INTERLINKAGES AMONG SOUTH EAST ASIAN STOCK MARKETS (A Comparison Between Pre- and Post-1997-Crisis Periods)1
Aamir Rafique Hashmi• and Liu Xingyun
Department of Economics
National University of Singapore 10 Kent Ridge Crescent
Singapore 119260
Abstract
This paper examines inter-linkages among South East Asian (SEA), Tokyo
(TK) and New York (NY) stock market returns before and after the Asian
currency crisis using simple correlations, Granger causality tests and VAR
models. We find that inter-linkages among SEA markets have increased after
the emergence of crisis. The New York market affects the SEA markets but is
not affected by them. Tokyo market seems to be isolated from the region.
Singapore stock market appears to dominate other markets in the region and
its effect on them is even greater than that of New York. All five SEA markets
affect one another and their effects are transmitted within two days in most of
the cases.
Key words: Stock Market Integration; South-East Asian Stock Markets
JEL classification: F15; G15
1 Presented at a conference sponsored by Journal of International Money and Finance and the
University of Rome Tor Vergata, in Rome, December 5-7, 2001. • Corresponding author at email: [email protected] or phone: (65) 874-3960.
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1. INTRODUCTION
Studies of market integration suggest that international stock markets have become
increasingly integrated in recent years2. Although this evidence is not undisputed,3 we can
safely comment that most of the recent studies find equity markets to be inter-linked. These
linkages can be attributed to the deregulation of international financial markets, shift to
floating exchange rates, advancement of communication system and information technology,
lower cost of transactions, and the development of new financial instruments and techniques.
There are a few interesting conclusions that emerge from the studies on stock market
integration. Some studies have found that New York market is the global leader and markets
in almost every part of the world are affected by it4. It has also been documented that
excluding New York, the other markets tend to have stronger relations with geographically
closer markets than with the remote ones. Saying the same thing in different words, intra-
continental linkages are stronger than inter-continental linkages5. Another finding is that
linkages are variable over time and generally major events (like 1973 and 1979 oil price
shocks, end of the Bretton Woods system, abolition of exchange controls in UK in late 1970s,
1987 stock market crash, 1991 Gulf) affect the linkages significantly.6 This last conclusion is
our motivation to study the effect of 1997 financial crisis on inter-linkages among SEA
markets. There are studies that concentrate on linkages among Asian markets7 but none has
attempted to study the effects of 1997 crisis on linkages.
2 The studies that provide evidence that markets are integrated and/or their degree of integration has been changing (mostly increasing) over time include, among many others, Ammer and May (1996), Arshanapalli and Doukas (1993), Bracker et al (1999), Eun and Shim (1989), Kasa (1992), Leachman and Fransic (1995), Longin and Solnik (1995) Rangvid (2001) and Taylor and Tonks (1989). For a comprehensive bibliography of studies on international stock market linkages see Roca (2000) pp.148-171. 3 See King (1994) and Baekaert and Harvey (1995). 4 Cheung and Mak (1992), Eun and Shim (1989), Koch and Koch (1993), Pesonen (1999) and Soydemir (2000) are just a few examples of such studies. 5 See, for example, Hilliard (1979) and Dekker et al (2001). 6 See, for example, Ammer and Mei (1996), Arshanapalli and Doukas (1993), Leachman and Fransic (1995) and Taylor and Tonks (1989). 7 Examples include Pan et al (1999), Dekker et al (2001) and Siklos and Ng (2001).
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In the course of our study we try to answer the following questions for both pre- and post-
crisis periods: are the SEA stock markets interlinked? How are the two largest markets of the
world are related to SEA markets? Which market appears to be the most influential in the
region? How much of the movements in one stock market can be explained by innovations in
other markets? How rapidly are the stock price movements in one market transmitted to
other markets?
In attempting to answer the above questions, analysis of correlations (and correlograms) and
Granger causality tests are conducted to examine the co-movements among the pair-wise
stock prices and returns. In addition, we also estimate vector-autoregressive models for both
pre- and post-crisis periods. The seven stock markets included in this study are New York
(NY), Tokyo (TK), Singapore (SG), Kuala Lumpur (KL), Bangkok (BK), Jakarta (JK), and
Manila (MN). Last five of these markets represent almost the entire South East region of Asia
The organization of the rest of the paper is as follows: Section 2 discusses the methodology
and data used. The empirical results are presented in Section 3 and Section 4 provides a
summary and conclusions of this study.
2. DATA AND METHODOLOGY
2.1 Data
The time series used in this study are daily stock market indexes at closing time, in terms of
local currencies. The stock indexes used are S&P 500 Composite, Nikkei 225 Stock Average,
Singapore Straits Times Index, Kuala Lumpur Composite, Bangkok S. E. T., Jakarta S. E.
Composite and Philippines S. E. Composite. The data cover a time period from 03/01/1994 to
29/12/2000, which is divided into two sub-periods. First sub-period has been named the pre-
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crisis period and covers from 03/01/1994 to 31/07/1997. The second sub-period has been
named the post-crisis period and covers from 01/08/1997 to 29/12/2000.
These stock market indices are transformed into continuously compounded daily rates of
return, defined as:
it
it
it PPR 1lnln −−=
where itP is stock price index series of market i. None of the data on stock prices that we use
have dividends reinvested. However, on a daily basis, dividends are relatively unimportant.
Changes in stock prices over short period are mainly affected by the arrival of information.
All these markets (except the New York) operate in similar time zones and there is a lot of
overlapping in their trading hours. Figure 1 plots the official trading hours for all seven
markets in our sample. As we are working with daily returns, we can treat the six markets
other than the New York as operating synchronously, although the closing price in Manila
may not be able to capture the effects of afternoon news from other markets operating in the
same time zone. The different opening and closing hours become more important when we
work with higher frequency data. In this study, however, our focus is on daily data so we
ignore the minor differences in opening and closing of markets in the SEA region. New York
is in a different time zone and opens when all these markets have closed. Due to this reason,
when we study the linkages from New York to other markets we should use the last day
returns in New York and current returns in other markets. However, when we study the
linkages from other markets to New York, we must consider the current returns for both.
2.2 Correlations and Cross-Correlograms
Simple correlations of daily returns reveal the link between the rates of change in the indices.
We also study the cross correlograms (up to 36 lags) between all pairs of the seven markets.
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We compute the correlation matrices for both pre- and post-crisis periods and then test their
equality using the Box-M statistic. It can be defined as [see Meric and Meric (1989)]:
( ) ( ) ( )
−+−×
+
−++
−+
−
+
−+−= −−~
122
~1
112121
2
ln1ln12
11
11
116
1321 RRnRRnnnnnk
kkM
where
k = number of variable in correlation matrix
ni = number of observations used to compute the ith correlation matrix
Ri = the ith correlation matrix
( ) ( )2
11
21
2211~
−+−+−
=nn
RnRnR
M follows a χ2 distribution with k(k+1)/2 degrees of freedom.
2.3 Granger Causality Test
Correlation does not necessarily imply causation in any economic sense. It is not unusual to
find that high correlations may simply be the result of spurious relationship and thus not of
much use. To address the question that whether some variable causes others, Granger (1969)
proposed a test to see how much of the current x can be explained by past values of x and
then to see whether adding lagged values of y can improve the explanation8. In a bivariate
VAR describing x and y, y does not Granger-cause x if the coefficient matrices jΦ are lower
triangular for all j:
+
++
+
+
=
−
−
−
−
−
−
t
t
pt
ptpp
p
t
t
t
t
t
t
yx
yx
yx
cc
yx
2
1)(
22)(
21
)(11
2
2)2(
22)2(
21
)2(11
1
1)1(
22)1(
21
)1(11
2
1 0...
00εε
φφφ
φφφ
φφφ
where j is the number of lag and j=1,2,�p.
To implement this test, we assume a particular autoregressive lag length p and estimate
tptpttptpttt uyyyxxxcx +++++++++= −−−−−− βββααα LL 221122111
8 For details, see Hamilton (1994).
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by OLS. We then conduct an F test of the null hypothesis
0: 210 ==== pH βββ L
2.4 The VAR Model
The popular time series technique of VAR is due to the seminal work of Sim (1980). It is
used to study the dynamic interrelations between n different variables. Many studies of stock
markets have used VAR models to study inter-linkages.9 The VAR model for n markets can
be expressed as:
t
m
sstst eRACR ++= ∑
=−
1
(1)
where Rt is a n × 1 column vector of daily rates of return of the n stock markets, C and As are,
respectively, n × 1 and n × n matrices of coefficients, m is the lag length, and et is a n × 1
column vector of error terms. The ijth component of As measures the direct effect that a
change in the return to the jth market will have on the return of the ith market in s period.
Since the coefficients in equation (1) contain complicated cross-equation feedbacks and are
difficult to describe intuitively, it is better to analyze the model�s reaction to typical random
shocks. By successive substitutions of the right-hand side of equation (1), we can obtain a
moving average representation as follow:
∑=
−+=x
sstst eBCR
0' (2)
Where each Bs is an n × n matrix. The Bij,s are called the impulse response functions, which
show the response of the ith market in s-period after a unit random shock in the jth market,
other things remaining constant. The decomposition of variance ( 2,
2, sisijB α∑ ) can reveal
how much variance of market i is determined by the innovations of market j in the s period. 9 Few examples include Ammer and Mei (1996), Chaudhary (1994), Eun and Shim (1989), King et al (1994), Rapach (2001), Roca (1999), Soydemir (2000) and Yuce and Mugan (2000).
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3. EMPIRICAL RESULTS
3.1 Preliminary Findings and Correlation Matrices
We start by plotting the price indexes for the seven markets (see Figure 2). During the entire
sample period the New York market shows an upward and Bangkok market shows a
downward trend. Tokyo market has long periods of upward and downward movement with a
slight downward trend. Singapore, Kuala Lumpur, Jakarta and Manila market indexes exhibit
movements similar to one another. These markets show random movements until the onset of
Asian crisis and after that we see a deep plunge followed by some recovery. During the year
2000 almost all these markets show a downward trend again. This similarity of movement
among Singapore, Kuala Lumpur, Jakarta and Manila is the first indication of possible
linkages among SEA markets.
Next we look at the graphs of returns (see Figure 3). All markets show an increase in
volatility after the crisis. This supports the view that the currency crisis has increased
uncertainty in markets resulting in higher level of risk. Although the graphs clearly reflect the
stationarity of stock returns, we formally test for unit roots. We are able to reject the null
hypothesis of unit roots in all seven series of returns.
To study the possible lead lag relations between different pairs of these markets we study the
Cross-Correlograms for each of the 21 pairs for both pre- and post-crisis periods. We find
that most of the significant correlations are confined to the first or second lags10 and there is
hardly any significant correlation beyond two lags. This simple finding is of great
significance as in the study of linkages using Granger causality tests and VAR models the
choice of lag length plays an important role.
10 We study up to 36 lags.
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Next, we construct correlation matrices for returns. We find prices to be highly correlated
among all markets (see Tables 2 and 3) and average value of the correlation coefficient (r) is
0.2577 in the pre-crisis period and 0.3205 in the post-crisis period11. All r are significant at
1% level except the one between Tokyo and Bangkok during the pre-crisis period. We notice
from a comparison of Tables 2 and 3 that among all the 21 correlations, there are only 5 that
have declined in the post crisis period and 4 out of these 5 involve Kuala Lumpur Stock
Market. The most remarkable result is that the correlation between Singapore and Kuala
Lumpur declines from 0.67 in pre-crisis period to 0.38 in post-crisis period. This finding
suggests that the linkages of Kuala Lumpur market with the other markets in the region have
been affected by the crisis. This conclusion is further supported by the results of our VAR
model below.
The value of Box-M statistic between the pre- and post-crisis periods is 205.48 that is
significant at 1% level. It testifies that the correlation matrices for the two periods are not
equal.
3.2 Granger Causality Test Results
It will be too simplistic to say something definitive on the basis of our results of simple
correlation and correlograms. To study the bilateral linkages further we use Granger
Causality Tests (GCTs). We run GCTs using three different lag lengths. We try 2, 5 and 15-
day lags. The results are reported in Tables 4 and 5.
For both pre- and post-crisis periods we observe that the New York market affects all other
markets and is not much affected by any of them. Tokyo market does not have any influence
11 We use the current period returns for all markets except the New York market for which we use the previous day returns. The idea is that New York market is likely to influence the other markets but is not very likely to be affected by them. We also try the current returns from the New York market but its correlation with the current returns from other markets is negligible. This is clear evidence of the dominance of the New York market.
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on the SEA regional markets and is not much affected by any market other than the New
York.
The pair-wise linkages between different SEA markets are very interesting. Singapore affects
all other four SEA markets before the crisis and is affected only by Kuala Lumpur. In the
post-crisis period, however, it is affected by all of them. This implies an increase in inter-
linkages among SEA markets after the crisis. The Kuala Lumpur market had a strong effect
on other four SEA markets before the crisis and was only affected by Singapore in the pre-
crisis period. After the crisis, it affects only Singapore and Manila but is affected by all four.
The Bangkok market provides a good example of increase in linkages after the crisis. We
notice that in the pre-crisis period it was only affected by Singapore and Kuala Lumpur
markets and it affected Jakarta and Manila. After the crisis, it was not being affected by
Kuala Lumpur any more but Manila and Jakarta were affecting it. The effects of Bangkok
market after the crisis are very significant and involved New York and Tokyo in addition to
all four SEA markets. Before the crisis, Jakarta was affected by all four and it affected only
Manila. After the crisis it was still affected by all (except KL) and was affecting all four.
Manila was affected by all four before and after the crisis. It affected only Jakarta before the
crisis but was affecting all four after it.
When we look at all the five SEA markets together (see figure 4) we notice that the linkages
have increased after the crisis. We also find that smaller markets (i.e. Bangkok, Jakarta and
Manila) have an increased influence on the larger markets (i.e. Singapore and Kuala Lumpur)
after the crisis.
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3.3 Results of the VAR Model
On the basis of our results in the previous section we find it unnecessary to include Tokyo in
our VAR model.12 Before we estimate the VAR as defined in equation (1), it is very
important to determine the appropriate number of lags. As we are using daily stock returns
and markets open five days a week, we estimate different VAR models with 30, 25, 20, 15,
10 and 5 to 1 lags. We compare AIC and SBC values of different models, and both statistics
suggest 2 lags. Our results of pair-wise cross-correlograms also suggest that the maximum
number of lags that exhibit correlations is two. Eun and Shim (1989) also find that price
changes in one market are transmitted to the other markets within a maximum of 48 hours.
On the basis of all the above reasons we choose two lags for our VAR model. This is
different from the literature on linkages that use VAR model. Generally the lag length is
taken to be around 15 days. We oppose the idea of long lag length for two reasons. First, it is
not supported by standard statistical criteria. Second, it is unlikely that effects of a shock in
one market on others will extend beyond two days.
Besides determining the lag length, another important decision to make is the ordering of
markets starting from the most exogenous one to the most endogenous one. This ordering is
crucial when we want to decompose the variance of the forecast error of a particular market.
We use market capitalization as the criterion that suggests the following ordering: New York,
Singapore, Kuala Lumpur, Bangkok, Jakarta, and Manila.13 We call this particular ordering
NSKBJM (using first letter of each market�s name).
12 As an exercise we try a VAR model with all seven markets and find that the inclusion of Tokyo does not affect our results. 13 See Figure 1 for details. Kuala Lumpur had a higher market capitalization than Singapore before the crisis but after the crisis Singapore took the lead. To take care of this inter-sample change, we also decompose the variance using NKSBJM ordering. The results are not reported but we discuss them below.
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3.3.1 Impulse Responses
First we examine the impulse responses of returns in various markets to a shock in their own
and other market innovations. As before, our focus is on comparing the responses in two sub-
periods. The graphic results are presented in Figures 4 and 5 for innovation in New York and
in Figures 6 and 7 for shocks in Singapore market. Taking a 25-day forecasting horizon14 we
see how the effects of a shock in US and Singapore market innovations are reflected in the
returns of other markets. In all cases the convergence is almost complete in less than 10 days
although the most significant transmission takes place during the first two days. To simplify
the analysis we concentrate on the first five days. The details are provided in Tables 8 and 9.
We briefly comment on each SE Asian market below.
The biggest shocks to Singapore market come from its own innovations, followed by the
shocks from New York on the next day. Most of the transmission is complete in two days. In
the pre-crisis period the strongest response comes on the first day to its own innovations, and
on the second day to New York innovations, which is due to non-synchronism. In the post-
crisis period the responses on the second day to shocks from other regional market are much
larger than the pre-crisis period.
The Kuala Lumpur market, before the crisis, is affected by Singapore on the first and second
days and by New York on the second day. The other markets do not seem to have much
effect on it. After the crisis, we notice that the shock in Singapore has a smaller effect on
Kuala Lumpur market. The effects of other markets, however, have increased after the crisis.
14 It may appear to be out of line with our previous contention that transmission is complete in two days. The reason for choosing 25-day forecast horizon is to show that responses reduce to negligible values very soon and after a week they are almost zero. This should not be surprising given the fact that our VAR has only two lags.
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In the pre-crisis period, the Bangkok market is affected by Singapore and Kuala Lumpur on
the first day and by New York and Singapore on the second. After the crisis we notice that
Jakarta also affects Bangkok on the second day.
All except Manila affected Jakarta in the pre-crisis period. We observe the same thing after
the crisis but also notice that Bangkok has a stronger effect than before. In case of Manila we
do not see much change after the crisis. For Jakarta and Manila, we also notice that the effect
of Singapore is much higher on the first day in the post-crisis period. This provides support to
the idea of Singapore acting as a regional leader after the crisis. We shall explore this point
further in the following section.
All these results should be interpreted keeping in mind the limitations of a VAR model. First,
we have seen that there are strong contemporaneous relationships between the returns in
these markets but we cannot have the contemporaneous returns from other markets on the
right hand side of the VAR equation for each market. This limitation prevents us to study the
contemporaneous linkages. Second, the ordering is very important in determining the impulse
responses and the market that appear high in our ordering are bound to have strong effect on
the markets that appear low. We also tried NKSBJM ordering (results not shown here) and
found that our broad conclusions remain the same.
3.3.2 Variance Decomposition
The problem with the impulse responses is that they show the effect of different days
separately. If we are interested in some kind of cumulative effect the variance decomposition
is a better tool. Once again we shall focus on SEA markets and compare the variance
decomposition in two sub-periods. Although most of the transmission is completed in two
days, we allow five days for impulse responses to fully show their effect and then discuss the
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variance decomposition on the fifth day when transmission is sure to be complete. All the
following discussion is based on Tables 6 and 7.
Starting with Singapore, we see that most of the forecast error variance is explained by
movement in its own returns and by the movement in the returns of New York market.
Important thing to notice is that New York market explains more of the movement in
Singapore market after the crisis.
When we look at the Kuala Lumpur market we find that Singapore explains a major portion
of its variance before the crisis (39%) but after the crisis it falls to mere 8%. When this result
is viewed together with the Granger causality test results, we find some evidence to support
the claim that Kuala Lumpur market has been slightly alienated from the region after the
crisis. One possible explanation could be the way in which Malaysian government responded
to the crisis. The capital controls imposed may have resulted in this isolation.
Its own shock followed by the shocks in Singapore and New York caused the variance in
Bangkok market. It is important to note that the influence of both Singapore and New York
increased after the crisis. Another important thing to note is that Singapore explained 12%
and 20% of the variance in Bangkok market in the two periods respectively, compared to 4%
and 6% respectively explained by the New York market. The implication is that Singapore
acts as a leader for Bangkok market both before and after the crisis and its effect has grown
stronger in the later period.
Singapore also appears to be the leader for Jakarta market in both pre- and post-crisis periods.
However, its influence is almost the same in two periods. Bangkok affects Jakarta more after
the crisis and its effect is almost as much as that of New York. Singapore also has a strong
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effect on Manila as it explains 10% and 16% of its variance in the two periods respectively.
New York and Jakarta each explained 3% before the crisis. After the crisis New York
explained 9% while Jakarta explained less than 2%. Bangkok explained 5% of variance in
Manila in the second period.
Before we draw any general conclusion from our analysis of variance decomposition it is
worthwhile to mention our variance decomposition results using NKSBJM ordering. We
notice that our conclusion about Singapore�s regional leadership is sensitive to the ordering
in the pre-crisis period. When we change the ordering to NKSBJM, Kuala Lumpur appears to
affect the regional markets more than Singapore in pre-crisis period. However, in the post-
crisis period Singapore emerges as a clear leader regardless of ordering.
We conclude that Singapore is a regional leader and its effect on the regional markets is even
greater than that of New York. This leadership has become stronger after the crisis. Kuala
Lumpur has been slightly alienated from the region after the crisis. Most of the markets have
become more sensitive to changes in Bangkok in the post-crisis period. The possible reason
could be the fact that the crisis started in Thailand and engulfed the region very quickly.
4. Summary and Conclusions
In this paper, the inter-linkages among daily returns in New York, Tokyo and five South East
Asian markets are examined for pre- and post-crisis periods separately, by using simple
correlations, Granger causality tests and VAR models. It is found that SEA markets are
closely linked with one another. The New York market exerts a strong influence on the
region but is not much affected by it. The Tokyo market is virtually unrelated to the region.
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Singapore emerges as a leader in the region and its leadership is stronger in the post-crisis
period. The basic proof of its leadership lies in the higher percentage of variance in the
regional markets that can be explained by movements in Singapore market. This conclusion
becomes more significant when we observe that the influence of Singapore on the region is
even greater than that of New York. Kuala Lumpur appears to have been slightly alienated
from the region after the crisis. This alienation could be the repercussion of capital control
policies of the Malaysian government. Bangkok appears to affect the region more after the
crisis. Jakarta also has an increased effect while Manila, being the smallest of five SEA
markets studied, does not have much influence on other markets.
Another important conclusion of our study is that inter-linkages among the SEA markets
have increased after the crisis and markets respond more promptly and actively to the
changes in neighboring markets.
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REFERENCES
Ammer, J and J.Mei (1996) �Measuring International Economic Linkages with Stock Market
Data� The Journal of Finance, 5, 1743-1763.
Arshanapalli, B. and Doukas, J. (1993) �International Stock Market Linkages: evidence from
the pre- and post-October 1987 period, Journal of Banking and Finance, 17, 193-208.
Bekaert, G. and Harvey, C. R. (1995) �Time-Varying World Market Integration�, Journal of
Finance, 50, 403-444.
Bracker, D.S. Docking, P.D. Koch (1999) �Economic Determinants of Evolution in
International Stock Market Integration� Journal of Empirical Finance, 6, 1-27.
Cheung, Y. �L. and Mak, S. �C. (1992) �The International Transmission of Stock Market
Fluctuations between the Developed Markets and the Asia-Pacific Markets�, Applied
Financial Economics, 2, 43-7.
Chowdhury, A. R. (1994) �Stock Market Independencies: Evidence form the Asian NIEs,
Journal of Macroeconomics, 16, 629-51.
Dekker, A., K. Sen and M. R. Young (2001) �Equity Market Linkages in the Asia Pacific
region: A Comparison of the Orthogonalised and Generalised VAR Approaches�,
Global Finance Journal, 12, 1-33.
Eun, C. S. and Shim, S. (1989) � International Transmission of Stock Market Movements�,
Journal of Financial and Quantitative Analysis, 24, 241-56.
Granger, C. W. .J. (1969) �Investigating Causal Relations by Econometric Models and Cross-
Spectral Methods,� Econometrica, 37, 424�438.
Hamilton, J. D. (1994) �Time Series Analysis�, Princeton University Press, Princeton, New
Jersey.
- - 16
Hilliard, J.E. (1979)�The Relationship Between Equity Indices on World Exchanges�,
Journal of Finance, 34, 103-14.
Kasa, K. (1992) �Common Stochastic Trends in International Stock Markets�, Journal of
Monetary Economics, 29, 95-124.
King, M.A., E. Sentana, and S.B. Wadhwani (1994) �Volatility and Links between National
Stock Markets�, Econometrica, 62, 901-933.
Koch, T.W. and P.D. Koch (1993) �Dynamic Relationship among the Daily Level of
National Stock Indexes� in International Financial Market Integration. Stansell, S. R.
(ed.), Blackwells, Mass.
Leachman, L. L. and Fransic, B. (1995) � Long-run Relations among the G-5 and G-7 Equity
Markets: Evidence on the Plaza and Louvre Accords�, Journal of Macroeconomics,
17, 551-77.
Longin, F and B. Solnik (1995) �Is the Correlation in International Equity Returns Constant:
1960-1990?� Journal of International money and Finance, 14, 3-26.
Meric, I. and G. Meric (1989) �Potential Gains from International Portfolio Diversification
and Inter-temporal Stability and Seasonality in International Stock Market
Relationships�, Journal of Banking of Finance, 13, 627-640.
Pan, Ming-Shiun, Y. A. Liu and H. J. Roth (1999) �Common Stochastic Trends and
Volatility in Asian-Pasific Equity Markets�, Global Finance Journal, 10, 161-172.
Pesonen, Hanna (1999) �Assessing Causal Linkages Between the Emerging Stock Markets of
Asia and Russia�, Russsian and East European Finance and Trade, 35, 73-82.
Rangvid, J. (2001) �Increasing Convergence Among European Stock Markets? A Recursive
Common Stochastic Trends Analysis�, Economics Letters, 71, 383-389.
- - 17
Rapach, David E. (2001) �Macro Shocks and Real Stock Prices�, Journal of Economics and
Business, 53, 5-26.
Roca, Eduardo D. (1999) �Short-term and Long-term Price Linkages Between the Equity
Markets of Australia and its Major Trading Partners�, Applied Financial Economics,
9, 501-511.
Roca, Eduardo D. (2000) �Price Interdependence Among Equity Markets in the Asia-Pacific
Region: Focus on Australia and ASEAN�, Ashgate, England.
Siklos, P. L. and N. Patrick (2001) �Integration Among Asia-Pacific and International Stock
Markets: Common Stochastic Trends and Regime Shifts�, Pacific Economic Review,
6, 89-110.
Sims, C. A. (1980) �Macroeconomics and Reality�, Econometrica, 48, 1-48.
Soydemir, Gokce (2000) �International Transmission Mechanism of Stock Market
Movements: Evidence from Emerging Equity Markets�, Journal of Forecasting, 19,
149-176.
Taylor, M. P. and Tonks, I. (1989) �The Internationalization of Stock Markets and the
Abolition of UK exchange Controls�, Review of Economics and Statistics, 71, 332-6.
Yuce, Ayse and C. S. Mugan (2000) �Linkages Among Eastern European Stock Markets and
the Major Stock Exchanges�, Russian and East European Finance and Trade, 36, 54-
69.
- - 18
Figure 1: Official Trading Hours of the Seven Markets GMT 0:00 1:00 2:00 3:00 4:00 5:00 6:00 7:00 8:00 9:00 10:00 11:00 12:00 13:00 14:00 15:00 16:00 17:00 18:00 19:00 20:00 21:00 22:00
Tokyo
Singapore
Manila
Kuala Lumpur
Jakarta*
Bangkok
New York
* Jakarta market trades during these hours from Monday to Thursday. On Friday it trades from 0230 to 0400 GMT only. Source: The Handbook of World Stock and Commodity Exchanges (1994) Basil Blackwell, London.
- - 19
Figure 2: Prices in Seven Markets From 03/01/1994 to 29/12/2000
400
600
800
1000
1200
1400
1600
1994 1995 1996 1997 1998 1999 2000
P_US
12000
14000
16000
18000
20000
22000
24000
1994 1995 1996 1997 1998 1999 2000
P_JAP
400
800
1200
1600
2000
2400
2800
1994 1995 1996 1997 1998 1999 2000
P_SP
200
400
600
800
1000
1200
1400
1994 1995 1996 1997 1998 1999 2000
P_MAL
0
400
800
1200
1600
2000
1994 1995 1996 1997 1998 1999 2000
P_THAI
200
300
400
500
600
700
800
1994 1995 1996 1997 1998 1999 2000
P_IND
1000
1500
2000
2500
3000
3500
1994 1995 1996 1997 1998 1999 2000
P_PH
P_US = S & P 500 Composite Stock Index P_Jap = Nikkei 225 Stock Average P_Sp = Singapore Straits Times Index P_Mal = Kuala Lumpur Composite P_Thai = Bangkok S. E. T. P_Ind = Jakarta S. E. Composite P_Ph = Manila S. E. Composite.
- - 20
Figure 3:Returns in Seven Markets From 03/01/1994 to 29/12/2000
-8
-6
-4
-2
0
2
4
6
1994 1995 1996 1997 1998 1999 2000
US
-8
-4
0
4
8
1994 1995 1996 1997 1998 1999 2000
JAP
-10
-5
0
5
10
15
20
1994 1995 1996 1997 1998 1999 2000
SP
-30
-20
-10
0
10
20
30
1994 1995 1996 1997 1998 1999 2000
MAL
-12
-8
-4
0
4
8
12
1994 1995 1996 1997 1998 1999 2000
THAI
-15
-10
-5
0
5
10
15
1994 1995 1996 1997 1998 1999 2000
IND
-10
-5
0
5
10
15
20
1994 1995 1996 1997 1998 1999 2000
PH
US = New York Jap = Tokyo S = Singapore M = Kuala Lumpur T = Bangkok I = Jakarta and P = Manila
- - 21
Figure 4
Linkages Between SEA Markets (Based on Granger Causality Tests with 2 lags)
Before the Crisis Singapore Kuala Lumpur Manila Bangkok Jakarta
After the Crisis Singapore Kuala Lumpur Manila Bangkok Jakarta
- - 22
Figure 4: Impulse Responses to US (New York) innovations
(Pre-crisis Period)
-.1
.0
.1
.2
.3
.4
.5
.6
.7
5 10 15 20 25
Response of US to US
-.05
.00
.05
.10
.15
.20
.25
.30
5 10 15 20 25
Response of SP to US
-.05
.00
.05
.10
.15
.20
.25
.30
5 10 15 20 25
Response of MAL to US
-.05
.00
.05
.10
.15
.20
.25
.30
5 10 15 20 25
Response of THAI to US
-.05
.00
.05
.10
.15
.20
.25
5 10 15 20 25
Response of IND to US
-.05
.00
.05
.10
.15
.20
.25
5 10 15 20 25
Response of PH to US
Response to Cholesky One S.D. Innovations
- - 23
Figure 5: Impulse Responses to US (New York) innovations (Post-crisis Period)
0.0
0.4
0.8
1.2
5 10 15 20 25
Response of US to US
-.1
.0
.1
.2
.3
.4
.5
.6
.7
.8
5 10 15 20 25
Response of SP to US
-.1
.0
.1
.2
.3
.4
.5
.6
.7
5 10 15 20 25
Response of MAL to US
-.1
.0
.1
.2
.3
.4
.5
.6
5 10 15 20 25
Response of THAI to US
-.1
.0
.1
.2
.3
.4
.5
.6
5 10 15 20 25
Response of IND to US
-.1
.0
.1
.2
.3
.4
.5
.6
5 10 15 20 25
Response of PH to US
Response to Cholesky One S.D. Innovations
- - 24
Figure 6: Impulse Responses to SP (Singapore) innovations (Pre-crisis Period)
-.010
-.008
-.006
-.004
-.002
.000
.002
5 10 15 20 25
Response of US to SP
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
5 10 15 20 25
Response of SP to SP
-.1
.0
.1
.2
.3
.4
.5
.6
.7
.8
5 10 15 20 25
Response of MAL to SP
-.1
.0
.1
.2
.3
.4
.5
.6
5 10 15 20 25
Response of THAI to SP
-.05
.00
.05
.10
.15
.20
.25
.30
5 10 15 20 25
Response of IND to SP
.00
.05
.10
.15
.20
.25
.30
.35
5 10 15 20 25
Response of PH to SP
Response to Cholesky One S.D. Innovations
- - 25
Figure 7: Impulse Responses to SP (Singapore) innovations (Post-crisis Period)
-.030
-.025
-.020
-.015
-.010
-.005
.000
.005
5 10 15 20 25
Response of US to SP
-0.4
0.0
0.4
0.8
1.2
1.6
2.0
5 10 15 20 25
Response of SP to SP
-.1
.0
.1
.2
.3
.4
.5
.6
.7
.8
5 10 15 20 25
Response of MAL to SP
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
5 10 15 20 25
Response of THAI to SP
-0.2
0.0
0.2
0.4
0.6
0.8
1.0
5 10 15 20 25
Response of IND to SP
-.1
.0
.1
.2
.3
.4
.5
.6
.7
5 10 15 20 25
Response of PH to SP
Response to Cholesky One S.D. Innovations
- - 26
Table 1: Market Value (Total Capitalization in US$)
Market Jan, 1994 June, 1997 Dec, 2000 New York (NY) $3,600b $8,600b $16,450b Tokyo (TK) $3,000b $3,100b $3,585b Singapore (SG) $125b $174b $200b Kuala Lumpur (KL) $205b $287b $127b Jakarta (JK) $40b $107b $31b Bangkok (BK) $110b $66b $30b Manila (MN) $32b $72b $16b Source: Asiaweek: Jan 5, 1994; June 27, 1997; December22, 2000.
Table 2: Correlation Matrix (Pre-Crisis Period)
NY(t-1) TK(t) SG(t) KL (t) BK(t) JK(t) MN(t) NY(t-1) 1.00
TK(t) 0.21 1.00
SG(t) 0.31 0.20 1.00
KL (t) 0.20 0.13 0.67 1.00
BK(t) 0.18 0.06 0.39 0.35 1.00
JK(t) 0.25 0.13 0.40 0.35 0.24 1.00
MN(t) 0.18 0.07 0.31 0.27 0.17 0.33 1.00
Table 3: Correlation Matrix (Post-Crisis Period)
NY(t-1) TK(t) SG(t) KL (t) BK(t) JK(t) MN(t) NY(t-1) 1.00
TK(t) 0.36 1.00
SG(t) 0.39 0.35 1.00
KL (t) 0.25 0.22 0.38* 1.00
BK(t) 0.23 0.24 0.50 0.34* 1.00
JK(t) 0.23* 0.21 0.45 0.26* 0.38 1.00
MN(t) 0.30 0.21 0.48 0.20* 0.40 0.35 1.00
* This correlation coefficient is lower than its pre-crisis counterpart.
- - 27
Table 4: Pair-wise Granger Causality Test Results
(Pre-Crisis Period) 2 Lags 5 Lags 15 Lags 2 Lags 5 Lags 15 LagsNY→TK 24.60*** 9.82*** 4.32*** TK→NY 0.34 0.29 0.61
NY→SG 46.50*** 19.22*** 7.72*** SG→NY 0.15 0.89 1.28
NY→KL 19.03*** 8.47*** 4.46*** KL→NY 1.12 1.00 0.84
NY→BK 15.18*** 6.84*** 3.03*** BK→NY 0.75 0.72 1.74**
NY→JK 31.20*** 12.29*** 4.47*** JK→NY 3.62** 1.47 1.43
NY→MN 16.75*** 7.36*** 3.51*** MN→NY 1.15 0.86 1.27
TK→SG 0.31 0.48 1.10 SG→TK 3.71** 1.84 1.37
TK→KL 0.45 0.64 1.00 KL→TK 2.73* 1.38 2.60***
TK→BK 0.80 0.62 1.13 BK→TK 0.04 0.41 2.30***
TK→JK 1.02 1.09 0.75 JK→TK 0.73 1.43 1.15
TK→MN 1.49 0.86 1.01 MN→TK 0.24 1.26 1.05
SP→KL 2.49* 1.78 1.45 KL→SG 3.18** 1.86* 0.79
SP→BK 3.18** 1.54 0.98 BK→SG 1.77 1.33 0.88
SP→JK 10.09*** 4.22*** 1.98** JK→SG 0.13 0.46 1.12
SG→MN 14.03*** 6.24*** 3.13*** MN→SG 0.72 0.79 1.32
KL→BK 3.47** 1.45 0.81 BK→KL 0.30 2.24** 1.06
KL→JK 15.01*** 6.26*** 2.17*** JK→KL 0.09 2.04* 1.24
KL→MN 10.74*** 5.37*** 2.47*** MN→KL 1.91 2.57** 2.68***
BK→JK 11.07*** 4.55*** 1.96** JK→BK 0.61 0.95 0.55
BK→MN 10.10*** 4.35*** 2.27*** MN→BK 0.68 2.22* 1.24
JK→MN 7.39*** 2.95** 1.71** MN→JK 8.61*** 4.02*** 2.12
***, ** and * mean significant at 1, 5 and 10%, respectively.
- - 28
Table 5: Pair-wise Granger Causality Test Results
(Post-Crisis Period) 2 Lags 5 Lags 15 Lags 2 Lags 5 Lags 15 Lags NY→TK 70.56*** 28.62*** 9.93*** TK→NY 0.91 0.34 0.82
NY→SG 75.67*** 33.19*** 11.58*** SG→NY 0.10 0.26 1.02
NY→KL 29.71*** 12.87*** 5.12*** KL→NY 2.48* 1.59 1.29
NY→BK 25.59*** 11.69*** 3.89*** BK→NY 3.69** 1.93 0.95
NY→JK 26.54*** 12.47*** 4.72*** JK→NY 1.45 0.84 1.07
NY→MN 41.48*** 17.20*** 6.39*** MN→NY 0.92 0.78 1.16
TK→SG 0.55 0.34 0.93 SG→TK 2.38* 1.44 1.52*
TK→KL 0.09 1.21 1.58* KL→TK 2.65* 1.57 1.37
TK→BK 1.20 0.53 0.65 BK→TK 2.75* 1.59 2.07***
TK→JK 0.04 0.66 1.01 JK→TK 1.72 0.77 0.86
TK→MN 1.05 0.59 0.67 MN→TK 1.97 1.21 0.74
SP→KL 3.04** 1.69 0.99 KL→SG 2.51* 1.56 0.95
SP→BK 11.30*** 5.29*** 2.45*** BK→SG 3.45** 2.44** 2.36***
SP→JK 2.71* 1.60 1.16 JK→SG 4.79*** 2.01* 1.26
SG→MN 21.17*** 8.27*** 3.46*** MN→SG 5.68*** 3.41*** 1.74**
KL→BK 0.12 0.19 1.19 BK→KL 4.43** 2.55** 1.42
KL→JK 0.32 0.75 2.14*** JK→KL 12.95*** 5.22*** 2.08***
KL→MN 8.85*** 3.63*** 3.92*** MN→KL 6.23*** 2.78** 1.58*
BK→JK 6.02*** 3.30*** 2.03** JK→BK 5.55*** 2.31** 1.30
BK→MN 17.43*** 7.67*** 4.05*** MN→BK 3.71** 1.91* 1.05
JK→MN 10.56*** 4.54*** 2.66*** MN→JK 3.84** 2.06* 1.53*
- - 29
Table 6: Variance Decomposition with NSKBJM Ordering
Pre-Crisis Period S.E. NY SG KL BK JK MNVariance Decomposition of NY:
1 0.680 100.000 0.000 0.000 0.000 0.000 0.0002 0.686 98.536 0.003 0.101 0.122 1.037 0.2005 0.687 98.209 0.023 0.287 0.124 1.079 0.279
Variance Decomposition of SG: 1 0.890 0.811 99.189 0.000 0.000 0.000 0.0002 0.951 10.210 89.364 0.069 0.270 0.020 0.0675 0.955 10.155 88.707 0.687 0.292 0.037 0.122
Variance Decomposition of KL: 1 1.188 0.332 40.364 59.304 0.000 0.000 0.0002 1.227 4.552 39.322 55.929 0.012 0.013 0.1735 1.232 4.548 39.030 55.977 0.034 0.022 0.389
Variance Decomposition of BK: 1 1.507 0.116 11.265 1.590 87.029 0.000 0.0002 1.551 3.119 11.660 1.747 83.242 0.135 0.0975 1.560 3.553 11.571 2.117 82.349 0.238 0.172
Variance Decomposition of JK: 1 0.782 0.490 11.788 1.146 0.267 86.310 0.0002 0.863 7.443 13.524 3.122 1.482 74.355 0.0735 0.880 7.313 15.114 3.093 1.611 71.518 1.351
Variance Decomposition of MN: 1 1.187 0.058 6.390 0.905 0.050 2.520 90.0772 1.248 3.251 10.112 1.232 0.598 2.536 82.2705 1.259 3.236 10.146 1.275 1.329 3.080 80.934
- - 30
Table 7:Variance Decomposition with NSKBJM Ordering
Post-Crisis Period S.E. NY SG KL BK JK MNVariance Decomposition of NY:
1 1.262 100.000 0.000 0.000 0.000 0.000 0.0002 1.273 98.284 0.005 0.540 0.918 0.175 0.0795 1.276 97.922 0.047 0.708 0.940 0.184 0.200
Variance Decomposition of SG: 1 1.735 1.524 98.476 0.000 0.000 0.000 0.0002 1.914 16.188 82.054 0.269 0.581 0.404 0.5045 1.929 15.944 80.779 0.873 0.652 1.242 0.511
Variance Decomposition of KL: 1 2.498 0.116 9.237 90.647 0.000 0.000 0.0002 2.594 5.935 8.698 84.093 0.086 1.091 0.0975 2.631 5.795 8.501 82.017 0.878 2.407 0.402
Variance Decomposition of BK: 1 2.210 0.304 19.700 2.601 77.395 0.000 0.0002 2.297 5.193 20.027 2.419 71.724 0.599 0.0395 2.319 6.011 19.675 2.529 70.848 0.820 0.118
Variance Decomposition of JK: 1 2.284 0.062 15.094 0.732 2.728 81.384 0.0002 2.402 5.146 14.549 0.747 4.306 75.171 0.0815 2.416 5.190 14.496 0.745 4.862 74.431 0.277
Variance Decomposition of MN: 1 1.761 0.791 13.636 0.011 3.219 1.036 81.3082 1.943 9.506 16.315 1.074 4.391 1.645 67.0695 1.956 9.931 16.180 1.462 4.577 1.660 66.190
- - 31
Table 8: Impulse Responses
Pre-Crisis Period Response of NY: Period NY SG KL BK JK MN
1 0.680 0.000 0.000 0.000 0.000 0.000 2 0.042 -0.004 -0.022 -0.024 0.070 -0.031 3 0.004 -0.009 -0.029 0.000 -0.010 -0.018 4 -0.011 -0.001 -0.005 -0.003 -0.010 0.006 5 0.000 -0.002 -0.001 -0.002 -0.004 -0.001
Response of SG: Period NY SG KL BK JK MN
1 0.080 0.886 0.000 0.000 0.000 0.000 2 0.293 0.149 0.025 0.049 -0.013 -0.025 3 0.004 -0.031 -0.068 -0.014 0.006 0.021 4 -0.014 -0.016 -0.032 -0.005 -0.011 0.007 5 -0.010 -0.005 0.001 0.001 -0.002 -0.003
Response of KL: Period NY SG KL BK JK MN
1 0.068 0.755 0.915 0.000 0.000 0.000 2 0.253 0.150 0.071 0.013 -0.014 -0.051 3 0.019 -0.005 -0.079 -0.018 0.006 0.056 4 -0.001 -0.005 -0.030 -0.004 -0.010 0.014 5 -0.011 -0.005 0.000 0.004 0.000 -0.005
Response of BK: Period NY SG KL BK JK MN
1 0.051 0.506 0.190 1.406 0.000 0.000 2 0.269 0.157 0.077 0.160 -0.057 -0.048 3 0.101 0.030 -0.092 -0.019 0.049 0.042 4 0.033 0.004 -0.033 -0.003 0.014 0.009 5 -0.008 0.000 -0.003 0.005 0.002 -0.001
Response of JK: Period NY SG KL BK JK MN
1 0.055 0.268 0.084 0.040 0.726 0.000 2 0.229 0.169 0.127 0.097 0.160 0.023 3 0.021 0.120 0.020 0.034 0.017 0.093 4 0.028 0.045 -0.016 0.015 -0.010 0.034 5 0.001 0.005 -0.008 0.009 0.004 0.005
Response of MN: Period NY SG KL BK JK MN
1 0.029 0.300 0.113 0.027 0.188 1.126 2 0.223 0.260 0.080 0.093 0.063 0.113 3 -0.024 0.052 0.031 0.105 0.096 -0.023 4 0.008 0.016 -0.008 0.026 0.009 0.014 5 0.005 0.018 -0.003 0.003 0.001 0.021
- - 32
Figure 9: Impulse Responses
Post-Crisis Period Response of NY: Period NY SG KL BK JK MN
1 1.262 0.000 0.000 0.000 0.000 0.000 2 -0.022 -0.009 0.093 0.122 0.053 0.036 3 -0.046 -0.026 0.053 -0.017 -0.012 0.044 4 0.010 0.002 -0.004 0.011 0.003 0.000 5 0.001 0.003 0.000 0.005 0.003 0.001
Response of SG: Period NY SG KL BK JK MN
1 0.214 1.721 0.000 0.000 0.000 0.000 2 0.740 0.206 0.099 0.146 0.122 0.136 3 0.002 -0.002 0.145 0.048 0.176 0.013 4 -0.021 -0.033 0.038 0.018 0.020 0.018 5 -0.008 -0.006 -0.008 0.018 0.008 -0.005
Response of KL: Period NY SG KL BK JK MN
1 -0.085 0.759 2.379 0.000 0.000 0.000 2 0.626 0.094 -0.044 0.076 0.271 0.081 3 -0.002 0.048 0.128 0.226 0.303 0.145 4 0.039 0.023 0.034 0.052 0.034 0.004 5 0.014 -0.015 -0.006 0.035 0.000 -0.014
Response of BK: Period NY SG KL BK JK MN
1 0.122 0.981 0.356 1.944 0.000 0.000 2 0.509 0.307 -0.024 0.061 0.178 0.045 3 0.222 0.031 0.079 0.154 0.107 0.065 4 -0.009 -0.007 0.046 0.030 0.033 0.007 5 -0.002 -0.012 0.005 0.012 0.000 0.003
Response of JK: Period NY SG KL BK JK MN
1 -0.057 0.887 0.195 0.377 2.060 0.000 2 0.542 0.229 0.070 0.326 0.304 0.068 3 0.034 -0.060 -0.003 0.188 0.082 -0.107 4 -0.069 -0.054 0.002 0.004 -0.009 -0.009 5 -0.006 -0.005 -0.018 0.000 -0.012 -0.003
Response of MN: Period NY SG KL BK JK MN
1 0.157 0.650 0.018 0.316 0.179 1.588 2 0.578 0.439 0.200 0.257 0.173 0.093 3 0.135 -0.030 0.119 0.096 0.023 -0.029 4 -0.053 -0.048 0.036 0.010 0.029 0.012 5 -0.009 -0.002 -0.002 0.007 0.006 0.009
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