Dynamic Stock Market Integration and Financial Crisis: the Case of China, Japan, and Korea

Jinho Jeong Professor, School of Business Administration, Korea University [email protected] Abstract This study examines the relationships between three Northeast Asian stock markets of China, Japan, and Korea during the period between January 1, 2000 and September 30, 2010, with particular attention placed on the global financial crisis period. The findings of this study are as follows. Firstly, China is influenced more by regional markets rather than the global market. On the other hand, Japan is influenced more by the global market rather than regional markets. Korea has the most balanced level of integration between the regional and global markets. Secondly, a portfolio created through an integrated market in the region would result in a significant decline in the unsystematic risk of each country, benefiting both the investor and local economies. Thirdly, the recent global financial crisis has caused a shift in the pattern of integration in the region. All three countries show a higher level of integration with the global market after the financial crisis . Finally, for China, the global market risk has become even greater than the domestic unsystematic risk since 2010. Overall result suggests that the degree of integration among countries tends to change over time, especially around periods marked by financial crisis and there is a diversification benefit of integrated regional market. Keywords: Market Integration, VAR, Risk Decomposition Model, Cointegration, Dynamic Conditional Correlation JEL Codes: F36, G15

1

I. Introduction

Since the formation of the WTO, traditional trade barriers have started to dissipate and the free flow of capital and resources has resulted in growing stock market integration between nations.

Furthermore, advancements in information technology

as well as the rise of multi-national corporations have escalated this trend in global stock markets. Regional economic cooperation’s such as the European Union (EU), North American Free Trade Agreement (NAFTA) and Asia Pacific Economic Council (APEC) etc. are all indicators that the trend towards integrated capital markets is rapidly escalating. Recently, the three main countries in the Northeast Asia—China, Japan, and Korea have recognized the importance of this integrated market and, in 2011, established a Three Nations Economic Cooperation Secretariat in Korea and reached an agreement to start a joint research into the possibility of a Three Nations FTA.

If this

agreement in regards to an integrated market is to succeed, it will become one of the biggest market forces leading the global economy with the combined Gross Domestic Product (GDP) as of 2009 being $10.8 trillion USD which will be the third biggest market following the EU ($16.4 trillion USD) and NAFTA ($14.2 trillion USD). As of 2009, the 3 economies of China, Korea and Japan contribute to one fifth of the world’s GDP but only 20% of this is generated from economic activity in the region (compared to the EU & NAFTA which contribute over 50% of its GDP from regional trading) meaning that there is huge potential for growth in this area. Lee (2010) finds that if an FTA was to be signed between Korea, China and Japan, this will lead to an increase of the GDP for each of the countries for 5.14%, 1.54%, and 1.21% respectively.

2

It is well known that the integration of stock markets increases liquidity and thus reduces transaction costs and negates certain risks associated with macroeconomic shocks. The size of the GDP and regional trade volume for the three countries satisfy and meets the conditions in regards to an integrated economy but from a financial perspective, none of the conditions for this integration has been scoped out properly. Therefore, financial market integration of the three countries will mean that surplus funds in the region can be invested into this joint market, resulting in these three countries securing a financial independence up to a certain extent from the western financial markets.

Furthermore, the integration of

the stock markets increases investment opportunities leading to higher efficiency of resource allocation and also further stimulates economic growth in and around the region. New investment is critical for the three economies of Korea, China and Japan to sustain its growth rates in the future, and up until now this investment has come from global financial markets rather than regional and domestic markets.

Because of this, a gap between the

real sector and the financial sector has occurred which has led to financial markets operating inefficiently thus leading to a limitation of growth in real markets. These problems can be minimized through strengthening the economic ties between countries in the region leading to a more robust financial market and the growth of the region’s economy.

However, studies regarding an integrated market in this region are still in its preliminary stages and lack any detailed research or empirical analysis at this point of time.

In this

paper, we focus on financial sector integration in this region. In particular, this study investigates the dynamic pattern of integration amongst the three countries’ stock markets to find whether we can benefit from the integrated stock market. The findings in this paper

3

can then be used as a basis of an action plan for an integrated market and stimulate a motivation for more future academic research in this field.

The sequence of this paper is as follows. The next section briefly reviews the literature. In Section III, the empirical framework is discussed. Section IV explains the data and sample statistics. Section V presents the empirical results. The last section gives the summary and conclusions.

4

II. Literature Review

There have been numerous studies on market integration and interdependence. Early works in this area analyzes the correlation coefficients across markets over certain time period. If the correlation is high, it is regarded that integration exists between the two markets. Using data from seven major European countries from 1970 to 1990, Longin and Solnik (1995) find that cross-country stock market correlations increase over time.

Karolyi

and Stulz (1996) study the daily return co-movements between the Japanese and U.S. stocks from 1988 to 1992 and find evidence to support that correlations are high when there are significant markets movements.

Palac-McMiken (1997) uses the monthly

ASEAN market indices (Indonesia, Malaysia, the Philippines, Singapore, and Thailand) between 1987 and 1995 and finds that with the exception of Indonesia, all the markets are linked with each other. He argues that there is still room for diversification across these markets despite evidence of interdependence among ASEAN stock markets. Masih and Masih (1999) find high levels of interdependence amongst markets in Thailand, Malaysia, the U.S., Japan, Hong Kong, and Singapore from 1992 to 1997.

Johnson and Soenen

(2002) study the equity market integration between the Japanese stock market and the other twelve equity markets in Asia. They find that the equity markets of Australia, China, Hong Kong, Malaysia, New Zealand, and Singapore are highly integrated with the stock market in Japan. They also find evidence to suggest that a higher import share as well as a greater differential in inflation rates, real interest rates, and GDP growth rates all have negative effects on stock market comovements between country pairs. Regarding the problem of using correlation analysis, Forbes and Rigobon (2002) show that unadjusted cross-market correlation coefficients are conditional on market volatility and therefore not

5

appropriate to gauge either the degree of integration or to distinguish it from contagion. However, Corsetti et al. (2002) raise a contrary argument, suggesting that the results of Forbes and Rigobon (2002) are dependent on the specification of idiosyncratic shocks and contagion was present during the Asian crisis if these shocks included, even if correlation measures are flawed. A study done by Chelley-Steeley (2005) picks up correlation analysis again to address integration, where the study models the movement of bivariate equity market correlations as a smooth transition trend to check how rapidly several equity markets of Eastern Europe are moving away from market segmentation. More recent papers have tried to capture the benefits of correlation coefficients within a GARCH framework which explicitly deals with volatility issues. Lucey and Voronkova (2007) use dynamic conditional correlation (DCC) derived from multivariate GARCH framework to make inferences about short-term interdependence between Russian equity market and developed markets.

Another group of papers makes use of asset pricing models. Bekaert and Harvey (1995) apply an asset pricing technique to study time-varying integration, with a conditional regime-switching two-factor model.

Barari (2004) uses a risk decomposition model to

investigate the degree of integration for the Latin American countries.

He finds a trend

towards increased regional integration relative to global integration until the mid-1990s and faster global integration versus regional integration during the second half of the 1990s in the region. Bekaert, Harvey and Ng (2005) propose a two-factor (global and regional) model to examine the equity market contagion during both the Mexican and Asian crisis of 1990s. De Jong and De Rong (2005) develop a factor asset pricing model and find that emerging stock markets have become less segmented from world stock markets and that

6

integration with the world significantly reduces the cost of capital. Hunter (2006) uses a multivariate GARCH-in-Mean asset-pricing model on three Latin American markets: Argentina, Chile and Mexico. He finds that these markets have not become integrated into the world equity market in the decade after liberalization. Tai (2007) estimates a dynamic international CAPM using a parsimonious multivariate GARCH-in-Mean (MGARCH-M) approach and shows that emerging Asian stock markets become integrated after they liberalize their equity markets.

Another line of studies have applied cointegration methods to investigate the financial market integration. These studies focus on the long-run equilibrium relations among a group of national equity markets. If these markets are cointegrated, they will not deviate very far from each other over a relatively long period. Chowdhury (1994) studies this relationship among 4 Newly Industrialized Economies (NIEs), Japan and the U.S., using daily data from 1986 to 1990. He finds that the U.S. market leads the four markets (Hong Kong, South Korea, Singapore, and Taiwan) and that there is significant link between the stock markets of Hong Kong and Singapore and those of Japan and the United States. He also finds that the U.S. market is not influenced by the four Asian markets. Naughton (1996) investigates whether the returns in selected Asian and developed equity markets are related and finds generally low correlation between Asian emerging markets and these markets and the developed market group. Ng (2002) examines the linkage among the ASEAN five countries in the 1990s. The results of his study indicate that there is no evidence of co-integrating relationship across the ASEAN stock markets, although individual countries do show a trend toward stronger linkage with each other. Yang et al. (2003) carry out a recursive cointegration analysis on major international stock markets and find some

7

diversification benefits to U.S. investors.

Johnson and Soenen (2003) show evidence of

association between eight equity markets in the Americas paired with the United States. Aggarwal et al. (2003) use cointegration techniques to check time-varying integration between the European markets and the US equity market, indicating the emergence of long-run equilibrium relations only since 1997. Controlling for three kinds of single structural change, Voronkova (2004) detects several cointegration relations between emerging Central European stock markets and the developed markets. Lagoarde-Segot and Lucey (2006) investigate capital markets in the Middle East and North Africa and finds a lack of a stable, long-run bivariate relationship between each of these markets and the benchmark markets from 1998 to 2004. Glezakos et al. (2007) investigate the short- and long-term relationships between major world financial markets with particular attention to the Greek stock exchange. The empirical results indicate that both long-term cointegrating relationships and short-term dynamic causal linkages strengthen though time. An and Brown (2010) examines the long-run relationships of the weekly and monthly index returns of the US, Brazil, Russia, India, and China stock markets during the period between October 13, 1995 and October 13, 2009. Their findings show that there is some cointegration between the US and China while there is no cointegration between the US and the other emerging markets. Based on these results they argue that investors would have better diversification investing in Brazil, Russia, or India rather than in China.

Several studies have investigated the effect of structural changes in the economy on the dynamic linkage of stock returns.

Shamsuddin and Kim (2003) found that the presence of

a stable long run relationship amongst the Australian, US and Japanese markets existed prior to the Asian crisis and disappeared in the post-Asian crisis period. Fujii (2005) reported

8

that the causal linkages among several emerging stock markets varied considerably during the time of rapid growth and major upheaval from 1990 in Asia and Latin America. Westermann (2004) empirically showed that the introduction of the Euro shifted the linkage across the Euro zone stock markets, and Kim et al. (2005) find that increased stability and higher levels of integration have emerged in the post-euro era. The observed shifts in the post-euro period may have reflected the fact that an overall macroeconomic convergence process associated with the single currency has emerged. For the transition economies, Chelley-Steeley (2005) finds a movement towards increased equity market integration by analyzing a smooth transition. Lucey and Voronkova (2007) also apply a series of cointegration testing methods on the relationship between Russian and other equity markets over the period of 1995-2004. They obtain mixed results about the number of cointegration relationships after the 1998 Russian equity market crisis.

Abdul Karim et al.

(2010) examine the effects of the global financial crisis on the integration and comovements of selected Islamic stock markets over the period spanning from February 15, 2006 to December 31, 2008. They divide the period of analysis into two periods, namely the pre-crisis period and during crisis period. The results of cointegration analysis provide no evidence in favor of the cointegration among the Islamic stock markets under study in both periods. They conclude that the global financial crisis does not seem to affect the long-run co-movements among the Islamic stock markets.

9

III. Methodologies

In this study, we employ various approaches to analyze stock market integration. Each approach is discussed in the following section.

1. Dynamic Conditional Correlation This study uses Unconditional Correlation (UC) and Dynamic Conditional Correlation (DCC) to investigate market interdependence. DCC allows stock market correlations to be a time-varying.1 We use a multivariate VAR-EGARCH model to capture a dynamic correlation pattern. 2 This approach demonstrates a more direct indication of interdependence between stock markets, where the dynamics of correlation are modeled together with those of the volatility of the series. By accounting for the time-varying volatility behavior of data series, a major advantage of using this is the detection of possible changes in conditional correlations over time when the state of the economy changes. The models are specified as follows:

4

Ri ,t   i ,0    i , j R j ,t 1   i ,t ,

i, j = 1, 2, 3, 4

(5)

j 1





4

 i2,t  exp  i ,0   i , j f j ( z j ,t 1 )   i ln( i2,t 1 )  j 1  f j ( z j ,t 1 ) | z j ,t 1 |  E (| z j ,t 1 |)   j z j ,t 1



(6) (7)

n

 ij (UC)= ij =  i j

R

R j ,t

n

n

t 1

i ,t

R R t 1

2 i ,t

t 1

(8) 2 j ,t

1

See Engle (2002) for a detailed discussion.

2

EGARCH model is used to consider the problem of asymmetric volatility in market return.

10

t 1

 z s

ij ,t (DCC) 

Et 1 ( zi ,t z j ,t ) Et 1 ( zi2,t ) Et 1 ( z 2j ,t )



s 1

i ,t  s

z j ,t  s

 t 1 s 2  t 1 s 2     zi ,t  s    z j ,t  s   s 1  s 1 

,

  i

(9)

Ri,t : ith return at time t σi,t2 : conditional variance εi,t : innovation zi,t : standardized innovation, (zi,t=εi,t/σi,t) Mean of Ri,t is 0 and estimated value of γi 0.94. 2. Vector Auto Regression (VAR)

This paper uses a vector auto regression (VAR) modeling framework to determine the transmission channels of market movements to different countries. Although most econometric models use economic theory as a basis for constructing the relationships among the variables, it has limitations in providing dynamic specifications that identifies all the relevant relationships. A non-structural approach such as the VAR provides an alternative system to traditional structural models in capturing the multidimensional relationship between countries’ stock markets more effectively. The VAR approach allows us to treat all endogenous variables in the system as a function of the lagged values of all the endogenous and exogenous variables in the system. The model is specified as follows:

Y t  A(L) Y t  i  B(L) X t  CU t

(1)

where Y is a vector of endogenous variables. X is a vector of exogenous variables. k A(L)  A1L    A k Lk and B(L)  B1L    Bk L are the lag polynomials, Ut is a vector of

11

innovations, and C is a contemporaneous matrix.

Although the impact of the global

market movements can be transmitted through other channels such as the exchange rates and could be addressed by adopting different policies such as adjustments to tax rates, we restrict our model only to the movements in the global and regional equity market. Standard lag length criteria are used to determine the number of optimal lags in the VAR. ' Consider orthogonalized structural shocks with unit variances Eu t u t  I and

E (Cu t u 't C' )  CC'   where

I

is the identity matrix and

Σ

is the variance-covariance

matrix from the least squares estimation of (1). For impulse response functions of the system, assume that C is a lower triangular matrix that just identifies the entire system. Such a lower triangular matrix can be obtained by the Choleski decomposition of

Σ

estimate as

detailed by Sims (1980 a,b).

3. Cointegration Test

Long-run comovements between stock markets have important regional and global implications, as a domestic economy cannot be insulated from external shocks and the scope for independent economic policy appears then limited. Cointegration has emerged as a powerful technique for investigating common trends, long-run relationships, and interdependencies among international stock markets, and provides a sound methodology for modeling both short- and long-run dynamics in a system of variables (e.g. Hamilton, 1994; Hendry, 1995; Enders, 1995; Campbell et al., 1997). If two or more variables are cointegrated, then stationary linear combinations of these variables may exist even though the variables themselves are individually nonstationary. As a general rule, nonstationary time series variables should not be used in regression models, in order to avoid the problem

12

of spurious regression. There is an exception to this rule. Suppose that two market returns can be expressed by the following regression:

Pit = a +bPjt + et

(2)

In the equations (2) above, Pi and Pj are stock indices on the ith and jth country, respectively. If Pit and Pjt are nonstationary I (1) variables, then we would expect that their difference, or any linear combination of them, such as et = Pit - a - bPjt, to be I (1) as well. However there are important cases when et = Pit - a - bPjt is a stationary I (0) process. In this case Pit and Pjt are said to be cointegrated. Cointegration implies that Pit and Pjt share similar stochastic trends, and they never diverge too far from each other since their difference et is stationary. The cointegrated variables Pit and Pjt exhibit a long term equilibrium relationship defined by

Pit = a +bPjt +et, and et is the equilibrium error, which represents short term deviations from the long-term relationship. b, cointegrating vector is regarded as an underlying force to make Pit and Pjt to maintain a long term equilibrium relationship.

In this study, Johansen

(1988) cointegration test is used to identify cointegrating relationship among the variables.

4. Risk Decomposition Model

We use a risk decomposition model to measure a differential degree of market integration across different capital markets. The rationale for developing an easily obtainable measure of county equity market segmentation lies in the importance of such a tool in country selection for portfolio diversification purposes. The proposed measure of equity market integration is a country’s systematic risk contribution to the global and the

13

regional benchmark market portfolios; more contribution implying a greater integration of the market with the benchmark. score.

The degree of integration is measured by integration

Integration score is calculated as a fraction of systematic risk in total country risk

vis-à-vis the benchmark. This paper uses the risk decomposition methodology suggested by Akdogan (1996, 1997) and Barari (2004). For the time-varying evolution of stock market linkages, this methodology is based on computing the individual countries’ contribution to the global and regional systematic risks.

Consider the following single index Return-generating model of the ith country,

Ri= a +br qr +bwRw +ei

(3)

where Ri and Rw are returns on the ith country index and on a benchmark index, respectively. qr is orthogonal to Rw and is obtained as residuals from the following regression:

Rr = a +brRw +qr

(4)

In the equations (3) and (4) above, Ri is the rate of return on the ith country, Rr and Rw are the rates of return on the benchmark regional and world portfolios respectively.

Barari

(2004) points out that by utilizing the above model we effectively break down the rate of return on the ith country into three components: (1) a component that is perfectly correlated with the rate of return on the regional market, (2) a component of the international market rate of return that is uncorrelated with the rate of return on the regional market, and (3) a

14

third component that is uncorrelated with either the first or the second component. The variance of Ri can then be decomposed by dividing both sides by var (Ri). We express the risk arguments on the right-hand side as fractions of total risk of investing in the ith country portfolio down into the following components.

ai=bir2var(qr)/var(Ri), bi=biw2var(Rw)/var(Ri), ci=var(e)/var(Ri) 1=a+b+c

ai, bi, and ci representing the regional systematic risk, world systematic risk, and unsystematic risk, respectively. For instance, ai is a relevant measure of the ith country regional integration, implying that if the country’s contribution to the regional systematic risk rises, it is becoming more integrated with the regional market. Likewise, bi is a relevant measure of the ith country international integration, implying that if the country’s contribution to the world systematic risk rises, it is becoming more integrated with the world market. In turn, if the regional market is becoming increasingly integrated with the world market, ai will be larger than bi, while the regional market’s segmentation from the rest of the world will be shown by ai larger than bi. Thus, by taking the ratio of by ai to bi, the ith country’s regional versus world integration can be observed. country’s unsystematic risk.

15

ci measures the

IV. Data and Sample Statistics

We use weekly close price indices of Korea Stock Composite (KOSPI), Shanghai Composite, and Nikkei 225 from January 2000 to September 2010 as the basis for our data. Returns are calculated as continuously compounding rates of returns. We use the S &P 500 return as a global benchmark against which we compare the individual markets due to it being one of the strongest representatives of the global financial market. Regional market return was measured by using equally weighted portfolio return of the regional countries excluding home market. All data was collected from Yahoo Finance (finance.yahoo.com). Since we use weekly data, there is no problem of temporal asymmetries. Table 2 reports basic descriptive statistics for the data. Korea displays the highest mean return and it is also rather volatile, with 33% higher standard deviation than that of United States. The Komogorov-Smirnov D tests reject the hypothesis of normality and left-skewness is found in all markets except China. For Japan, stock market underperforms the United State. The best performance among three markets is achieved by Korea (0.11%) and the lowest is Japan (-0.12%).

Descriptive Statistics of Weekly Returns, 2000-2010 Komogorov Return

Mean Maximum Minimum Std.Dev Skewness Kurtosis

-Smirnov D (P Value) 0.0749

Korea

0.11

17.03

-22.93

4.01

-0.61

6.81

US

-0.04

11.36

-29.95

2.92

-1.97

23.06

0.0829

China

0.09

13.94

-14.90

3.63

0.05

4.69

0.0521

Japan

-0.12

0.08

-36.26

3.44

-2.13

24.03

***, ** and * represent the levels of significance of 1%, 5% and 10% respectively.

16

(0.01)***

0.0562 (