The furore and chaos created by the Asian financial crisis have ignited many studies on numerous subjects, and it is believed that the crisis has changed the way nations being administered and policies formed and implemented especially those regarding monetary and fiscal policies. Johansen (1991) cointegration method was used and the period was divided into two sub periods, albeit pre crisis and post crisis. The results obtained are similar with a number of past literatures pointing to no long run relationship between stock price and exchange rate for both periods

Baharom, A.H.

Habibullah, M.S.

Royfaizal, R. C

Munich Personal RePEc Archive

MPRA
Munich Personal RePEc Archive
Causation analysis between stock price
and exchange rate: Pre and post crisis
study on Malaysia
A.H. Baharom and R. C Royfaizal and M.S. Habibullah
Universiti Putra Malaysia
17. February 2008
Online at http://mpra.ub.uni-muenchen.de/11925/
MPRA Paper No. 11925, posted 4. December 2008 12:17 UTC
Causation analysis between stock price and exchange rate: Pre and post crisis study on 
Malaysia  
 
by 
 
A.H. Baharom, R.C. Royfaizal and M.S. Habibullah
1
 
 
 
ABSTRACT 
 
 The furore and chaos created by the Asian financial crisis have ignited many 
studies on numerous subjects, and it is believed that the crisis has changed the way 
nations being administered and policies formed and implemented especially those 
regarding monetary and fiscal policies. Johansen (1991) cointegration method was used 
and the period was divided into two sub periods, albeit pre crisis and post crisis. The 
results obtained are similar with a number of past literatures pointing to no long run 
relationship between stock price and exchange rate for both periods. 
 
 
Keywords: Stock price, exchange rate, Asian financial crisis, Cointegration. 
 
JEL code: G14, F31. 
 
 
1.0 INTRODUCTION 
The Asian Financial Crisis in 1997-1998 struck the economy of many Asian 
countries and these Asian countries suffered continuous currency depreciation and their 
stock markets were badly hit. Thai Baht was the first to be hit, and soon it spread to its 
neighbors. In Malaysia, the traditionally stable ringgit collapsed and it was followed by 
the collapse of the KLSE whereby KL composite index which decreased by 44.8% in the 
second half of 1997 (Azman-Saini et al., 2006). The issue of whether the stock prices and 
exchange rates are dynamically related has excited the interest of many researchers. 
Bahmani-Oskooee and Sohrabian (1992), and Nieh and Lee (2001) found that there are 
no long-run relationship between stock prices and exchange rates. While the former 
studied about the scenario in the United States, the latter’s study was on the dynamic 
relationship for G-7 countries, namely Canada, France, Germany, Italy, Japan, United 
States, and the United Kingdom. A study on Malaysia, Azman-Saini et al. (2006) found 
that during the crisis period, exchange rates led stock price. In their study they used 
                                                        
1
 All authors are affiliated with the Department of Economics, Faculty of Economics and Management, 
University Putra Malaysia, Serdang, 43400 UPM Selangor. The author for correspondence is Baharom 
Abdul Hamid, Lecturer. Tel: (603) 89467710; E-mail: baharom@econ.upm.edu.my 
Granger non-causality test proposed by Toda and Yamamoto (1995). However, this paper 
explore the long run relationship between stock price index and exchange rate (pre and 
post crisis) using the multivariate Johansen-Juselius method. The other distinct difference 
is, while Azman Saini et al. use bilateral exchange rate, our study employ the real 
effective exchange rate (REER) due to some intrinsic reasons.  
 
Malaysia is one of the most open and fast growing economies in South East Asia, 
hence stock market and foreign exchange market play an integral role in contributing to 
the financial development and economic growth in Malaysia. The volatility of exchange 
rates and stock prices would be a hindrance to a healthy and warranted growth. Thus, the 
understanding of relationship between stock prices and exchange rates is of utmost 
importance and very vital to capture the co-movement or trends of these two important 
macroeconomic variables. There are two schools of thoughts on this subject, whereby the 
traditional approach explained that the exchange rate leads the stock price, and the 
changes of exchange rates would affect the value of a firm via competitiveness, assets 
and liabilities. Similarly, the changes of exchange rates would eventually affect the firm’s 
profits or loss and thus, the value of its equity.  
 
In contrast, portfolio approach claims that actually stock prices lead exchange 
rates. When stock prices decreases, it reflects a reduction in investors’ wealth, and this 
ultimately leads lower of demand for money, which in turn causes the depreciation of the 
currency. Under portfolio approach, the main assumption is that stock price is inversely 
related with the exchange rates. 
 
This paper is organized as follow, whereby in the next section, we discuss the 
method and sources of data used in analysis. The third section will be on the results of the 
study while the concluding last section contains our conclusion. 
 
 
2.0 DATA AND METHODOLOGY 
The sample period is segregated into two periods, which is January 1988 to June 
1997 for the pre crisis period and July 1998 to December 2006 for the post crisis period. 
The REER was chosen in this study because the Malaysian Ringgit was pegged to the US 
dollar right after the crisis and this might contribute to a misconstrued finding. The 
efficiency of the stock market is also in question, if there exist any long run relationship, 
and then the stock market can be assumed inefficient due to interdependency.  
 
In empirical economics macroeconomic variables comprises of non stationary 
series. Treating non stationary variables in empirical analysis is important so that the 
results of spurious regression can be avoided. According to the concept of cointegration, 
two or more non-stationary time series share a common trend, then they are said to be 
cointegrated. The theoretical framework highlighted are expressed as follows: the 
component of the vector Yt = (y1t,y2t,…,ynt)’ are considered to be cointegrated of order 
d,b, denoted Yt ~ CI (d,b) if (i) all the component Yt are stationary after n difference, or 
integrated of order d and noted as Yt ~ I(d). (ii) presence of a vector  β = (β1,β2,…,βn) in 
such that linear combination βYt = β1y1t + β2y2t +…+ βnynt whereby the vector β is named 
the cointegrating vector. A few major characteristics of this model are that the 
cointegration relationship obtained indicates a linear combination of non-stationary 
variables, in which all variables must be integrated of the same order and lastly if there 
are n series of variables, there may be as many as n-1 linearly independent cointegrating 
vectors.  
 
 Johansen’s (1991) cointegration test is adopted to determine whether the linear 
combination of the series possesses a long-run equilibrium relationship. The numbers of 
significant cointegrating vectors in non-stationary time series are tested by using the 
maximum likelihood based λtrace and λmax statistics introduced by Johansen and Juselius 
(1990). The advantage of this test is that it utilises test statistic that can be used to 
evaluate cointegration relationship among a group of two or more variables. Therefore, it 
is a superior test as it can deal with two or more variables that may be more than one 
cointegrating vector in the system. 
 
 Prior to testing for the number of significant cointgrating vectors, the likelihood 
ratio (LR) tests are performed to determine the lag length of the vector autoregressive 
system. In the Johansen procedure, following a vector autoregressive (VAR) model, it 
involves the identification of rank of the n x n matrix ∏ in the specification given by: 
 
1
1
k
i
tktitit YYY
       (1) 
 
where Yt is a column vector of the n variables, ∆ is the difference operator, Γ and ∏ are 
the coefficient matrices, k denotes the lag length and δ is a constant. In the absence of 
cointegrating vector, ∏ is a singular matrix, which means that the cointegrating vector 
rank is equal to zero. On the other hand, in a cointegrated scenario, the rank of ∏ could 
be anywhere between zero. In other words, the Johansen cointegration test can determine 
the number of cointegrating equation and this number is named the cointegrating rank. 
 
 The Johansen Maximum likelihood test provides a test for the rank of ∏, namely 
the trace test (λtrace) and the maximum eigenvalue test (λmax). Firstly, the λtrace statistic test 
whether the number of cointegrating vector is zero or one. Then, the λmax statistic test 
whether a single cointegration equation is sufficient. Both test statistics are given as 
follows: 
 
)ˆ1ln()(
1
p
ri
trace Tr
        (2) 
)ˆ1ln()1,( 1rtrace Trr        (3) 
 
where p is the number of separate series to be analysed, T is the number of usable 
observations and λ is the estimated eigenvalues.  
 
 
 
3.0 EMPIRICAL RESULTS 
There have been numerous studies examining the relationship between stock 
prices and exchange rates. And the results are mixed, such as stock prices affecting 
exchange rates as found by Abdalla and Murinde (1997), vice versa feedback effect as 
found by Ajayi and Mougoue (1996) or no relationship found by Ibrahim (2000). 
However, the empirical results of long run relationship between stock prices and 
exchange rates are at best mixed. This is because of different data set and methodology 
used. In this study, the data set consists of monthly real effective exchange rates (REER) 
and stock prices index (SP) for Malaysia covering the period from January 1988 to 
December 2006. In the analysis, the period of study is divided into two sample periods: 
first, the pre-crisis period spanning from January 1988 to June 1997; and second, the 
post-crisis period from July 1998 to December 2006. 
 
Before proceeding to the cointegration test, the order of integration of the series 
was determined using the Augmented Dickey-Fuller (ADF) unit root test. Table 1 reports 
the result of the unit root test. The results clearly shows that the null hypothesis of a unit 
root cannot be rejected at the 5% level for REER and SP in their levels. However, the null 
hypothesis is rejected at 5% level when REER and SP one in their first-differences. Thus, 
these indicated that REER and SP are integrated of order one, I(1), in other words they 
are difference stationary process.  
 
Since the series are of the same order, we proceed to test the existence of 
cointegrating relations among the REER and SP using the Johansen cointegration test. 
The results are reported in Table 2. The results indicate that the null hypothesis of no 
cointegration cannot be rejected in the pre- and post-crisis periods. Therefore, we 
concluded that there is no evidence of long run relationship between the real effective 
exchange rate (REER) and the stock price index (SP) for both periods, more specifically, 
pre and post crisis periods. In addition the results show that the relationship between the 
stock price and the real effective exchange rate did not change before and after the Asian 
Financial crisis. 
 
4.0 CONCLUSION AND IMPLICATION 
The results clearly show that both the variables of interest, the REER and SP are 
not cointegrated for both period, pre and post crisis. Our findings support the results 
obtained by Ibrahim (2000) but in contrast with Azman Saini et al. (2006). This might be 
due to different sets of variables chosen and also different method used in the analysis. 
The result also clearly show that Malaysia’s stock market can be considered efficient 
since it is found to independent of exchange rate. The findings of no long-run equilibrium 
between stock price index and exchange rate imply that Malaysian stock market is 
informational efficient. Under the efficient market hypothesis, market participants cannot 
use the growth of the exchange rates as a trading rule in order to exploit abnormal profit. 
On the other hand, if the variables are cointegrated or have a long-run equilibrium 
relationship, then the movements in exchange rate can be exploitable as a trading rule to 
reap abnormal profit. 
 
 
REFERENCES 
 
Ajayi, A.R., and Mougoue, M. (1996). ―On the dynamic relation between stock prices 
and exchange rates‖. The Journal of Financial Research, 19, 193-207. 
 
Abdalla, I.S.A., and Murinde, V. (1997). ―Exchange rate and stock price interactions in 
emerging financial markets: evidence on India, Korea, Pakistan and Philippines‖. 
Applied Financial Economics, 7, 25-35.  
 
Azman-Saini, W.N.W., Habibullah, M.S., Law, S.H. and Dayang-Afizzah, A.M. (2006) 
―Stock Prices, exchange rates and causality in Malaysia: a note‖. The ICFAI 
Journal of Financial Economics, 5, 7-13. 
 
Bahmani-Oskooee, M., and Sohrabian, A. (1992). ―Stock prices and the effective 
exchange rate of the dollar‖. Applied Economics, 24, 459-464. 
 
Ibrahim, M.H. (2000). ―Cointegration and Granger causality tests of stock price and 
exchange rate interactions in Malaysia‖. ASEAN Economic Bulletin, 17, 36-47. 
 
Johansen, S. (1991). ―Estimation and hypothesis testing of cointegration vectors in 
Gaussian vector autoregressive models‖. Econometrica 59, 1551–1580. 
 
Johansen, S. and Juselius, K. (1990). ―The full information maximum likelihood 
procedure for inference on cointegration—with applications to the demand for 
money‖. Oxford Bulletin of Economics and Statistics, 52, 169–210. 
 
Nieh, C.C. and 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. 
 
Osterwald-Lenum, M. (1992). ―A note with quantiles of the asymptotic distribution of the 
maximum likelihood cointegration rank test statistics‖. Oxford Bulletin of 
Economics and Statistics, 54, 461-471. 
 
Schwert, G.W. (1987). ―Effects of model specification on tests for unit roots in 
macroeconomic data‖. Journal of Monetary Economics, 20, 73-103. 
 
Toda, H.Y., and Yamamoto, T. (1995). ―Statistical inference in vector autoregressions 
with possibly integrated processes‖. Journal of Econometrics, 66, 225-250. 
 
 
 
 
 
Table 1: Results of the Unit Root Tests  
ADF Test 
  Level First Difference 
Variables Constant, Constant, Constant, Constant, 
No Trend Trend No Trend Trend 
Before Crisis     
REER -2.5511 -3.2227 -7.5299** -7.5146** 
 (2) (2) (0) (0) 
SP -1.3931 -3.0751 -6.7332** -6.7165** 
 (1) (2) (0) (0) 
After Crisis     
REER  -1.8119 -2.1411 -10.2628** -10.2413** 
 (1) -1 (0) (0) 
SP -2.7934 -3.4297 -7.0962** 7.0767** 
  (1) (1) (0) (0) 
Note: ** denotes significant at 5% significance levels, respectively. Figures in parenthesis ( ) refer to the 
selected lag length.  The lag length was arbitrarily selected using SIC. 
 
 
Table 2: Results of Cointegration Test   
Vectors r = 0 r ≤ 1 
Pre-crisis results (Jan, 1988 - June, 1997),  Lags = 4 
 Trace test 11.61 0.47 
 5% level 15.41 3.76 
 Max-Eigen test 11.15 0.47 
 5% level 14.07 3.76 
    
Post-crisis results (July, 1998 - Dec, 2006),  Lags = 4 
 Trace test 12.40 2.59 
 5% level 15.41 3.76 
 Max-Eigen Test 9.81 2.59 
  5% level 14.07 3.76 
Note: r indicates the number of cointegrating vectors. Trace and Max-Eigen denote the trace statistic and 
maximum eigenvalue statistic. The critical values are obtained from Osterwald-Lenum (1992). Lag 
selection (k) is based on Schwert (1987) formula. 


Causation analysis between stock price and exchange rate: Pre and post crisis study on Malaysia

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