Description
The purpose of this paper is to examine the short-term stock market interactions between
US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan.
These six economies along with Japan and Australia have the largest stock exchanges in the
Asia-Pacific region. The importance of the US market to the Asian economies is the prime motivation
for a quantitative assessment of its role in this region. The objective of this study is to measure the
dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs)
(Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China
and India) post Asian crisis of 1997 and also to capture the market interactions during the sub-prime
crisis.
Journal of Financial Economic Policy
Dynamic interdependence between US and Asian markets: an empirical study
Sowmya Dhanaraj Arun Kumar Gopalaswamy Suresh Babu M
Article information:
To cite this document:
Sowmya Dhanaraj Arun Kumar Gopalaswamy Suresh Babu M, (2013),"Dynamic interdependence between
US and Asian markets: an empirical study", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 220 - 237
Permanent link to this document:http://dx.doi.org/10.1108/17576381311329670
Downloaded on: 24 January 2016, At: 21:46 (PT)
References: this document contains references to 54 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 305 times since 2013*
Users who downloaded this article also downloaded:
Linyue Li, Nan Zhang, Thomas D. Willett, (2012),"Measuring macroeconomic and financial market
interdependence: a critical survey", J ournal of Financial Economic Policy, Vol. 4 Iss 2 pp. 128-145 http://
dx.doi.org/10.1108/17576381211228989
J ames R. Barth, Gerard Caprio, Ross Levine, (2013),"Bank regulation and supervision in 180
countries from 1999 to 2011", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 111-219 http://
dx.doi.org/10.1108/17576381311329661
Amit Ghosh, (2013),"Exchange rate flexibility in Latin America", J ournal of Financial Economic Policy, Vol. 5
Iss 2 pp. 238-250http://dx.doi.org/10.1108/17576381311329689
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Dynamic interdependence
between US and Asian markets:
an empirical study
Sowmya Dhanaraj and Arun Kumar Gopalaswamy
Department of Management Studies, IIT Madras, Mumbai, India, and
Suresh Babu M
Department of Humanities and Social Sciences, IIT Madras, Mumbai, India
Abstract
Purpose – The purpose of this paper is to examine the short-term stock market interactions between
US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan.
These six economies along with Japan and Australia have the largest stock exchanges in the
Asia-Paci?c region. The importance of the US market to the Asian economies is the prime motivation
for a quantitative assessment of its role in this region. The objective of this study is to measure the
dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs)
(Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China
and India) post Asian crisis of 1997 and also to capture the market interactions during the sub-prime
crisis.
Design/methodology/approach – The study has employed Granger causality tests and
generalized forecast error variance decomposition (FEVD) analysis to analyze the ?uctuations in
and the extent of short-term interdependence between the US and Asian economies. VAR model was
estimated to run the simulations for FEVD analysis.
Findings – The empirical results from FEVD analysis revealed the dominance of US stock market
on Asian markets; the USA being a large economy of the world, an important trading partner and
major supplier of capital to Asian region. Stock markets of Asia are not immune to the shocks
originating in the USA although the effects of shocks vary considerably across markets. Further,
an important implication is that major crisis events can in?uence the relationship among stock
markets.
Originality/value – This is one of the ?rst papers in the Asian context examining the
interdependence with the US markets. Hence, even though most of the Asian economies went
through liberalization, the macroeconomic and ?nancial circumstances were very different before,
after and during the process. This motivated the examination of the interactions between US and other
Asian markets.
Keywords United States of America, Asia, Emerging markets, Newly industrialised economies,
Stock markets, Dynamic market interdependence, US and Asian newly industrialized economies,
Emerging market economies, Granger causality, Forecast error variance decomposition
Paper type Research paper
I. Introduction
Capital markets in Asia have generated considerable interest among regional as well as
global investors given the rapid increase in the level of economic activities and growing
foreign investments in the region. In just a span of one decade, i.e. from 1999 to 2009,
volumes of trade, market capitalization and the number of listed companies grew
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F4, G1
Journal of Financial Economic Policy
Vol. 5 No. 2, 2013
pp. 220-237
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311329670
JFEP
5,2
220
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
manifold; Asia-Paci?c region’s share of the world market capitalization jumped from
around 20 to 30 per cent (US$13,623 billion); and now the region has around 46 per cent
(i.e. 20,983 companies) of the world’s listed companies[1].
The gradual liberalization and globalization of stock markets and advances in
information technology (IT) have contributed to increased integration between national
stock markets ( Jeon and Chiang, 1991). This has led to a growing similarity in reactions
towards changes in macroeconomic policies and the global ?nancial environment,
thereby having signi?cant implications for asset pricing and international portfolio
diversi?cation. This study attempts to examine the short-termstock market interactions
between US and the six major Asian markets – China, India, Hong Kong, Singapore,
South Korea and Taiwan[2]. These six economies along with Japan and Australia
have the largest stock exchanges in the Asia-Paci?c region. The importance of the US
market to the Asian economies is the prime motivation for a quantitative assessment of
its role in this region.
Globalization has increased the interrelationship between economies, primarily in
terms of growing trade ?ows and capital ?ows. Despite surging intra-regional trade,
Asian economies continue to depend heavily on the USAas major of market. The USAis
the major trading partner contributing to over 20 per cent of the total merchandise trade
of the Asia-Paci?c region, and also accounted for more than 10 per cent of the Foreign
Direct Investment (FDI) in Asian economies between 1991 and 2008. The six Asian
economies (China, India, Hong Kong, Singapore, SouthKorea andTaiwan) accountedfor
more than 50 per cent of the total trade and investment of the USAwith the Asia-Paci?c
region. Darrat and Zhong (2002) studied the in?uence of established markets of
Japan and the USA on 11 Asian emerging markets for the period 1987-1999 and
concluded that US market was the permanent driving force of Asian emerging markets
while Japan has only temporary effects. Echoing these views, Arshanapalli et al. (1995)
and Baharumshah et al. (2003) also concluded that Asian markets were more integrated
with the USA than Japan.
In this context, this study attempts to measure the dynamic stock market
interdependence between the US market and the Asian NIEs (Hong Kong, Singapore,
South Korea and Taiwan) and EMEs (China and India) post the Asian crisis of 1997
and also capture the market interactions during the sub-prime crisis. Granger-causality
tests and generalized forecast error variance decomposition (FEVD) analysis are used
to analyse the ?uctuations and the extent of short-term interdependence between the
US and Asian economies. The study is organised as follows: Section II highlights the
existing studies on the extent of stock market integration of US and Asian markets;
Section III gives a brief description of the measures of time-varying integration among
world’s equity markets; Section IV describes sample data; Section V explains the
methodology used and results obtained, and the conclusions and implications of the
study based on the results obtained are discussed in Section VI.
II. Literature review
Existing literature throws light on the sensitivity of the four Asian NIEs to US stock
price movements. Cha and Oh (2000) studied weekly stock indices of the USA, Japan
and the four Asian NIEs for the 1980-1998 period and reported that after the October
1987 stock market crash the US market began to have a signi?cant impact on the
Hong Kong and Singapore markets, but its in?uence on Taiwan and South Korea
Dynamic
interdependence
221
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
remained unchanged. The impact of the US market on Taiwan and South Korea
increased dramatically since the 1997 Asian ?nancial crisis, while the impact of Japan on
the four Asian NIEs was relatively low. Singapore and Hong Kong had opened up their
stock markets earlier and guaranteed almost free capital ?ows in and out. On the other
hand, Korea and Taiwan had historically put many restrictions on international capital
?ows and foreign ownership until the liberalization in the early 1990s[3]. Therefore, the
in?uence of the US market was directly transmitted to the Hong Kong and Singapore
markets before the 1997 crisis, whereas it increased sharply for Korean and Taiwanese
markets after the crisis. Cha and Cheung (1998) used the vector autoregressive (VAR)
approach to model weekly stock indices for the period 1975-1992 and found that US’
in?uence on Singapore and Hong Kong was signi?cant but insigni?cant for the other
two Asian NIEs. Choudry et al. (2007) on the other hand, used cointegration analysis of
daily data for the period 1988-2003 and reported that the in?uence of the USA on all the
four markets further intensi?ed post the 1997 crisis. Batareddy et al. (2012) used the ten
year data period from 1998 to 2008 and suggested that the chosen Asian markets
cointegrated withthe USAandJapan, post the Asian ?nancial crisis. However, there was
only one cointegrating vector among the ?ve stock markets, leaving four common
stochastic trends among the ?ve variables. The study suggested that the four Asian
emerging stock markets are not insulated from exogenous shocks originating from the
USAand Japan. However, the effects of global or regional in?uences differ considerably
across these markets. Gopalaswamy et al. (2010a) focused on the price spillover effects
based on Wavelets for India, China, Taiwan, South Korea, the USA and Japan. It was
found that there was a price spillover effect from the USA to the Asian emerging
markets, but the converse was not true. Price spillover from the Japanese to the South
Korean market was more than that of anyother Asian emerging markets. In comparison,
China experienced the least price spillover effect fromboth Japan and the USA. Thus, the
literature reinforces the popular perception that despite the presence of the regional
leader, Japan, the US stock market is the most exogenous market and best predicts the
Asian markets.
With the growth rates of the four Asian tigers slowing (to around 5 per cent) since
early 2000, attention has been increasingly shifting to China and India due to their
rapid economic transformations in the recent decades (average growth rates of China
and India were at 10 and 7 per cent, respectively, during the period 2000-2008).
Using monthly data stock indices, Yang et al. (2003) proved that during 1985-2001,
there was no long-termrelationship between the stock markets of the USAand India, but
found clear evidence of cointegration between the two markets during the 1997-1998
emerging market crisis. Similarly, Ghosh et al. (1999) found evidence of cointegration
between US and Indian markets during the Asian crisis of 1997. Wong et al. (2005) and
Lamba (2005) also con?rmed that the Indian stock markets were signi?cantly in?uenced
by the developed markets of the USA, the UK and Japan, both in the long run and short
run, post the 1997 crisis period. Thus, clearly a unidirectional causality existed fromthe
developed markets to the Indian market, with the US exerting maximum in?uence
followed by the UK and Japanese markets.
Despite its huge economic growth and ?nancial development, China’s stock markets
remain largely isolated from the world stock markets. Bahng and Shin (2003) employed
the VAR modelling of daily stock indices of China, Korea, Japan and the USA for the
period of 1990 and 2000 and found that while the USA was highly integrated with the
JFEP
5,2
222
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
capital markets of Korea and Japan, it had a very low in?uence on Mainland China.
Similarly, Huang et al. (2000) and Hsiao et al. (2003) also used cointegration and VAR
modelling, respectively, and concluded that the US markets had little or no impact on
Chinese markets. However, inarecent studycoveringthe period1993-2006, Ozdemir et al.
(2009) found signi?cant causality fromthe USAto China by using the US dollar value of
daily closing prices of Shangai and S&P500 price index. Gopalaswamy et al. (2010b)
studied the role of Chinese dominance in the East Asia region following the free trade
agreement between ASEAN and China. The data was divided into two sub-periods,
1998-2006 and 2007-2010, to enable comparison of the dominance role before and after
the global ?nancial crisis. The results indicated that for the ?rst sub-period 1998-2006
the USA dominated the regional markets with the Chinese market being exogenous;
whereas in the post global crisis period, though the US market remained signi?cant it
was gradually declining; on the other hand, the growing in?uence of Chinese markets
on the other regional markets was perceptible. Owing to mixed results, it can be
concluded that the process of integration between Chinese and US stock markets is far
from complete.
Masih and Masih (1999) argued that the price leadership of the USA may be
attributed to two factors: ?rst, the US market, with its dominance in the global market,
is also the most in?uential producer of information; second, international investors
often overreact to news from the USA and undervalue information from other markets.
Hence, innovations in US market in?uence Asian markets signi?cantly while the
reciprocal relationships have been minimal.
This research complements and adds to the existing empirical literature on stock
market integration signi?cantly. First, this study contextualises the different measures
of stock market integration used in the literature. Second, both long-term and
short-term interactions between US and individual Asian economies for the post
Asian crisis period and the sub-prime crisis period, i.e. 1999-2009 are examined. Third,
this study measures the interdependence for every quarter instead of the whole
period to observe the ?uctuations in the interactions between the US and Asian stock
markets.
III. Measures of stock market integration
The term “stock market integration” in ?nancial economics represents a broad area of
research. There are two basic approaches to measuring international stock market
integrationfromthe price-basedperspective. The ?rst approachis a direct one invokingthe
law of one price and measures the degree of price equality. The second, an indirect
approach, measures the degree of price comovement or interdependence between equity
markets in the short run or long run. Many studies have focused on investigating this
empirical relationship between national stock markets, for which ?ndings of comovements
or interdependence are essentially a loose measure of market integration. Keeping the
relevant literature in mind, the different methods used to measure time-varying market
linkages are presented below.
3.1 Direct approach – ex-ante framework
Although widely used by empiricists, the concept of “equity market integration” is
embedded in asset pricing theory. From the asset pricing point of view, integration is
de?ned as a process of enabling the convergence of returns on the assets of similar risks
Dynamic
interdependence
223
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
across all markets. Hence, studies based on international asset pricing models test the
hypothesis of identical asset returns across markets on a risk-adjusted basis and offer a
fundamental ex-ante framework. According to Kearney and Lucey (2004), the dif?culty
with asset pricing models and its derivatives is their sensitivity to distinct regional/local
risks such as currency risk, political risk, etc. in model speci?cation. They also assume
a degree of integration or segmentation between markets.
3.2 Indirect approach – ex-post framework
Bracker andKoch (1999) used simple correlations of daily asset returns between national
markets to measure the daily comovement. This is a straightforward approach and has
been widely used as an indirect measure of market integration (Pretorius, 2002; Tavares,
2009; Lucey and Zhang, 2010). However, Forbes and Rigobon (2002) criticized the simple
correlation measures of comovements as they contain heteroskedasticity bias.
Hence, in recent studies, estimations of time-varying conditional correlations take
into account the persistence in the conditional means and variances of stock prices and
also avoid the heteroskedasticity bias. Kizys and Pierdzioch (2006) used the bivariate
GARCH model to estimate conditional correlations and Kim et al. (2005) employed an
EGARCH model to account for the difference in positive and negative shocks and fat
tails in equity returns.
Bailey and Choi (2003) argued that correlations per se do not indicate any links
between a pair of markets and rather neglected the possibility of ?uctuations attributable
to global shocks. For example, markets that are subject to the same exogenous shocks
(such as commodity market changes or political events) will arti?cially enhance the
appearance of international equity market integration to the extent that the comovement
in returns will occur without integration.
It is in this context that another indicator of stock market interdependence – FEVD
obtained by VAR modelling of asset returns was used by a number of empirical
studies ( Janakiramanan and Lamba, 1998; Dekker et al., 2001; Cha and Oh, 2000; Tan
and Tse, 2002). This provides an alternative method of depicting system dynamics
and measures the relative importance of each market in generating ?uctuations in
the returns of its own market and the other markets as well (Liu et al., 2006; Lin and
Cheng, 2008).
Bracker et al. (1999) used an alternative approach – the Geweke feedback measures
to account for comovements on the same days and also the lead-lag relationships
across days. Johnson and Soenen (2003) argued that feedback measures re?ect the
magnitude of increase or decrease in market integration and provided a more
appropriate framework than the VAR model which suffers from speci?cation bias.
Parallel to these, there are many studies that use cointegration measures to assess
the degree of integration of equity markets (Chan et al., 1992; Siklos and Ng, 2001;
Choudry et al., 2007; Lim, 2009). Cointegration tests determine whether stock prices
of different national markets exhibit comovements over the long run while allowing
for short run divergence. Colthup and Zhong (2005) used trace test statistics derived
from recursive cointegration to determine the factors driving long run comovements.
However, one drawback as pointed out by Awokuse et al. (2009) is that recursive
estimation with a growing window of data assumes that the system is evolving into
some ?nal form and thus is inadequate in tracking time-varying parameters. Table I
presents the aspects of market integration represented by these measures.
JFEP
5,2
224
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
In this study, the FEVD analysis obtained by VAR modelling of stock returns was
used to determine the short-term interactions between the US and Asian markets. The
study did not focus on other methods like correlation because they do not necessarily
imply causation and high correlations can be a result of spurious relationship leading
to erroneous conclusions. Similarly, Geweke measures also neglect the possibility
of ?uctuations attributable to global shocks. On the other hand, the FEVD technique
uses simulations on an estimated VAR model and thereby measures the responses of a
given market to innovations in other markets. This method has been extensively used
by researchers investigating the extent of stock market interdependence
( Janakiramanan and Lamba, 1998; Dekker et al., 2001; Sheng and Tu, 2000).
However, the shortcoming of the traditional approach of FEVD analysis is that the
results vary according to the order of variables in the VAR model. This is overcome
by employing the recently developed generalized FEVD analysis of Pesaran and
Shin (1998).
IV. Data
To capture the interdependence among the stock markets of US and six major Asian
economies, data on daily closing stock indices fromJanuary 1, 1999 to December 31, 2009
in local currencies was used. Voronkova (2004) suggested that monthly or even weekly
data may actually be too long and would obscure the interactions that may last only for
fewdays. Inaddition, the indices were applied in local currencies to restrict their changes
to movements in security prices and thereby avoid distortion due to exchange rate
movements (Choudry et al., 2007). The indices chosen to represent the stock markets of
each Asian economy were as follows: Shangai SE Composite Index (China), Bombay SE
SENSEX (India), Hang Seng Index (Hong Kong), Straits Times Index (Singapore),
Korean SE Composite Index (South Korea), Taiwan SE Corp. Weighted Index (Taiwan)
and Dow Jones Industrial Average (USA).
Since the study intended to address the stock market interdependence post Asian
crisis and post liberalization for most economies, the time period chosen for this study
was from January 1999 to December 2009. In addition, the time period also captures the
recent experiences of sub-prime crisis in 2008. Data on closing prices of stock indices
was collected from Yahoo ?nance (www.?nance.yahoo.com). To make the trading days
of each Asian market and US market consistent, the price from the last business day
was considered when either of the stock exchanges was closed for a national holiday,
non-working day, etc. The stock exchanges of the Asian economies considered in this
study close when the New York Exchange opens for each day. Given the time
Measure Aspects of market integration represented
Correlation Same day comovement of stock returns between a pair national markets
Geweke feedback 1. Same day comovement of stock returns
2. Lead-lag relationships of stock returns across days
FEVD – VAR Dynamic responses of one market to innovations in other markets
Cointegration Long run relationship between stock prices while allowing for short-term
deviations
Integration index-asset
pricing
Convergence of rates of return of stocks with similar risk across markets
Table I.
Measures of equity
market integration
Dynamic
interdependence
225
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
difference between the US and Asian markets, a shock in the US market during a day t
will not be re?ected in the Asian stock market until day t þ 1. However, a change in
the Asian markets during day t will be re?ected in the US market the same day. Thus,
the appropriate pairing of time t 2 1 for the USA and time t for the Asian markets was
adopted as suggested by Liu et al. (1998).
All daily indices were transformed into daily rates of return in the empirical
estimations, and were calculated as a difference in the natural logarithms as follows:
R
it
¼ ln P
it
2 ln P
it21
, where R
it
denotes the rate of returnof the ithmarket ondayt, and
P
it
(P
it21
) denotes the stock index on day t (t 2 1). The descriptive statistics presented in
Table II was calculated with the daily rates of return series.
V. Methodology and results
5.1 Unit root tests
VAR modelling cannot be applied if the variables used are non-stationary as it may
lead to spurious regression results. As a preliminary step, each series was checked
for a unit root using the ADF and PP tests with and without trend. The selection of
optimal lag length was determined by minimizing AIC and using the critical values
of MacKinnon (1996). The results of t-statistics for the unit root tests of log levels and
?rst differences of daily stock indices of the six Asian stock markets and the US
market are presented in Table III. The results indicated that for every stock price
index the unit root hypothesis was not rejected at 1, 5 and 10 per cent signi?cance
levels; whereas, tests performed on the ?rst differences of log stock prices strongly
indicated that each of the ?rst-differenced series was stationary. The evidence
supports that all stock price index series contain a single unit root, i.e. they are
integrated of order one.
5.2 Granger-causality tests
In an initial attempt to check for presence of short-term stock market comovements, the
relationships of the bivariate Granger-causality were analyzed. Suppose Y
t
and X
t
as
US and any Asian stock price index, respectively. Testing causal relation between the
two series can be based on the following bivariate autoregression:
Y
t
¼ a
0
þ
X
n
k¼1
a
k
Y
t2k
þ
X
n
k¼1
b
k
X
t2k
þ1
y;t
; ð5:1Þ
Stock market
index of DJI SSE BSE HSI STI KOSPI TSEC
Mean 4.9 £ 10
25
0.0003 0.0006 0.0002 0.0002 0.0004 8.5 £ 10
25
Median 0.0001 0.0000 0.0009 0.0000 0.0001 0.0006 0.0000
Maximum 0.1050 0.0940 0.1599 0.1340 0.0753 0.1128 0.0851
Minimum 20.082 20.0926 20.1181 20.136 20.092 20.128 20.0994
SD 0.0128 0.0167 0.0176 0.0168 0.0135 0.0190 0.0162
Skewness 20.104 20.0425 20.0975 20.020 20.334 20.401 20.1304
Kurtosis 11.191 7.3053 8.6784 10.242 8.0978 6.9581 5.5834
Jarque-Bera 7,866.5 2,207.4 3,782.4 6,169.6 3,117.3 1,928.8 796.73
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Table II.
Summary statistics of
daily rates of return
JFEP
5,2
226
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
X
t
¼ f
0
þ
X
n
k¼1
f
k
Y
t2k
þ
X
n
k¼1
u
k
X
t2k
þ1
x;t
; ð5:2Þ
where a
0
and f
0
are constants, a
k
, b
k
, f
k
, u
k
are estimated coef?cients, 1
y, t
and 1
x, t
are
uncorrelated disturbance terms with zero means and ?nite variances.
The null hypothesis that X
t
does not Granger-cause Y
t
is rejected if the b
k
coef?cients in equation (5.1) are jointly different from zero and this can be tested using
the standard F-test. Similarly, Y
t
Granger-causes X
t
, if the f
k
coef?cients are jointly
different from zero in equation (5.2).
Granger-causality test requires that the data series are stationary; otherwise
in?uence from the F-statistics of causality test might be spurious because the test
statistics will have non-standard distributions. Since, both the ADF test and PP
test proved that the log levels of stock price indices were non-stationary and the ?rst
differences (log returns) were stationary, the latter was used in the causality tests.
The causality test requires that an error correction term be incorporated into the test
if the non-stationary log levels of indices are cointegrated. Granger (1988) indicated
that causality tests might reach incorrect conclusions if they failed to include an error
correction term of the cointegrated series. Thus, if the variables are not cointegrated,
the VAR model can be used because there is no need of an error-correction model.
The maximum likelihood method of Johansen (1991) and Johansen and Juselius
(1990) were used to examine whether the Dow Jones index and the stock price index
series of each Asian market are cointegrated. Table IV presents the results for
trace test and maximum eigen value test. While the trace test evaluates the null
hypothesis of at most r cointegrating vectors versus the general p cointegrating
vectors, maximum eigen value test evaluates the null hypothesis of r cointegrating
vectors versus the alternative hypothesis of r þ 1 cointegrating vectors. The results
indicate no evidence of cointegration on a bilateral basis between the US and Asian
stock markets.
Without trend With trend
ADF PP ADF PP
Stock
index Levels Returns Levels Returns Levels Returns Levels Returns
ln SSE 20.932 253.45
* * *
20.975 253.47
* * *
21.252 253.44
* * *
21.297 253.46
* * *
ln BSE 20.483 249.92
* * *
20.446 249.85
* * *
21.671 249.91
* * *
21.599 249.85
* * *
ln HSI 21.528 253.95
* * *
21.469 253.98
* * *
21.959 253.94
* * *
21.895 253.97
* * *
ln STI 21.487 251.40
* * *
21.556 251.46
* * *
21.487 251.39
* * *
21.786 251.45
* * *
ln
KOSPI
21.325 252.21
* * *
21.298 252.21
* * *
22.255 252.20
* * *
22.230 252.20
* * *
ln TSEC 21.776 251.97
* * *
21.874 252.03
* * *
21.863 251.97
* * *
21.958 252.02
* * *
ln DJIA 22.246 232.69
* * *
22.377 2133.4
* * *
22.234 232.69
* * *
22.352 2133.4
* * *
Notes: Rejection of null hypothesis at:
*
10,
* *
5 and
* * *
1 per cent levels; the critical values of the
ADF test without trend and with trend were 23.43 and 23.96, respectively, at 1 per cent signi?cance
level; the critical values of the PP test without trend and with trend were 23.43 and 23.96,
respectively, at 1 per cent signi?cance level; MacKinnon (1996) critical values were used for the
rejection of unit root hypothesis
Table III.
Unit root tests
Dynamic
interdependence
227
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Since, the variables are not cointegrated; a VAR model was applied to test for
Granger-causality. The number of lags used in the VAR model is important because the
number of lags chosenis expectedtoaffect the empirical results. AICwas usedto determine
the optimal laglength. The selectedlaglengthwas less thanthree for all markets. However,
a lag of three was used to effectively reduce the serial correlation in the residuals. The
results are presented in Table V. Interpretation of the results revealed that Dow Jones
index signi?cantly Granger-causes all the Asian stock market indices and also the stock
prices of Asian markets affect the US market. Thus, the Granger-causality tests proved
the existence of bi-directional short-term interactions between the US and the Asian
stock markets.
5.3 FEVD analysis
Granger-causality tests cannot be used to gauge the relative strength of causality
among the variables. In this study, generalized FEVD analysis was used to measure
the extent to which shocks in one market are explained by the other market in the
Stock market
index
Null
hypothesis
Trace
statistic Probability
a
Null
hypothesis
Max eigen
value Probability
a
SSEC r ¼ 0 6.9691 0.5812 r ¼ 0 5.8785 0.6289
r # 1 1.0906 0.2963 r ¼ 1 1.0906 0.2963
BSE r ¼ 0 6.2997 0.6601 r ¼ 0 6.2749 0.5782
r # 1 0.0248 0.8747 r ¼ 1 0.0248 0.8747
HSI r ¼ 0 8.1179 0.4529 r ¼ 0 6.4733 0.5532
r # 1 1.6446 0.1997 r ¼ 1 1.6446 0.1997
STI r ¼ 0 9.3106 0.3374 r ¼ 0 7.8835 0.3906
r # 1 1.4271 0.2322 r ¼ 1 1.4271 0.2322
KOSPI r ¼ 0 6.6228 0.6219 r ¼ 0 5.6463 0.6589
r # 1 0.9764 0.3231 r ¼ 1 0.9764 0.3231
TSEC r ¼ 0 14.469 0.0709 r ¼ 0 10.313 0.1922
r # 1 4.1563 0.0415 r ¼ 1 4.1563 0.0415
Note:
a
MacKinnon-Haug-Michelis (1999) p-values
Table IV.
Bivariate cointegration
tests between DJIA and
Asian stock indices
Stock market index Null hypothesis F-statistic Probability
SSEC DJIA KSSEC 2.3457 0.021
*
SSEC KDJIA 2.7566 0.007
* *
BSE DJIA KBSE 8.0632 2 £ 10
205
* *
BSE KDJIA 41.213 5 £ 10
226
* *
HSI DJIA KHSI 7.6139 0.000
* *
HSI KDJIA 83.472 6 £ 10
236
* *
STI DJIA KSTI 27.765 4 £ 10
237
* *
STI KDJIA 3.0365 0.003
* *
KOSPI DJIA KKOSPI 6.7843 0.000
* *
KOSPI KDJIA 33.303 4 £ 10
221
* *
TSEC DJIA KTSEC 13.813 4 £ 10
211
* *
TSEC KDJIA 4.7001 0.000
* *
Note: Rejection of null hypothesis at:
*
1 and
* *
5 per cent signi?cance levels
Table V.
Granger-causality tests
JFEP
5,2
228
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
bivariate VAR system. This provides a quantitative measure of short-run dynamic
interdependencies between US and Asian markets.
For each Asian market, the following bivariate VAR model was constructed:
R
t
¼ C þ
X
L
s¼1
b
s
R
t2s
þ e
t
ð5:3Þ
where R
t
is a m £ 1 column vector of daily stock index returns, C and b
s
are,
respectively, m £ 1 and m £ m matrices of coef?cients, L is the lag length, and e
t
is a
m £ 1 column vector of forecast errors of the linear predictor R
t
using past values R
t2s
.
Here, m ¼ 2 (stock indices of US and an Asian market). By construction, e
t
is a serially
uncorrelated error term, however the components of e
t
may be contemporaneously
correlated. That is, E(e
t
) ¼ 0, E(e
t
e
0
t2s
) ¼ 0, for all s – 0 and E(e
t
e
0
t
) ¼ V for all t,
where V ¼ {s
ij
; i; j; ¼ 1; 2; . . . ; m} is m £ m covariance matrix and is positive
de?nite.
By successive substitutions of the right hand side in equation (5.3), the VAR
system can be expressed as the moving average model of innovations as indicated
below:
R
t
¼ m þ
X
1
s¼0
A
s
e
t2s
ð5:4Þ
where m is a m £ 1 vector of constants. This represents R
t
as a linear combination
of current and past one-step ahead forecast errors. The i, jth component of A
s
,
measures the responses of the ith market after a unit random shock in the jth market
in s periods.
To use “isolated” shocks, innovations are orthogonalized using the Cholesky
decomposition in the traditional approach. The results of variance decomposition
obtained by the traditional method are sensitive to the choice of ordering of variables in
the VAR model as orthogonalization of innovations imposes a recursive structure in the
model. Hence, the generalized approach to FEVD which is invariant to the ordering of
the variables was drawn from equation (5.4). The generalized FEVD was calculated as
follows:
u
g
ij
ðkÞ ¼
s
k21
ij
P
k21
s¼0
ðe
0
i
A
s
Ve
j
Þ
2
P
k21
s¼0
e
0
i
A
s
VA
0
s
e
i
ð5:5Þ
The percentage of FEVD of Asian markets explained by the US market and vice versa
was estimated for every quarter of the period 1999-2009. This captures the ?uctuations
in interdependence between these markets. The results of ten-day ahead FEVD analysis
for the US stock markets and the Asian economies are plotted in Figures 1 and 2,
respectively.
The percentage of unexpected variance (innovations) in each Asian market explained
by the US market for every quarter over the period 1999-2009 is plotted in Figure 1.
Several major ?ndings emerged from the analysis. First, a substantial number of
interactions were found to exist among the US and Asian markets. A single market’s
own innovations did not fully account for its own variance, i.e. no market was absolutely
exogenous. Among the Asian markets considered for the study, Hong Kong and
Dynamic
interdependence
229
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 1.
Variance accounted for
by US stock market to
innovations in Asian
stock markets
Hong Kong Singapore
South Korea Taiwan
China India
Notes: The X-axis represents the year; the Y-axis represents the percentage of variance
explained by the US stock market to innovations in Asian markets obtained by FEVD analysis
in a bivariate VAR framework
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
JFEP
5,2
230
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 2.
Variance accounted for by
Asian stock markets to
innovations in the US
stock market
Hong Kong Singapore
South Korea Taiwan
China
India
Notes: The X-axis represents the year; the Y-axis represents the percentage variance
explained by the Asian stock market to innovations in those of US market obtained by
FEVD analysis in a bivariate VAR framework
99 00 01 02 03 04 05 06 07 08 09 99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
0
10
20
30
40
50
0
10
20
30
40
50
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09 99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
Dynamic
interdependence
231
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Singapore markets had signi?cant interactions with the US market followed by
South Korea, Taiwan and India. In contrast, although its markets have been open to
foreign investors since the early 1990s, China was relatively more immune to shocks
from the US market. On an average, the US market accounted for around 20 per cent of
the variance in Hong Kong and Singapore markets; more than 15 per cent in the case of
Korean and Taiwanese markets and 10 and 5 per cent in the case of Indian and Chinese
markets, respectively.
Prior studies indicated signi?cant increase in stock market interdependence during
the crisis period. Similarly, the percentage of variance of Asian markets explained by
the US market indicated a marked increase during the sub-prime crisis period. During
2007-2009, the average variance explained by US market jumped to around 25 per cent
for the Hong Kong and Singapore markets. This was also true for other Asian markets
as the US market accounted for more than 20 per cent of variance in Korean and
Taiwanese markets during the same period. However, the Chinese market by and large
remained unaffected by the sub-prime crisis, since the variance explained by the US
market increased only by 1 per cent during this period. The phenomenon of greater
interdependence between the markets con?rmed the inference of prior research that
equity market integration has progressed over time.
The innovations in US markets accounted by Asian markets are shown in Figure 2.
The contribution of Asian markets to US markets’ innovations remained lower than the
US market’s contribution to Asian markets. On an average, among the Asian markets
considered, Hong Kong, Singapore and Indian markets explained around 15 per cent of
the variance in the US market while other markets explained only around 5 per cent.
Thus, it can be concluded that the USA is the global leader and strongly in?uences the
movements in stock prices of Asia’s emerging and NIEs.
VI. Summary and conclusion
Empirical results fromthe FEVDanalysis revealed that the US stock market dominates
the Asian markets; the reasons for this being that the USAis one of the largest economies
of the world, and is not only an important trading partner but also a major supplier of
capital to the Asian region. Thus, stock markets of Asia are not immune to the shocks
originating in the USA, although the effects of shocks vary considerably across markets.
In addition, as indicated in the earlier studies, an important implication is that major
crises can in?uence the relationship among stock markets. Arshanapalli and Doukas
(1993), and Meric and Meric (1997) comparing the pre- and post-October 1987 periods
demonstrated that the correlation between national stock markets increased and
portfolio diversi?cation bene?ts to investors decreased signi?cantly after the 1987
crash. The 1997-1998 ?nancial crisis is another global event that had a strong effect on
the world’s stock markets. Meric et al. (2001) and Yang et al. (2003) proved that the
crisis affected the comovement patterns of the world’s stock markets signi?cantly
and that the bene?ts of portfolio diversi?cation decreased considerably after the
1997-1998 crisis. Similarly, empirical results proved that US market’s in?uence on
Asian markets increased signi?cantly during the sub-prime crisis period of 2007-2008.
Interestingly, though, US’ in?uence on China remained unaffected even during the
crisis period.
The Hong Kong market, followed by the Singapore market were affected most by the
US shocks, primarily because neither of these countries impose any restrictions on
JFEP
5,2
232
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
equity investment for either foreigners or domestic residents and they also permit
foreign currency to be either imported or exported. Thus, shocks fromthe USAcould be
more directly transmitted to these two ?nancial centers (Cha and Oh, 2000). Though the
Korean, Taiwanese and Indian markets opened in the early 1990s, foreign investors
faced huge restrictions on shares open to investment. There were also limits on
shareholding by foreigners which were gradually relaxed after the Asian crisis and
hence the impact of the US market has increase since then.
The reasons for why the Chinese stock markets are not integrated with the USA and
other countries despite the country’s increased importance in the region and in the world
economy are many. One key factor is that there are two groups of stock transactions
in China; one which is open to foreigners, and the other which is open only for the local
residents; also the Chinese stock markets are not as free as those of other countries.
This may lead to distortion in causality test of the Chinese stock markets with other
markets. Another key factor is the restrictions on international capital ?ows, impeding
foreigners from investing in Chinese domestic equity markets. Although from 2002
onwards, quali?ed foreign institutional investors (QFIIs) are allowed to invest in the
Chinese domestic stock markets, their stakes are subject to severe constraints. At the
same time, domestic investors are also facing huge restrictions on international portfolio
investments (Huyghebaert and Wang, 2009). Thus, the Chinese stock markets have
remained isolated from US in?uence when compared to the other markets in the
Asian-Paci?c region.
The ambiguous economic outcomes of equity market integration are highlighted by
Segot and Lucey (2008). Increased stock market interdependence implies progressing
integration of stock markets. This leads to reduction in cost of capital and increase in the
average price of ?nancial assets but also weakens the attractiveness of international
portfolio diversi?cation (Kearney and Lucey, 2004). Also, stock market integration
accentuates the risk of contagion as problems are likely to be transmitted from
one market segment to another segment. Hsin (2004) examined the mechanism of
transmission of shocks and ?nancial contagion across markets and revealed that
Asia-Paci?c NIEs and EMEs are more susceptible to contagious effects from developed
markets like the USA.
The opening of stock markets to foreign investors has also witnessed a high degree
of concentration, volatility of capital ?ows and the risks of abrupt reversals. The
rami?cations of such volatile and disruptive conditions became evident during the 1997
Asian ?nancial crisis and the recent sub-prime crisis of 2008. Segot and Lucey (2008)
argued that optimal stock market integration depends on a trade-off between cheaper
capital and ?nancial stability. Thus, the process of equity market integration has to be
monitored carefully and policymakers should consider institutional development and
corporate governance reforms before further liberalizing their ?nancial system.
Notes
1. Source: World Federation of Exchanges.
2. China and India are categorized as emerging market economies (EME) while Hong Kong,
Singapore, South Korea and Taiwan are categorized as newly industrialized economies
(NIE). Source: World Economic Outlook Database, 2010 of IMF.
3. The of?cial liberalization dates of Hong Kong, Singapore, South Korea and Taiwan are
January 1973, June 1978, January 1992 and January 1991 (Phylaktis and Ravazzolo, 2002).
Dynamic
interdependence
233
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
References
Arshanapalli, B. and Doukas, J. (1993), “International stock market linkages: evidence from
the pre- and post-October 1987 period”, Journal of Banking & Finance, Vol. 17,
pp. 193-208.
Arshanapalli, B., Doukas, J. and Lang, L. (1995), “Pre and post October 1987 stock market
linkages between US and Asian markets”, Paci?c-Basin Finance Journal, Vol. 3,
pp. 57-73.
Awokuse, T.O., Chopra, A. and Bessler, D.A. (2009), “Structural change and international stock
market interdependence: evidence from Asian emerging markets”, Economic Modelling,
Vol. 26, pp. 549-559.
Baharumshah, A.Z., Sarmidi, T. and Tan, H.B. (2003), “Dynamic linkages of Asian stock
markets”, Journal of Asia Paci?c Economy, Vol. 8, pp. 180-209.
Bahng, J.S. and Shin, S. (2003), “Do stock price indices respond asymmetrically? Evidence from
China, Japan, and South Korea”, Journal of Asian economics, Vol. 14, pp. 541-563.
Bailey, W. and Choi, J. (2003), “International market linkages”, Journal of Economics and
Business, Vol. 55, pp. 399-404.
Batareddy, M., Gopalaswamy, A.K. and Huang, C.H. (2012), “The stability of long-run
relationships: a study on Asian emerging and developed stock markets ( Japan and US)”,
International Journal of Emerging Markets, Vol. 7, pp. 31-48.
Bracker, K. and Koch, P. (1999), “Economic determinants of correlation structure across
international equity markets”, Journal of Economics and Business, Vol. 51, pp. 443-471.
Bracker, K., Docking, D. and Koch, P. (1999), “Economic determinants of evolution in
international stock market integration”, Journal of Empirical Finance, Vol. 6, pp. 1-27.
Cha, B. and Cheung, Y. (1998), “The impact of the US and the Japanese equity markets on the
emerging Asia-Paci?c equity markets”, Asia-Paci?c Financial Markets, Vol. 5,
pp. 191-209.
Cha, B. and Oh, S. (2000), “The relationship between developed equity markets and the paci?c
basin’s emerging equity markets”, International Review of Economics Finance, Vol. 9,
pp. 299-322.
Chan, K.C., Gup, B.E. and Pan, M. (1992), “An empirical analysis of stock prices in major Asian
markets and the United States”, Financial Review, Vol. 27, pp. 289-307.
Choudry, T., Lu, L. and Peng, K. (2007), “Common stochastic trends among Far East stock prices:
effects of the Asian ?nancial crises”, International Review of Financial Analysis, Vol. 16,
pp. 242-261.
Colthup, G. and Zhong, M. (2005), “Analyses of equity market linkages in the paci?c basin”,
paper presented at Afaanz 2005 Conference, Melbourne, July 3-5.
Darrat, A.F. and Zhong, M. (2002), “Permanent and transitory driving forces in the Asian-Paci?c
stock markets”, Financial Review, Vol. 37, pp. 35-51.
Dekker, A., Sen, K. and Young, M.R. (2001), “Equity market linkages in the Asia Paci?c region:
a comparison of the orthogonalised and generalised VAR approaches”, Global Finance
Journal, Vol. 12, pp. 1-33.
Forbes, K. and Rigobon, R. (2002), “No contagion, only interdependence: measuring stock market
co-movements”, Journal of Finance, Vol. 57, pp. 2223-2261.
Ghosh, A., Saidi, R. and Johnson, K. (1999), “Who moves the Asia-Paci?c stock markets – US or
Japan? Empirical evidence based on the theory of cointegration”, Financial Review, Vol. 34,
pp. 159-170.
JFEP
5,2
234
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Gopalaswamy, A.K., Huang, C.H. and Batareddy, M. (2010a), “Price spillover effects from
developed to Asian emerging stock markets: wavelet analysis”, Working Paper Series,
Asian Institute of Technology, Thailand.
Gopalaswamy, A.K., Kumar, S.S.S. and Huang, C.H. (2010b), “A study on the changing role of
Chinese stock market among the East Asian stock market group”, Working Paper Series,
Asian Institute of Technology, Thailand.
Granger, C.W.J. (1988), “Some recent development in a concept of causality”, Journal of
Econometrics, Vol. 39, pp. 199-211.
Hsiao, F.S.T., Hsiao, M.W. and Yamashita, A. (2003), “The impact of the US economy on
the Asia-Paci?c region: does it matter?”, Journal of Asian Economics, Vol. 14,
pp. 219-241.
Hsin, C.W. (2004), “A multilateral approach to examining the comovements among major world
equity markets”, International Review of Financial Analysis, Vol. 13, pp. 433-462.
Huang, B.N., Yang, C.W. and Hu, J.W.S. (2000), “Causality and cointegration of stock markets
among the United States, Japan and the South China growth triangle”, International
Review of Financial Analysis, Vol. 9, pp. 281-297.
Huyghebaert, N. and Wang, L. (2009), “The co-movement of stock markets in East Asia: did the
1997-1998 Asian ?nancial crisis really strengthen stock market integration?”, China
Economic Review, Vol. 21, pp. 98-112.
Janakiramanan, S. and Lamba, A.S. (1998), “An empirical examination of linkages between
Paci?c-Basin stock markets”, Journal of International Financial Markets, Institutions and
Money, Vol. 8, pp. 155-173.
Jeon, B.N. and Chiang, T.C. (1991), “A system of stock prices in world stock exchanges:
common stochastic trends for 1975-1990”, Journal of Economics and Business, Vol. 43,
pp. 329-338.
Johansen, S. (1991), “Estimation and hypothesis testing of cointegration vectors in Gaussian
vector autoregressive models”, Econometrica: Journal of the Econometric Society, Vol. 59,
pp. 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, Vol. 52, pp. 169-210.
Johnson, R. and Soenen, L. (2003), “Economic integration and stock market comovement in
Americas”, Journal of Multinational Financial Management, Vol. 13, pp. 85-100.
Kearney, C. and Lucey, B.M. (2004), “International equity market integration: theory,
evidence and implications”, International Review of Financial Analysis, Vol. 13,
pp. 571-583.
Kim, S.J., Moshirian, F. and Wu, E. (2005), “Dynamic stock market integration driven by
European monetary integration – an empirical analysis”, Journal of Banking & Finance,
Vol. 29, pp. 2475-2502.
Kizys, R. and Pierdzioch, C. (2006), “Business-cycle ?uctuations and international equity
correlations”, Global Finance Journal, Vol. 17, pp. 252-270.
Lamba, A.S. (2005), “An analysis of the short- and long-run relationships between
South Asian and developed equity markets”, International Journal of Business, Vol. 10,
pp. 383-402.
Lim, L.K. (2009), “Convergence and interdependence between ASEAN-5 stock markets”,
Mathematics and Computers in Simulation, Vol. 79, pp. 2957-2966.
Dynamic
interdependence
235
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Lin, C.H. and Cheng, W.H. (2008), “Economic determinants of comovements across international
stock markets: the example of Taiwan and its key trading partners”, Applied Economics,
Vol. 40, pp. 1187-1205.
Liu, S.Z., Lin, K.C. and Lai, S.M. (2006), “Stock market interdependence and trade relations:
a correlation test for the US and its trading partners”, Economics Bulletin, Vol. 7,
pp. 1-15.
Lucey, B.M. and Zhang, Q.Y. (2010), “Does cultural distance matter in international stock market
comovement? Evidence from emerging economies around the world”, Emerging Markets
Review, Vol. 11, pp. 62-78.
MacKinnon, J.G. (1996), “Numerical distribution functions for unit root and cointegration tests”,
Journal of Applied Econometrics, Vol. 11, pp. 601-618.
Masih, R. and Masih, A.M.M. (1999), “Are Asian stock market ?uctuations due mainly to
intra-regional contagion effects? Evidence based on Asian emerging stock markets”,
Paci?c-Basin Finance Journal, Vol. 7, pp. 251-282.
Meric, I. and Meric, G. (1997), “Co-movements of European equity markets before and after the
1987 crash”, Multinational Finance Journal, Vol. 1, pp. 137-152.
Meric, G., Leal, R.P.C., Ratner, M. and Meric, I. (2001), “Co-movements of US and Latin American
equity markets before and after the 1987 crash”, International Review of Financial
Analysis, Vol. 10, pp. 219-235.
Ozdemir, Z.A., Olgun, H. and Saracoglu, B. (2009), “Dynamic linkages between the center and
periphery in international stock markets”, Research in International Business and Finance,
Vol. 23, pp. 46-53.
Pesaran, H.H. and Shin, Y. (1998), “Generalized impulse response analysis in linear multivariate
models”, Economics Letters, Vol. 58, pp. 17-29.
Phylaktis, K. and Ravazzolo, F. (2002), “Measuring ?nancial and economic integration with
equity prices in emerging markets”, Journal of International Money and Finance, Vol. 21,
pp. 879-904.
Pretorius, E. (2002), “Economic determinants of emerging stock market interdependence”,
Emerging Markets Review, Vol. 3, pp. 84-105.
Segot, T.L. and Lucey, B.M. (2008), “The capital markets of the Middle East and North African
region: situation and characteristics”, Emerging Markets Finance and Trade, Vol. 44,
pp. 68-81.
Sheng, H.C. and Tu, A.H. (2000), “A study of cointegration and variance decomposition among
national equity indices before and during the period of the Asian ?nancial crisis”, Journal
of Multinational Financial Management, Vol. 10, pp. 345-365.
Siklos, P.L. and Ng, P. (2001), “Integration among Asia-Paci?c and international stock
markets: common stochastic trends and regime shifts”, Paci?c Economic Review, Vol. 6,
pp. 89-110.
Tan, K.B. and Tse, Y.K. (2002), “The integration of the East and South-East Asian equity
markets”, Working Paper Series, International Center for the Study of East Asian
Development, Kitakyushu.
Tavares, J. (2009), “Economic integration and the comovement of stock returns”, Economics
Letters, Vol. 103, pp. 65-67.
Voronkova, S. (2004), “Equity market integration in Central European emerging markets:
a cointegration analysis with shifting regimes”, International Review of Financial Analysis,
Vol. 13, pp. 633-647.
JFEP
5,2
236
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Wong, W.K., Agarwal, A. and Du, J. (2005), “Financial integration for Indian stock market,
a fractional cointegration approach”, NUS Working Paper No. 0501.
Yang, J., Kolari, J.W. and Min, I. (2003), “Stock market integration and ?nancial crises: the case of
Asia”, Applied Financial Economics, Vol. 13, pp. 477-486.
Corresponding author
Arun Kumar Gopalaswamy can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
Dynamic
interdependence
237
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Nuruzzaman Arsyad. 2015. Integration between East and Southeast Asian equity markets. Journal of
Financial Economic Policy 7:2, 104-121. [Abstract] [Full Text] [PDF]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_654526159.pdf
The purpose of this paper is to examine the short-term stock market interactions between
US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan.
These six economies along with Japan and Australia have the largest stock exchanges in the
Asia-Pacific region. The importance of the US market to the Asian economies is the prime motivation
for a quantitative assessment of its role in this region. The objective of this study is to measure the
dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs)
(Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China
and India) post Asian crisis of 1997 and also to capture the market interactions during the sub-prime
crisis.
Journal of Financial Economic Policy
Dynamic interdependence between US and Asian markets: an empirical study
Sowmya Dhanaraj Arun Kumar Gopalaswamy Suresh Babu M
Article information:
To cite this document:
Sowmya Dhanaraj Arun Kumar Gopalaswamy Suresh Babu M, (2013),"Dynamic interdependence between
US and Asian markets: an empirical study", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 220 - 237
Permanent link to this document:http://dx.doi.org/10.1108/17576381311329670
Downloaded on: 24 January 2016, At: 21:46 (PT)
References: this document contains references to 54 other documents.
To copy this document: [email protected]
The fulltext of this document has been downloaded 305 times since 2013*
Users who downloaded this article also downloaded:
Linyue Li, Nan Zhang, Thomas D. Willett, (2012),"Measuring macroeconomic and financial market
interdependence: a critical survey", J ournal of Financial Economic Policy, Vol. 4 Iss 2 pp. 128-145 http://
dx.doi.org/10.1108/17576381211228989
J ames R. Barth, Gerard Caprio, Ross Levine, (2013),"Bank regulation and supervision in 180
countries from 1999 to 2011", J ournal of Financial Economic Policy, Vol. 5 Iss 2 pp. 111-219 http://
dx.doi.org/10.1108/17576381311329661
Amit Ghosh, (2013),"Exchange rate flexibility in Latin America", J ournal of Financial Economic Policy, Vol. 5
Iss 2 pp. 238-250http://dx.doi.org/10.1108/17576381311329689
Access to this document was granted through an Emerald subscription provided by emerald-srm:115632 []
For Authors
If you would like to write for this, or any other Emerald publication, then please use our Emerald for
Authors service information about how to choose which publication to write for and submission guidelines
are available for all. Please visit www.emeraldinsight.com/authors for more information.
About Emerald www.emeraldinsight.com
Emerald is a global publisher linking research and practice to the benefit of society. The company
manages a portfolio of more than 290 journals and over 2,350 books and book series volumes, as well as
providing an extensive range of online products and additional customer resources and services.
Emerald is both COUNTER 4 and TRANSFER compliant. The organization is a partner of the Committee
on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive
preservation.
*Related content and download information correct at time of download.
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Dynamic interdependence
between US and Asian markets:
an empirical study
Sowmya Dhanaraj and Arun Kumar Gopalaswamy
Department of Management Studies, IIT Madras, Mumbai, India, and
Suresh Babu M
Department of Humanities and Social Sciences, IIT Madras, Mumbai, India
Abstract
Purpose – The purpose of this paper is to examine the short-term stock market interactions between
US and six major Asian markets – China, India, Hong Kong, Singapore, South Korea and Taiwan.
These six economies along with Japan and Australia have the largest stock exchanges in the
Asia-Paci?c region. The importance of the US market to the Asian economies is the prime motivation
for a quantitative assessment of its role in this region. The objective of this study is to measure the
dynamic stock market interdependence of US and Asian newly industrialized economies (NIEs)
(Hong Kong, Singapore, South Korea and Taiwan) and emerging market economies (EMEs) (China
and India) post Asian crisis of 1997 and also to capture the market interactions during the sub-prime
crisis.
Design/methodology/approach – The study has employed Granger causality tests and
generalized forecast error variance decomposition (FEVD) analysis to analyze the ?uctuations in
and the extent of short-term interdependence between the US and Asian economies. VAR model was
estimated to run the simulations for FEVD analysis.
Findings – The empirical results from FEVD analysis revealed the dominance of US stock market
on Asian markets; the USA being a large economy of the world, an important trading partner and
major supplier of capital to Asian region. Stock markets of Asia are not immune to the shocks
originating in the USA although the effects of shocks vary considerably across markets. Further,
an important implication is that major crisis events can in?uence the relationship among stock
markets.
Originality/value – This is one of the ?rst papers in the Asian context examining the
interdependence with the US markets. Hence, even though most of the Asian economies went
through liberalization, the macroeconomic and ?nancial circumstances were very different before,
after and during the process. This motivated the examination of the interactions between US and other
Asian markets.
Keywords United States of America, Asia, Emerging markets, Newly industrialised economies,
Stock markets, Dynamic market interdependence, US and Asian newly industrialized economies,
Emerging market economies, Granger causality, Forecast error variance decomposition
Paper type Research paper
I. Introduction
Capital markets in Asia have generated considerable interest among regional as well as
global investors given the rapid increase in the level of economic activities and growing
foreign investments in the region. In just a span of one decade, i.e. from 1999 to 2009,
volumes of trade, market capitalization and the number of listed companies grew
The current issue and full text archive of this journal is available at
www.emeraldinsight.com/1757-6385.htm
JEL classi?cation – F4, G1
Journal of Financial Economic Policy
Vol. 5 No. 2, 2013
pp. 220-237
qEmerald Group Publishing Limited
1757-6385
DOI 10.1108/17576381311329670
JFEP
5,2
220
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
manifold; Asia-Paci?c region’s share of the world market capitalization jumped from
around 20 to 30 per cent (US$13,623 billion); and now the region has around 46 per cent
(i.e. 20,983 companies) of the world’s listed companies[1].
The gradual liberalization and globalization of stock markets and advances in
information technology (IT) have contributed to increased integration between national
stock markets ( Jeon and Chiang, 1991). This has led to a growing similarity in reactions
towards changes in macroeconomic policies and the global ?nancial environment,
thereby having signi?cant implications for asset pricing and international portfolio
diversi?cation. This study attempts to examine the short-termstock market interactions
between US and the six major Asian markets – China, India, Hong Kong, Singapore,
South Korea and Taiwan[2]. These six economies along with Japan and Australia
have the largest stock exchanges in the Asia-Paci?c region. The importance of the US
market to the Asian economies is the prime motivation for a quantitative assessment of
its role in this region.
Globalization has increased the interrelationship between economies, primarily in
terms of growing trade ?ows and capital ?ows. Despite surging intra-regional trade,
Asian economies continue to depend heavily on the USAas major of market. The USAis
the major trading partner contributing to over 20 per cent of the total merchandise trade
of the Asia-Paci?c region, and also accounted for more than 10 per cent of the Foreign
Direct Investment (FDI) in Asian economies between 1991 and 2008. The six Asian
economies (China, India, Hong Kong, Singapore, SouthKorea andTaiwan) accountedfor
more than 50 per cent of the total trade and investment of the USAwith the Asia-Paci?c
region. Darrat and Zhong (2002) studied the in?uence of established markets of
Japan and the USA on 11 Asian emerging markets for the period 1987-1999 and
concluded that US market was the permanent driving force of Asian emerging markets
while Japan has only temporary effects. Echoing these views, Arshanapalli et al. (1995)
and Baharumshah et al. (2003) also concluded that Asian markets were more integrated
with the USA than Japan.
In this context, this study attempts to measure the dynamic stock market
interdependence between the US market and the Asian NIEs (Hong Kong, Singapore,
South Korea and Taiwan) and EMEs (China and India) post the Asian crisis of 1997
and also capture the market interactions during the sub-prime crisis. Granger-causality
tests and generalized forecast error variance decomposition (FEVD) analysis are used
to analyse the ?uctuations and the extent of short-term interdependence between the
US and Asian economies. The study is organised as follows: Section II highlights the
existing studies on the extent of stock market integration of US and Asian markets;
Section III gives a brief description of the measures of time-varying integration among
world’s equity markets; Section IV describes sample data; Section V explains the
methodology used and results obtained, and the conclusions and implications of the
study based on the results obtained are discussed in Section VI.
II. Literature review
Existing literature throws light on the sensitivity of the four Asian NIEs to US stock
price movements. Cha and Oh (2000) studied weekly stock indices of the USA, Japan
and the four Asian NIEs for the 1980-1998 period and reported that after the October
1987 stock market crash the US market began to have a signi?cant impact on the
Hong Kong and Singapore markets, but its in?uence on Taiwan and South Korea
Dynamic
interdependence
221
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
remained unchanged. The impact of the US market on Taiwan and South Korea
increased dramatically since the 1997 Asian ?nancial crisis, while the impact of Japan on
the four Asian NIEs was relatively low. Singapore and Hong Kong had opened up their
stock markets earlier and guaranteed almost free capital ?ows in and out. On the other
hand, Korea and Taiwan had historically put many restrictions on international capital
?ows and foreign ownership until the liberalization in the early 1990s[3]. Therefore, the
in?uence of the US market was directly transmitted to the Hong Kong and Singapore
markets before the 1997 crisis, whereas it increased sharply for Korean and Taiwanese
markets after the crisis. Cha and Cheung (1998) used the vector autoregressive (VAR)
approach to model weekly stock indices for the period 1975-1992 and found that US’
in?uence on Singapore and Hong Kong was signi?cant but insigni?cant for the other
two Asian NIEs. Choudry et al. (2007) on the other hand, used cointegration analysis of
daily data for the period 1988-2003 and reported that the in?uence of the USA on all the
four markets further intensi?ed post the 1997 crisis. Batareddy et al. (2012) used the ten
year data period from 1998 to 2008 and suggested that the chosen Asian markets
cointegrated withthe USAandJapan, post the Asian ?nancial crisis. However, there was
only one cointegrating vector among the ?ve stock markets, leaving four common
stochastic trends among the ?ve variables. The study suggested that the four Asian
emerging stock markets are not insulated from exogenous shocks originating from the
USAand Japan. However, the effects of global or regional in?uences differ considerably
across these markets. Gopalaswamy et al. (2010a) focused on the price spillover effects
based on Wavelets for India, China, Taiwan, South Korea, the USA and Japan. It was
found that there was a price spillover effect from the USA to the Asian emerging
markets, but the converse was not true. Price spillover from the Japanese to the South
Korean market was more than that of anyother Asian emerging markets. In comparison,
China experienced the least price spillover effect fromboth Japan and the USA. Thus, the
literature reinforces the popular perception that despite the presence of the regional
leader, Japan, the US stock market is the most exogenous market and best predicts the
Asian markets.
With the growth rates of the four Asian tigers slowing (to around 5 per cent) since
early 2000, attention has been increasingly shifting to China and India due to their
rapid economic transformations in the recent decades (average growth rates of China
and India were at 10 and 7 per cent, respectively, during the period 2000-2008).
Using monthly data stock indices, Yang et al. (2003) proved that during 1985-2001,
there was no long-termrelationship between the stock markets of the USAand India, but
found clear evidence of cointegration between the two markets during the 1997-1998
emerging market crisis. Similarly, Ghosh et al. (1999) found evidence of cointegration
between US and Indian markets during the Asian crisis of 1997. Wong et al. (2005) and
Lamba (2005) also con?rmed that the Indian stock markets were signi?cantly in?uenced
by the developed markets of the USA, the UK and Japan, both in the long run and short
run, post the 1997 crisis period. Thus, clearly a unidirectional causality existed fromthe
developed markets to the Indian market, with the US exerting maximum in?uence
followed by the UK and Japanese markets.
Despite its huge economic growth and ?nancial development, China’s stock markets
remain largely isolated from the world stock markets. Bahng and Shin (2003) employed
the VAR modelling of daily stock indices of China, Korea, Japan and the USA for the
period of 1990 and 2000 and found that while the USA was highly integrated with the
JFEP
5,2
222
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
capital markets of Korea and Japan, it had a very low in?uence on Mainland China.
Similarly, Huang et al. (2000) and Hsiao et al. (2003) also used cointegration and VAR
modelling, respectively, and concluded that the US markets had little or no impact on
Chinese markets. However, inarecent studycoveringthe period1993-2006, Ozdemir et al.
(2009) found signi?cant causality fromthe USAto China by using the US dollar value of
daily closing prices of Shangai and S&P500 price index. Gopalaswamy et al. (2010b)
studied the role of Chinese dominance in the East Asia region following the free trade
agreement between ASEAN and China. The data was divided into two sub-periods,
1998-2006 and 2007-2010, to enable comparison of the dominance role before and after
the global ?nancial crisis. The results indicated that for the ?rst sub-period 1998-2006
the USA dominated the regional markets with the Chinese market being exogenous;
whereas in the post global crisis period, though the US market remained signi?cant it
was gradually declining; on the other hand, the growing in?uence of Chinese markets
on the other regional markets was perceptible. Owing to mixed results, it can be
concluded that the process of integration between Chinese and US stock markets is far
from complete.
Masih and Masih (1999) argued that the price leadership of the USA may be
attributed to two factors: ?rst, the US market, with its dominance in the global market,
is also the most in?uential producer of information; second, international investors
often overreact to news from the USA and undervalue information from other markets.
Hence, innovations in US market in?uence Asian markets signi?cantly while the
reciprocal relationships have been minimal.
This research complements and adds to the existing empirical literature on stock
market integration signi?cantly. First, this study contextualises the different measures
of stock market integration used in the literature. Second, both long-term and
short-term interactions between US and individual Asian economies for the post
Asian crisis period and the sub-prime crisis period, i.e. 1999-2009 are examined. Third,
this study measures the interdependence for every quarter instead of the whole
period to observe the ?uctuations in the interactions between the US and Asian stock
markets.
III. Measures of stock market integration
The term “stock market integration” in ?nancial economics represents a broad area of
research. There are two basic approaches to measuring international stock market
integrationfromthe price-basedperspective. The ?rst approachis a direct one invokingthe
law of one price and measures the degree of price equality. The second, an indirect
approach, measures the degree of price comovement or interdependence between equity
markets in the short run or long run. Many studies have focused on investigating this
empirical relationship between national stock markets, for which ?ndings of comovements
or interdependence are essentially a loose measure of market integration. Keeping the
relevant literature in mind, the different methods used to measure time-varying market
linkages are presented below.
3.1 Direct approach – ex-ante framework
Although widely used by empiricists, the concept of “equity market integration” is
embedded in asset pricing theory. From the asset pricing point of view, integration is
de?ned as a process of enabling the convergence of returns on the assets of similar risks
Dynamic
interdependence
223
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
across all markets. Hence, studies based on international asset pricing models test the
hypothesis of identical asset returns across markets on a risk-adjusted basis and offer a
fundamental ex-ante framework. According to Kearney and Lucey (2004), the dif?culty
with asset pricing models and its derivatives is their sensitivity to distinct regional/local
risks such as currency risk, political risk, etc. in model speci?cation. They also assume
a degree of integration or segmentation between markets.
3.2 Indirect approach – ex-post framework
Bracker andKoch (1999) used simple correlations of daily asset returns between national
markets to measure the daily comovement. This is a straightforward approach and has
been widely used as an indirect measure of market integration (Pretorius, 2002; Tavares,
2009; Lucey and Zhang, 2010). However, Forbes and Rigobon (2002) criticized the simple
correlation measures of comovements as they contain heteroskedasticity bias.
Hence, in recent studies, estimations of time-varying conditional correlations take
into account the persistence in the conditional means and variances of stock prices and
also avoid the heteroskedasticity bias. Kizys and Pierdzioch (2006) used the bivariate
GARCH model to estimate conditional correlations and Kim et al. (2005) employed an
EGARCH model to account for the difference in positive and negative shocks and fat
tails in equity returns.
Bailey and Choi (2003) argued that correlations per se do not indicate any links
between a pair of markets and rather neglected the possibility of ?uctuations attributable
to global shocks. For example, markets that are subject to the same exogenous shocks
(such as commodity market changes or political events) will arti?cially enhance the
appearance of international equity market integration to the extent that the comovement
in returns will occur without integration.
It is in this context that another indicator of stock market interdependence – FEVD
obtained by VAR modelling of asset returns was used by a number of empirical
studies ( Janakiramanan and Lamba, 1998; Dekker et al., 2001; Cha and Oh, 2000; Tan
and Tse, 2002). This provides an alternative method of depicting system dynamics
and measures the relative importance of each market in generating ?uctuations in
the returns of its own market and the other markets as well (Liu et al., 2006; Lin and
Cheng, 2008).
Bracker et al. (1999) used an alternative approach – the Geweke feedback measures
to account for comovements on the same days and also the lead-lag relationships
across days. Johnson and Soenen (2003) argued that feedback measures re?ect the
magnitude of increase or decrease in market integration and provided a more
appropriate framework than the VAR model which suffers from speci?cation bias.
Parallel to these, there are many studies that use cointegration measures to assess
the degree of integration of equity markets (Chan et al., 1992; Siklos and Ng, 2001;
Choudry et al., 2007; Lim, 2009). Cointegration tests determine whether stock prices
of different national markets exhibit comovements over the long run while allowing
for short run divergence. Colthup and Zhong (2005) used trace test statistics derived
from recursive cointegration to determine the factors driving long run comovements.
However, one drawback as pointed out by Awokuse et al. (2009) is that recursive
estimation with a growing window of data assumes that the system is evolving into
some ?nal form and thus is inadequate in tracking time-varying parameters. Table I
presents the aspects of market integration represented by these measures.
JFEP
5,2
224
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
In this study, the FEVD analysis obtained by VAR modelling of stock returns was
used to determine the short-term interactions between the US and Asian markets. The
study did not focus on other methods like correlation because they do not necessarily
imply causation and high correlations can be a result of spurious relationship leading
to erroneous conclusions. Similarly, Geweke measures also neglect the possibility
of ?uctuations attributable to global shocks. On the other hand, the FEVD technique
uses simulations on an estimated VAR model and thereby measures the responses of a
given market to innovations in other markets. This method has been extensively used
by researchers investigating the extent of stock market interdependence
( Janakiramanan and Lamba, 1998; Dekker et al., 2001; Sheng and Tu, 2000).
However, the shortcoming of the traditional approach of FEVD analysis is that the
results vary according to the order of variables in the VAR model. This is overcome
by employing the recently developed generalized FEVD analysis of Pesaran and
Shin (1998).
IV. Data
To capture the interdependence among the stock markets of US and six major Asian
economies, data on daily closing stock indices fromJanuary 1, 1999 to December 31, 2009
in local currencies was used. Voronkova (2004) suggested that monthly or even weekly
data may actually be too long and would obscure the interactions that may last only for
fewdays. Inaddition, the indices were applied in local currencies to restrict their changes
to movements in security prices and thereby avoid distortion due to exchange rate
movements (Choudry et al., 2007). The indices chosen to represent the stock markets of
each Asian economy were as follows: Shangai SE Composite Index (China), Bombay SE
SENSEX (India), Hang Seng Index (Hong Kong), Straits Times Index (Singapore),
Korean SE Composite Index (South Korea), Taiwan SE Corp. Weighted Index (Taiwan)
and Dow Jones Industrial Average (USA).
Since the study intended to address the stock market interdependence post Asian
crisis and post liberalization for most economies, the time period chosen for this study
was from January 1999 to December 2009. In addition, the time period also captures the
recent experiences of sub-prime crisis in 2008. Data on closing prices of stock indices
was collected from Yahoo ?nance (www.?nance.yahoo.com). To make the trading days
of each Asian market and US market consistent, the price from the last business day
was considered when either of the stock exchanges was closed for a national holiday,
non-working day, etc. The stock exchanges of the Asian economies considered in this
study close when the New York Exchange opens for each day. Given the time
Measure Aspects of market integration represented
Correlation Same day comovement of stock returns between a pair national markets
Geweke feedback 1. Same day comovement of stock returns
2. Lead-lag relationships of stock returns across days
FEVD – VAR Dynamic responses of one market to innovations in other markets
Cointegration Long run relationship between stock prices while allowing for short-term
deviations
Integration index-asset
pricing
Convergence of rates of return of stocks with similar risk across markets
Table I.
Measures of equity
market integration
Dynamic
interdependence
225
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
difference between the US and Asian markets, a shock in the US market during a day t
will not be re?ected in the Asian stock market until day t þ 1. However, a change in
the Asian markets during day t will be re?ected in the US market the same day. Thus,
the appropriate pairing of time t 2 1 for the USA and time t for the Asian markets was
adopted as suggested by Liu et al. (1998).
All daily indices were transformed into daily rates of return in the empirical
estimations, and were calculated as a difference in the natural logarithms as follows:
R
it
¼ ln P
it
2 ln P
it21
, where R
it
denotes the rate of returnof the ithmarket ondayt, and
P
it
(P
it21
) denotes the stock index on day t (t 2 1). The descriptive statistics presented in
Table II was calculated with the daily rates of return series.
V. Methodology and results
5.1 Unit root tests
VAR modelling cannot be applied if the variables used are non-stationary as it may
lead to spurious regression results. As a preliminary step, each series was checked
for a unit root using the ADF and PP tests with and without trend. The selection of
optimal lag length was determined by minimizing AIC and using the critical values
of MacKinnon (1996). The results of t-statistics for the unit root tests of log levels and
?rst differences of daily stock indices of the six Asian stock markets and the US
market are presented in Table III. The results indicated that for every stock price
index the unit root hypothesis was not rejected at 1, 5 and 10 per cent signi?cance
levels; whereas, tests performed on the ?rst differences of log stock prices strongly
indicated that each of the ?rst-differenced series was stationary. The evidence
supports that all stock price index series contain a single unit root, i.e. they are
integrated of order one.
5.2 Granger-causality tests
In an initial attempt to check for presence of short-term stock market comovements, the
relationships of the bivariate Granger-causality were analyzed. Suppose Y
t
and X
t
as
US and any Asian stock price index, respectively. Testing causal relation between the
two series can be based on the following bivariate autoregression:
Y
t
¼ a
0
þ
X
n
k¼1
a
k
Y
t2k
þ
X
n
k¼1
b
k
X
t2k
þ1
y;t
; ð5:1Þ
Stock market
index of DJI SSE BSE HSI STI KOSPI TSEC
Mean 4.9 £ 10
25
0.0003 0.0006 0.0002 0.0002 0.0004 8.5 £ 10
25
Median 0.0001 0.0000 0.0009 0.0000 0.0001 0.0006 0.0000
Maximum 0.1050 0.0940 0.1599 0.1340 0.0753 0.1128 0.0851
Minimum 20.082 20.0926 20.1181 20.136 20.092 20.128 20.0994
SD 0.0128 0.0167 0.0176 0.0168 0.0135 0.0190 0.0162
Skewness 20.104 20.0425 20.0975 20.020 20.334 20.401 20.1304
Kurtosis 11.191 7.3053 8.6784 10.242 8.0978 6.9581 5.5834
Jarque-Bera 7,866.5 2,207.4 3,782.4 6,169.6 3,117.3 1,928.8 796.73
Probability 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000
Table II.
Summary statistics of
daily rates of return
JFEP
5,2
226
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
X
t
¼ f
0
þ
X
n
k¼1
f
k
Y
t2k
þ
X
n
k¼1
u
k
X
t2k
þ1
x;t
; ð5:2Þ
where a
0
and f
0
are constants, a
k
, b
k
, f
k
, u
k
are estimated coef?cients, 1
y, t
and 1
x, t
are
uncorrelated disturbance terms with zero means and ?nite variances.
The null hypothesis that X
t
does not Granger-cause Y
t
is rejected if the b
k
coef?cients in equation (5.1) are jointly different from zero and this can be tested using
the standard F-test. Similarly, Y
t
Granger-causes X
t
, if the f
k
coef?cients are jointly
different from zero in equation (5.2).
Granger-causality test requires that the data series are stationary; otherwise
in?uence from the F-statistics of causality test might be spurious because the test
statistics will have non-standard distributions. Since, both the ADF test and PP
test proved that the log levels of stock price indices were non-stationary and the ?rst
differences (log returns) were stationary, the latter was used in the causality tests.
The causality test requires that an error correction term be incorporated into the test
if the non-stationary log levels of indices are cointegrated. Granger (1988) indicated
that causality tests might reach incorrect conclusions if they failed to include an error
correction term of the cointegrated series. Thus, if the variables are not cointegrated,
the VAR model can be used because there is no need of an error-correction model.
The maximum likelihood method of Johansen (1991) and Johansen and Juselius
(1990) were used to examine whether the Dow Jones index and the stock price index
series of each Asian market are cointegrated. Table IV presents the results for
trace test and maximum eigen value test. While the trace test evaluates the null
hypothesis of at most r cointegrating vectors versus the general p cointegrating
vectors, maximum eigen value test evaluates the null hypothesis of r cointegrating
vectors versus the alternative hypothesis of r þ 1 cointegrating vectors. The results
indicate no evidence of cointegration on a bilateral basis between the US and Asian
stock markets.
Without trend With trend
ADF PP ADF PP
Stock
index Levels Returns Levels Returns Levels Returns Levels Returns
ln SSE 20.932 253.45
* * *
20.975 253.47
* * *
21.252 253.44
* * *
21.297 253.46
* * *
ln BSE 20.483 249.92
* * *
20.446 249.85
* * *
21.671 249.91
* * *
21.599 249.85
* * *
ln HSI 21.528 253.95
* * *
21.469 253.98
* * *
21.959 253.94
* * *
21.895 253.97
* * *
ln STI 21.487 251.40
* * *
21.556 251.46
* * *
21.487 251.39
* * *
21.786 251.45
* * *
ln
KOSPI
21.325 252.21
* * *
21.298 252.21
* * *
22.255 252.20
* * *
22.230 252.20
* * *
ln TSEC 21.776 251.97
* * *
21.874 252.03
* * *
21.863 251.97
* * *
21.958 252.02
* * *
ln DJIA 22.246 232.69
* * *
22.377 2133.4
* * *
22.234 232.69
* * *
22.352 2133.4
* * *
Notes: Rejection of null hypothesis at:
*
10,
* *
5 and
* * *
1 per cent levels; the critical values of the
ADF test without trend and with trend were 23.43 and 23.96, respectively, at 1 per cent signi?cance
level; the critical values of the PP test without trend and with trend were 23.43 and 23.96,
respectively, at 1 per cent signi?cance level; MacKinnon (1996) critical values were used for the
rejection of unit root hypothesis
Table III.
Unit root tests
Dynamic
interdependence
227
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Since, the variables are not cointegrated; a VAR model was applied to test for
Granger-causality. The number of lags used in the VAR model is important because the
number of lags chosenis expectedtoaffect the empirical results. AICwas usedto determine
the optimal laglength. The selectedlaglengthwas less thanthree for all markets. However,
a lag of three was used to effectively reduce the serial correlation in the residuals. The
results are presented in Table V. Interpretation of the results revealed that Dow Jones
index signi?cantly Granger-causes all the Asian stock market indices and also the stock
prices of Asian markets affect the US market. Thus, the Granger-causality tests proved
the existence of bi-directional short-term interactions between the US and the Asian
stock markets.
5.3 FEVD analysis
Granger-causality tests cannot be used to gauge the relative strength of causality
among the variables. In this study, generalized FEVD analysis was used to measure
the extent to which shocks in one market are explained by the other market in the
Stock market
index
Null
hypothesis
Trace
statistic Probability
a
Null
hypothesis
Max eigen
value Probability
a
SSEC r ¼ 0 6.9691 0.5812 r ¼ 0 5.8785 0.6289
r # 1 1.0906 0.2963 r ¼ 1 1.0906 0.2963
BSE r ¼ 0 6.2997 0.6601 r ¼ 0 6.2749 0.5782
r # 1 0.0248 0.8747 r ¼ 1 0.0248 0.8747
HSI r ¼ 0 8.1179 0.4529 r ¼ 0 6.4733 0.5532
r # 1 1.6446 0.1997 r ¼ 1 1.6446 0.1997
STI r ¼ 0 9.3106 0.3374 r ¼ 0 7.8835 0.3906
r # 1 1.4271 0.2322 r ¼ 1 1.4271 0.2322
KOSPI r ¼ 0 6.6228 0.6219 r ¼ 0 5.6463 0.6589
r # 1 0.9764 0.3231 r ¼ 1 0.9764 0.3231
TSEC r ¼ 0 14.469 0.0709 r ¼ 0 10.313 0.1922
r # 1 4.1563 0.0415 r ¼ 1 4.1563 0.0415
Note:
a
MacKinnon-Haug-Michelis (1999) p-values
Table IV.
Bivariate cointegration
tests between DJIA and
Asian stock indices
Stock market index Null hypothesis F-statistic Probability
SSEC DJIA KSSEC 2.3457 0.021
*
SSEC KDJIA 2.7566 0.007
* *
BSE DJIA KBSE 8.0632 2 £ 10
205
* *
BSE KDJIA 41.213 5 £ 10
226
* *
HSI DJIA KHSI 7.6139 0.000
* *
HSI KDJIA 83.472 6 £ 10
236
* *
STI DJIA KSTI 27.765 4 £ 10
237
* *
STI KDJIA 3.0365 0.003
* *
KOSPI DJIA KKOSPI 6.7843 0.000
* *
KOSPI KDJIA 33.303 4 £ 10
221
* *
TSEC DJIA KTSEC 13.813 4 £ 10
211
* *
TSEC KDJIA 4.7001 0.000
* *
Note: Rejection of null hypothesis at:
*
1 and
* *
5 per cent signi?cance levels
Table V.
Granger-causality tests
JFEP
5,2
228
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
bivariate VAR system. This provides a quantitative measure of short-run dynamic
interdependencies between US and Asian markets.
For each Asian market, the following bivariate VAR model was constructed:
R
t
¼ C þ
X
L
s¼1
b
s
R
t2s
þ e
t
ð5:3Þ
where R
t
is a m £ 1 column vector of daily stock index returns, C and b
s
are,
respectively, m £ 1 and m £ m matrices of coef?cients, L is the lag length, and e
t
is a
m £ 1 column vector of forecast errors of the linear predictor R
t
using past values R
t2s
.
Here, m ¼ 2 (stock indices of US and an Asian market). By construction, e
t
is a serially
uncorrelated error term, however the components of e
t
may be contemporaneously
correlated. That is, E(e
t
) ¼ 0, E(e
t
e
0
t2s
) ¼ 0, for all s – 0 and E(e
t
e
0
t
) ¼ V for all t,
where V ¼ {s
ij
; i; j; ¼ 1; 2; . . . ; m} is m £ m covariance matrix and is positive
de?nite.
By successive substitutions of the right hand side in equation (5.3), the VAR
system can be expressed as the moving average model of innovations as indicated
below:
R
t
¼ m þ
X
1
s¼0
A
s
e
t2s
ð5:4Þ
where m is a m £ 1 vector of constants. This represents R
t
as a linear combination
of current and past one-step ahead forecast errors. The i, jth component of A
s
,
measures the responses of the ith market after a unit random shock in the jth market
in s periods.
To use “isolated” shocks, innovations are orthogonalized using the Cholesky
decomposition in the traditional approach. The results of variance decomposition
obtained by the traditional method are sensitive to the choice of ordering of variables in
the VAR model as orthogonalization of innovations imposes a recursive structure in the
model. Hence, the generalized approach to FEVD which is invariant to the ordering of
the variables was drawn from equation (5.4). The generalized FEVD was calculated as
follows:
u
g
ij
ðkÞ ¼
s
k21
ij
P
k21
s¼0
ðe
0
i
A
s
Ve
j
Þ
2
P
k21
s¼0
e
0
i
A
s
VA
0
s
e
i
ð5:5Þ
The percentage of FEVD of Asian markets explained by the US market and vice versa
was estimated for every quarter of the period 1999-2009. This captures the ?uctuations
in interdependence between these markets. The results of ten-day ahead FEVD analysis
for the US stock markets and the Asian economies are plotted in Figures 1 and 2,
respectively.
The percentage of unexpected variance (innovations) in each Asian market explained
by the US market for every quarter over the period 1999-2009 is plotted in Figure 1.
Several major ?ndings emerged from the analysis. First, a substantial number of
interactions were found to exist among the US and Asian markets. A single market’s
own innovations did not fully account for its own variance, i.e. no market was absolutely
exogenous. Among the Asian markets considered for the study, Hong Kong and
Dynamic
interdependence
229
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 1.
Variance accounted for
by US stock market to
innovations in Asian
stock markets
Hong Kong Singapore
South Korea Taiwan
China India
Notes: The X-axis represents the year; the Y-axis represents the percentage of variance
explained by the US stock market to innovations in Asian markets obtained by FEVD analysis
in a bivariate VAR framework
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
5
10
15
20
25
30
35
40
45
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
JFEP
5,2
230
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Figure 2.
Variance accounted for by
Asian stock markets to
innovations in the US
stock market
Hong Kong Singapore
South Korea Taiwan
China
India
Notes: The X-axis represents the year; the Y-axis represents the percentage variance
explained by the Asian stock market to innovations in those of US market obtained by
FEVD analysis in a bivariate VAR framework
99 00 01 02 03 04 05 06 07 08 09 99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
0
10
20
30
40
50
0
10
20
30
40
50
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09 99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
0
10
20
30
40
50
99 00 01 02 03 04 05 06 07 08 09
Dynamic
interdependence
231
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Singapore markets had signi?cant interactions with the US market followed by
South Korea, Taiwan and India. In contrast, although its markets have been open to
foreign investors since the early 1990s, China was relatively more immune to shocks
from the US market. On an average, the US market accounted for around 20 per cent of
the variance in Hong Kong and Singapore markets; more than 15 per cent in the case of
Korean and Taiwanese markets and 10 and 5 per cent in the case of Indian and Chinese
markets, respectively.
Prior studies indicated signi?cant increase in stock market interdependence during
the crisis period. Similarly, the percentage of variance of Asian markets explained by
the US market indicated a marked increase during the sub-prime crisis period. During
2007-2009, the average variance explained by US market jumped to around 25 per cent
for the Hong Kong and Singapore markets. This was also true for other Asian markets
as the US market accounted for more than 20 per cent of variance in Korean and
Taiwanese markets during the same period. However, the Chinese market by and large
remained unaffected by the sub-prime crisis, since the variance explained by the US
market increased only by 1 per cent during this period. The phenomenon of greater
interdependence between the markets con?rmed the inference of prior research that
equity market integration has progressed over time.
The innovations in US markets accounted by Asian markets are shown in Figure 2.
The contribution of Asian markets to US markets’ innovations remained lower than the
US market’s contribution to Asian markets. On an average, among the Asian markets
considered, Hong Kong, Singapore and Indian markets explained around 15 per cent of
the variance in the US market while other markets explained only around 5 per cent.
Thus, it can be concluded that the USA is the global leader and strongly in?uences the
movements in stock prices of Asia’s emerging and NIEs.
VI. Summary and conclusion
Empirical results fromthe FEVDanalysis revealed that the US stock market dominates
the Asian markets; the reasons for this being that the USAis one of the largest economies
of the world, and is not only an important trading partner but also a major supplier of
capital to the Asian region. Thus, stock markets of Asia are not immune to the shocks
originating in the USA, although the effects of shocks vary considerably across markets.
In addition, as indicated in the earlier studies, an important implication is that major
crises can in?uence the relationship among stock markets. Arshanapalli and Doukas
(1993), and Meric and Meric (1997) comparing the pre- and post-October 1987 periods
demonstrated that the correlation between national stock markets increased and
portfolio diversi?cation bene?ts to investors decreased signi?cantly after the 1987
crash. The 1997-1998 ?nancial crisis is another global event that had a strong effect on
the world’s stock markets. Meric et al. (2001) and Yang et al. (2003) proved that the
crisis affected the comovement patterns of the world’s stock markets signi?cantly
and that the bene?ts of portfolio diversi?cation decreased considerably after the
1997-1998 crisis. Similarly, empirical results proved that US market’s in?uence on
Asian markets increased signi?cantly during the sub-prime crisis period of 2007-2008.
Interestingly, though, US’ in?uence on China remained unaffected even during the
crisis period.
The Hong Kong market, followed by the Singapore market were affected most by the
US shocks, primarily because neither of these countries impose any restrictions on
JFEP
5,2
232
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
equity investment for either foreigners or domestic residents and they also permit
foreign currency to be either imported or exported. Thus, shocks fromthe USAcould be
more directly transmitted to these two ?nancial centers (Cha and Oh, 2000). Though the
Korean, Taiwanese and Indian markets opened in the early 1990s, foreign investors
faced huge restrictions on shares open to investment. There were also limits on
shareholding by foreigners which were gradually relaxed after the Asian crisis and
hence the impact of the US market has increase since then.
The reasons for why the Chinese stock markets are not integrated with the USA and
other countries despite the country’s increased importance in the region and in the world
economy are many. One key factor is that there are two groups of stock transactions
in China; one which is open to foreigners, and the other which is open only for the local
residents; also the Chinese stock markets are not as free as those of other countries.
This may lead to distortion in causality test of the Chinese stock markets with other
markets. Another key factor is the restrictions on international capital ?ows, impeding
foreigners from investing in Chinese domestic equity markets. Although from 2002
onwards, quali?ed foreign institutional investors (QFIIs) are allowed to invest in the
Chinese domestic stock markets, their stakes are subject to severe constraints. At the
same time, domestic investors are also facing huge restrictions on international portfolio
investments (Huyghebaert and Wang, 2009). Thus, the Chinese stock markets have
remained isolated from US in?uence when compared to the other markets in the
Asian-Paci?c region.
The ambiguous economic outcomes of equity market integration are highlighted by
Segot and Lucey (2008). Increased stock market interdependence implies progressing
integration of stock markets. This leads to reduction in cost of capital and increase in the
average price of ?nancial assets but also weakens the attractiveness of international
portfolio diversi?cation (Kearney and Lucey, 2004). Also, stock market integration
accentuates the risk of contagion as problems are likely to be transmitted from
one market segment to another segment. Hsin (2004) examined the mechanism of
transmission of shocks and ?nancial contagion across markets and revealed that
Asia-Paci?c NIEs and EMEs are more susceptible to contagious effects from developed
markets like the USA.
The opening of stock markets to foreign investors has also witnessed a high degree
of concentration, volatility of capital ?ows and the risks of abrupt reversals. The
rami?cations of such volatile and disruptive conditions became evident during the 1997
Asian ?nancial crisis and the recent sub-prime crisis of 2008. Segot and Lucey (2008)
argued that optimal stock market integration depends on a trade-off between cheaper
capital and ?nancial stability. Thus, the process of equity market integration has to be
monitored carefully and policymakers should consider institutional development and
corporate governance reforms before further liberalizing their ?nancial system.
Notes
1. Source: World Federation of Exchanges.
2. China and India are categorized as emerging market economies (EME) while Hong Kong,
Singapore, South Korea and Taiwan are categorized as newly industrialized economies
(NIE). Source: World Economic Outlook Database, 2010 of IMF.
3. The of?cial liberalization dates of Hong Kong, Singapore, South Korea and Taiwan are
January 1973, June 1978, January 1992 and January 1991 (Phylaktis and Ravazzolo, 2002).
Dynamic
interdependence
233
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
References
Arshanapalli, B. and Doukas, J. (1993), “International stock market linkages: evidence from
the pre- and post-October 1987 period”, Journal of Banking & Finance, Vol. 17,
pp. 193-208.
Arshanapalli, B., Doukas, J. and Lang, L. (1995), “Pre and post October 1987 stock market
linkages between US and Asian markets”, Paci?c-Basin Finance Journal, Vol. 3,
pp. 57-73.
Awokuse, T.O., Chopra, A. and Bessler, D.A. (2009), “Structural change and international stock
market interdependence: evidence from Asian emerging markets”, Economic Modelling,
Vol. 26, pp. 549-559.
Baharumshah, A.Z., Sarmidi, T. and Tan, H.B. (2003), “Dynamic linkages of Asian stock
markets”, Journal of Asia Paci?c Economy, Vol. 8, pp. 180-209.
Bahng, J.S. and Shin, S. (2003), “Do stock price indices respond asymmetrically? Evidence from
China, Japan, and South Korea”, Journal of Asian economics, Vol. 14, pp. 541-563.
Bailey, W. and Choi, J. (2003), “International market linkages”, Journal of Economics and
Business, Vol. 55, pp. 399-404.
Batareddy, M., Gopalaswamy, A.K. and Huang, C.H. (2012), “The stability of long-run
relationships: a study on Asian emerging and developed stock markets ( Japan and US)”,
International Journal of Emerging Markets, Vol. 7, pp. 31-48.
Bracker, K. and Koch, P. (1999), “Economic determinants of correlation structure across
international equity markets”, Journal of Economics and Business, Vol. 51, pp. 443-471.
Bracker, K., Docking, D. and Koch, P. (1999), “Economic determinants of evolution in
international stock market integration”, Journal of Empirical Finance, Vol. 6, pp. 1-27.
Cha, B. and Cheung, Y. (1998), “The impact of the US and the Japanese equity markets on the
emerging Asia-Paci?c equity markets”, Asia-Paci?c Financial Markets, Vol. 5,
pp. 191-209.
Cha, B. and Oh, S. (2000), “The relationship between developed equity markets and the paci?c
basin’s emerging equity markets”, International Review of Economics Finance, Vol. 9,
pp. 299-322.
Chan, K.C., Gup, B.E. and Pan, M. (1992), “An empirical analysis of stock prices in major Asian
markets and the United States”, Financial Review, Vol. 27, pp. 289-307.
Choudry, T., Lu, L. and Peng, K. (2007), “Common stochastic trends among Far East stock prices:
effects of the Asian ?nancial crises”, International Review of Financial Analysis, Vol. 16,
pp. 242-261.
Colthup, G. and Zhong, M. (2005), “Analyses of equity market linkages in the paci?c basin”,
paper presented at Afaanz 2005 Conference, Melbourne, July 3-5.
Darrat, A.F. and Zhong, M. (2002), “Permanent and transitory driving forces in the Asian-Paci?c
stock markets”, Financial Review, Vol. 37, pp. 35-51.
Dekker, A., Sen, K. and Young, M.R. (2001), “Equity market linkages in the Asia Paci?c region:
a comparison of the orthogonalised and generalised VAR approaches”, Global Finance
Journal, Vol. 12, pp. 1-33.
Forbes, K. and Rigobon, R. (2002), “No contagion, only interdependence: measuring stock market
co-movements”, Journal of Finance, Vol. 57, pp. 2223-2261.
Ghosh, A., Saidi, R. and Johnson, K. (1999), “Who moves the Asia-Paci?c stock markets – US or
Japan? Empirical evidence based on the theory of cointegration”, Financial Review, Vol. 34,
pp. 159-170.
JFEP
5,2
234
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Gopalaswamy, A.K., Huang, C.H. and Batareddy, M. (2010a), “Price spillover effects from
developed to Asian emerging stock markets: wavelet analysis”, Working Paper Series,
Asian Institute of Technology, Thailand.
Gopalaswamy, A.K., Kumar, S.S.S. and Huang, C.H. (2010b), “A study on the changing role of
Chinese stock market among the East Asian stock market group”, Working Paper Series,
Asian Institute of Technology, Thailand.
Granger, C.W.J. (1988), “Some recent development in a concept of causality”, Journal of
Econometrics, Vol. 39, pp. 199-211.
Hsiao, F.S.T., Hsiao, M.W. and Yamashita, A. (2003), “The impact of the US economy on
the Asia-Paci?c region: does it matter?”, Journal of Asian Economics, Vol. 14,
pp. 219-241.
Hsin, C.W. (2004), “A multilateral approach to examining the comovements among major world
equity markets”, International Review of Financial Analysis, Vol. 13, pp. 433-462.
Huang, B.N., Yang, C.W. and Hu, J.W.S. (2000), “Causality and cointegration of stock markets
among the United States, Japan and the South China growth triangle”, International
Review of Financial Analysis, Vol. 9, pp. 281-297.
Huyghebaert, N. and Wang, L. (2009), “The co-movement of stock markets in East Asia: did the
1997-1998 Asian ?nancial crisis really strengthen stock market integration?”, China
Economic Review, Vol. 21, pp. 98-112.
Janakiramanan, S. and Lamba, A.S. (1998), “An empirical examination of linkages between
Paci?c-Basin stock markets”, Journal of International Financial Markets, Institutions and
Money, Vol. 8, pp. 155-173.
Jeon, B.N. and Chiang, T.C. (1991), “A system of stock prices in world stock exchanges:
common stochastic trends for 1975-1990”, Journal of Economics and Business, Vol. 43,
pp. 329-338.
Johansen, S. (1991), “Estimation and hypothesis testing of cointegration vectors in Gaussian
vector autoregressive models”, Econometrica: Journal of the Econometric Society, Vol. 59,
pp. 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, Vol. 52, pp. 169-210.
Johnson, R. and Soenen, L. (2003), “Economic integration and stock market comovement in
Americas”, Journal of Multinational Financial Management, Vol. 13, pp. 85-100.
Kearney, C. and Lucey, B.M. (2004), “International equity market integration: theory,
evidence and implications”, International Review of Financial Analysis, Vol. 13,
pp. 571-583.
Kim, S.J., Moshirian, F. and Wu, E. (2005), “Dynamic stock market integration driven by
European monetary integration – an empirical analysis”, Journal of Banking & Finance,
Vol. 29, pp. 2475-2502.
Kizys, R. and Pierdzioch, C. (2006), “Business-cycle ?uctuations and international equity
correlations”, Global Finance Journal, Vol. 17, pp. 252-270.
Lamba, A.S. (2005), “An analysis of the short- and long-run relationships between
South Asian and developed equity markets”, International Journal of Business, Vol. 10,
pp. 383-402.
Lim, L.K. (2009), “Convergence and interdependence between ASEAN-5 stock markets”,
Mathematics and Computers in Simulation, Vol. 79, pp. 2957-2966.
Dynamic
interdependence
235
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Lin, C.H. and Cheng, W.H. (2008), “Economic determinants of comovements across international
stock markets: the example of Taiwan and its key trading partners”, Applied Economics,
Vol. 40, pp. 1187-1205.
Liu, S.Z., Lin, K.C. and Lai, S.M. (2006), “Stock market interdependence and trade relations:
a correlation test for the US and its trading partners”, Economics Bulletin, Vol. 7,
pp. 1-15.
Lucey, B.M. and Zhang, Q.Y. (2010), “Does cultural distance matter in international stock market
comovement? Evidence from emerging economies around the world”, Emerging Markets
Review, Vol. 11, pp. 62-78.
MacKinnon, J.G. (1996), “Numerical distribution functions for unit root and cointegration tests”,
Journal of Applied Econometrics, Vol. 11, pp. 601-618.
Masih, R. and Masih, A.M.M. (1999), “Are Asian stock market ?uctuations due mainly to
intra-regional contagion effects? Evidence based on Asian emerging stock markets”,
Paci?c-Basin Finance Journal, Vol. 7, pp. 251-282.
Meric, I. and Meric, G. (1997), “Co-movements of European equity markets before and after the
1987 crash”, Multinational Finance Journal, Vol. 1, pp. 137-152.
Meric, G., Leal, R.P.C., Ratner, M. and Meric, I. (2001), “Co-movements of US and Latin American
equity markets before and after the 1987 crash”, International Review of Financial
Analysis, Vol. 10, pp. 219-235.
Ozdemir, Z.A., Olgun, H. and Saracoglu, B. (2009), “Dynamic linkages between the center and
periphery in international stock markets”, Research in International Business and Finance,
Vol. 23, pp. 46-53.
Pesaran, H.H. and Shin, Y. (1998), “Generalized impulse response analysis in linear multivariate
models”, Economics Letters, Vol. 58, pp. 17-29.
Phylaktis, K. and Ravazzolo, F. (2002), “Measuring ?nancial and economic integration with
equity prices in emerging markets”, Journal of International Money and Finance, Vol. 21,
pp. 879-904.
Pretorius, E. (2002), “Economic determinants of emerging stock market interdependence”,
Emerging Markets Review, Vol. 3, pp. 84-105.
Segot, T.L. and Lucey, B.M. (2008), “The capital markets of the Middle East and North African
region: situation and characteristics”, Emerging Markets Finance and Trade, Vol. 44,
pp. 68-81.
Sheng, H.C. and Tu, A.H. (2000), “A study of cointegration and variance decomposition among
national equity indices before and during the period of the Asian ?nancial crisis”, Journal
of Multinational Financial Management, Vol. 10, pp. 345-365.
Siklos, P.L. and Ng, P. (2001), “Integration among Asia-Paci?c and international stock
markets: common stochastic trends and regime shifts”, Paci?c Economic Review, Vol. 6,
pp. 89-110.
Tan, K.B. and Tse, Y.K. (2002), “The integration of the East and South-East Asian equity
markets”, Working Paper Series, International Center for the Study of East Asian
Development, Kitakyushu.
Tavares, J. (2009), “Economic integration and the comovement of stock returns”, Economics
Letters, Vol. 103, pp. 65-67.
Voronkova, S. (2004), “Equity market integration in Central European emerging markets:
a cointegration analysis with shifting regimes”, International Review of Financial Analysis,
Vol. 13, pp. 633-647.
JFEP
5,2
236
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
Wong, W.K., Agarwal, A. and Du, J. (2005), “Financial integration for Indian stock market,
a fractional cointegration approach”, NUS Working Paper No. 0501.
Yang, J., Kolari, J.W. and Min, I. (2003), “Stock market integration and ?nancial crises: the case of
Asia”, Applied Financial Economics, Vol. 13, pp. 477-486.
Corresponding author
Arun Kumar Gopalaswamy can be contacted at: [email protected]
To purchase reprints of this article please e-mail: [email protected]
Or visit our web site for further details: www.emeraldinsight.com/reprints
Dynamic
interdependence
237
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
This article has been cited by:
1. Nuruzzaman Arsyad. 2015. Integration between East and Southeast Asian equity markets. Journal of
Financial Economic Policy 7:2, 104-121. [Abstract] [Full Text] [PDF]
D
o
w
n
l
o
a
d
e
d
b
y
P
O
N
D
I
C
H
E
R
R
Y
U
N
I
V
E
R
S
I
T
Y
A
t
2
1
:
4
6
2
4
J
a
n
u
a
r
y
2
0
1
6
(
P
T
)
doc_654526159.pdf