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.

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.