate
since, capital account determines exchange rates (Branson, 1983, Gavin, 1989).

Few researches have
indicated a relation between exchange rates and stock prices. Bahmani – Oskooe &
Sohrabian (1992) found a bi-directional causality, even though there was no
long-run relation using Granger Concept of Causality, Akaike’s final prediction
error conjecture and Chow Test. Granger and others (2000) applied unit root and
cointegration models to determine the appropriate Granger causality relation
between stock prices and exchange rates during Asian crisis. The authors found
positive correlations between exchange rates and stock prices from the data of
Japan and Thailand. Whereas the data of Taiwan indicated negative correlation.
Ramaswamy and Yeung (2005) considered the causality between stock markets and
exchange rates in nine east Asian economies and found that the direction of
causality demonstrated a hit-and-run behaviour and switched according to the
length of the period. Alagidede and others (2010) conducted three variations of
Granger causality test and found out causality from exchange rates to stock
prices for Canada, Switzerland and UK. A weak causality in the other direction
was found only for Switzerland. Causality was observed from stock prices to
exchange rates in Japan. Nieh and Lee (2001) found lack of long-run
relationship between the stock prices and exchange rates.

There is not much
empirical observation of the changing dynamics of the financial markets in
Egypt. Thus this study explores the dynamic relationship between stock prices
and exchange rate in Eygpt.

3.      Data and Methods

Stock prices data
comprise of EGX 30 and Exchange rate data comprises of USD/EGP. The data have
been collected from www.investing.com. The period of data collection is from
2009 January to 2017 December. The year 2009 was chosen since EGX 30 was
started that year. All analysis was carried out
using GRETL software.

3.1. Unit Root Test

The Unit Root Test of
Dickey and Fuller (1981) has been employed. It is used to ensure that the data
of a time series variable is non-stationary using an auto-regressive model.
This test has been deployed to ensure that data is stationary and ensuring that
all variables are I (1) as variables that are I (0) indicate an automatic
long-run equilibrium correlation.

The Augmented Dickey
Fuller (ADF) Unit Root Test consists of estimating the following regression:

?Yt
= ?1 + ?2t + ? Yt-1 + ?i  t-1 +
?t

The null of non-stationarity hypothesis is stated
thus:

H0: A unit
root ‘or’ non-stationarity ‘or’ ? = 0

H1: No unit
root ‘or’ stationarity ‘or’ ? < 0, since ? = ? – 1 The unit root tests were carried out on the stock index (EGX) and exchange rate (EGP). Then the series were tested for unit roots at the level series. First difference of level series were obtained using the following equation: x = x – x (1) 3.2. Engle – Granger Two-step Cointegration Test Engle-Granger Cointegration Technique (1987) has been used to estimate the long-run equilibrium between Stock Prices and Exchange Rate. The cointegration between EGX (Stock Price) as dependent variable with EGP (Exchange rate) for Eygpt was done after ADF unit root tests of the two series. The Cointegration regression is given as: Yt = ?1 + ?2 Z t + ut EGXt = ?1 + ?2 EGP t + ut   Following this the residuals, ut, are saved from the cointegrating equation and tested using ADF to see if they are I(0) under the following hypotheses: H0: A unit root in the cointegrating regression residuals, ?t ~ I(0) H1: No unit root in the cointegrating regression residuals, ?t ~ I(0) When the residuals of cointegrating regression are I(0), it indicates stationarity and the null would be rejected. Thus we can conclude that there is a cointegration in the regression, where the residuals of both EGX and EGP are stationary. The next step involves the estimation of the Error Correction Model (ECM) the short-term model using the residuals, ?t. ?t = EGXt ? ?1 ? ?2EGP ? ?3t EGXt = ?0 + ?1EGPt + ?2ut?1 + ?t 3.3  Granger Causality Granger Causality test was conducted to see if there is any predictive causality between the two variables EGX and EGP. The test involves estimating the following pair of regressions:                   EGXt = i EGPt-1 + i EGXt-1 + u1t                   EGPt = I EGPt-1 + i EGXt-1 + u2t   3.      Results The Engle-Granger Cointegration methodology stipulates that to examine the long run relationship between two variables, they both have to be integrated of the same order. Hence we need to find out if the variables in the study – EGX and EGP are integrated of the same order.  An Augmented Dickey Fuller (ADF) test was employed to test for unit root in the level series of EGP and EGX. It was found that the level series, i.e. EGX I(0) and EGP I(0) are non-stationary. Next the ADF test was run on the two series after differencing them. It was found that the two series – EGX and EGP are integrated of the order 1, i.e. EGX I(1) and EGP I(1). Therefore, the basic condition for cointegration is satisfied. The results are presented in Table 1.   Table 1. Test for Unit Root using Augmented Dickey Fuller Test Series With Constant With Constant & Trend Stationary/ Non-stationary t statistic p-value t statistic p-value EGX (level) 0.208605 0.972 -0.932747 0.9477 Non-stationary EGX (Differenced) -10.1695 <0.001** -10.2296 <0.001** Stationary EGP (level) -0.0919912 0.946 -1.55178 0.8052 Non-stationary EGP (Differenced) -10.8838 <0.001** -11.0217 <0.001** Stationary ** Significant at 1% level As seen from the above results, the ADF test revealed that both EGX and EGP level series were non-stationary since the p-value was not significant at 1% level. The Null Hypothesis was accepted. After differencing the EGX and EGP, it was observed through the ADF test that the integrated series I(1) were stationary, since p-value was significant at 1% level and the Null Hypothesis was rejected. The criteria for a Co-integration relationship as laid down by Engle and Granger has been fulfilled. Next it was examined whether there was a co-integrating relation between EGX and EGP.   The estimates of the long run relationship - (Cointegrating regression): EGX    = 2324.38 + 629.912 EGP + ut t           = 7.740          18.59 pvalue  = <0.0001        <0.0001   Where ut is the residual from the long run Cointegrating regression as depicted in Fig 1.   Fig. 1. Residual Plot of the Cointegrating Regression In order to find out if the variables EGX and EGP have long run relationship, the residuals obtained from the above relationship was tested for stationarity using ADF test shown in Table 2.   Table 2. Testing for stationarity of residuals using Augmented Dickey Fuller Test Series With Constant With Constant & Trend Stationary/Non-stationary t statistic p-value t statistic p-value ut -3.5575 0.008275** -3.58477 0.03592* Stationary ** Significant at 1% level *  Significant at 5% level   It was observed through the ADF test that the residuals were stationary, since p-value was significant at 5% level. Therefore, the Null Hypothesis was rejected. It could be concluded that there EGX and EGP have a cointegrating relationship.   It has been observed that EGX and EGP are cointegrated. There is a long-term relationship or equilibrium between Egyptian Stock Prices and Egyptian Pound exchange rate. It implies that there may be disequilibrium in the short run. The error term in the above equation may be considered as "equilibrium error." This error term can be used to tie the short-term behaviour of EGX with its long-term value. The Error Correction Mechanism (ECM ) was first used by Sargan (1984) and later popularised by Engle and Granger. The Granger Representation Theorem states that if two variables Y and X are cointegrated then the relationship between the two can be expressed as ECM. The ECM equation for the study is given below: d_EGX            = 98.25 + 49.64 d_ EGP – 0.083 ut-1 t           = 1.64        0.75               - 1.73 pvalue  = 0.104     0.45                  0.087   The test for causality was done using Granger Causality test. The null hypothesis tested was that Stock Prices do not (Granger) cause Exchange Rate and vice-versa. The results are shown in Table 3 Table 3. Causality between EGX and EGP Direction of causality Number of lags F value Decsion EGX          EGP 2 2.064 (0.1323) Accepted EGP          EGX 2 23.878 (< 0.0001) Rejected   From the above it is evident that Stock Prices (EGX) does not (Granger) cause Exchange rate (EGP). However it is proved that Exchange Rate (EGP) does (Granger) cause Stock Prices (EGX) in the context of Egypt.     4.      Conclusion A change in one macroeconomic variable is bound to impact the other variables as well. Any change in the foreign exchange market is sure to affect the secondary market. An analysis was undertaken to find out the dynamic relationship between stock prices and exchange rates with reference to Egypt. Utilising the data of Egyptian Stock Index (EGX) and the Exchange rate of the EGP/USD, from January 2009 to December 2017, it has been observed that there is a long-term equilibrium between stock prices and exchange rate in Egypt, since the conditions of Cointegration are fulfilled. The short-term equilibrium between the two variables is however is not significant. There is unidirectional Granger causality. It is found that Exchange rate (EGP) has a causal effect on the Stock Prices (EGX). The policy implication is that the government of a country should seriously consider the impact the foreign exchange market has on the secondary market before any decision to alter fundamentals in the foreign exchange market. Any adjustment of the exchange rate has an altercating effect on the entire economy.                                   References Alagidede P., Panagiotidis, T. & Zhang, X. (2010). Causal relationship between stock prices and exchange rates. Stirling Economic Discussion Paper, 2010-05. Retrieved from https://www.stir.ac.uk/research/hub/publication/233. Bahmani-Oskooe, M. & Sohrabian, A. (1992). Stock prices and the effective exchange rate of the dollar. Applied Economics, 24(4), 459-465. Branson, W.H. (1983) Macroeconomic determinants of real exchange rate risk. In Herring, R.J. (Ed.), Managing Foreign Exchange Rate Risk. Cambridge: Cambridge University Press. Dickey, D.A. & Fuller, W.A. (1981). Likelihood ratio statistics for autoregressive time series with a unit root. Econometrica, 49(4), 1057-1073. Dornbusch, R. & Fisher, S. (1980). Exchange rates and the current account. American Economic Review, 70, 960-97. Retrieved from http://www.mit.edu/~14.54/handouts/ dornbusch80.pdf Engle, R. & Granger, C. (1987). Co-integration and error correction representation, estimation and testing. Econometrica, 55, 251-267. Retrieved from Stable URL: http://www.jstor.org/stable/1913236. Gavin, M. (1989). The stock market and exchange rate dynamics. Journal of International Money and Finance, 8, 181-200. Retrieved from https://www.sciencedirect.com/science/ article/pii/0261560689900223 Granger, C. W. J., Huang, B-N., & Yang, C.W. (2000). "A bivariate causality between stock prices and exchange rates: Evidence from recent Asian flu." The Quarterly Review of Economics and Finance 40 (3) : 337-354. Retrieved from https://www.sciencedirect.com/ science/article/pii/S1062976900000429. Gujarati, D. N., & Porter, D. C. (2009). Basic econometrics (5th ed.). Boston: McGraw-Hill. Nieh, C-C. & Lee, C-F. (2001). Dynamic relationship between stock prices and exchange rates for G-7 countries. The Quarterly Review of Economics and Finance 41,  477–490. Ramasamy, B. & Yeung, M.C.H. (2005). The causality between stock returns and exchange rates: revisited. Australian Economic Papers, 44, 162-169. doi:10.1111/j.1467-8454.2005.00257.x.

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