Stock market of south korea

58
Stock Market of South Korea Yuvraj Samant National Law University Jodhpur 784

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Stock Market in South Korea

Transcript of Stock market of south korea

Page 1: Stock market of south korea

Stock Market of South Korea

Yuvraj Samant

National Law University Jodhpur

784

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South Korea’s Stock Market Structure South Korea’s equity market is comprised of

two segments: 762 stocks listed on the original Korea Stock Exchange (Stock Market Division in the now integrated Korea Exchange), and 982 stocks listed on the original KOSDAQ (now called KOSDAQ Market Division).

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The Korea Stock Exchange listed issues are traditionally the major focus for foreign investors. Of the 99 stocks currently included in the MSCI South Korea Index, only one trades on the KOSDAQ. YTD performance of the two market segments is strikingly different. While the KOSPI Composite (which tracks all stocks listed on the original Korea Stock Exchange) is flat YTD, the KOSDAQ Composite index is up significantly (Chart 1). Since Korea Stock Exchange listed stocks are the major investment vehicles for foreign investors, the lagging KOSPI causes a lot of international concern.

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Similar to the S&P 500 index, the KOSPI 200, a popular local market benchmark, tracks the largest 200 companies on the Korea Stock Exchange and accounts for more than 70% of its total market cap. The MSCI South Korea Index, which tracks the higher end of the large cap universe, has only 6 of the 99 companies not already in the KOSPI 200. Combing through the largest 200 companies, some notable traits can be found.

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One company dominates the index performance Samsung Electronics Co. Ltd., with a market

cap exceeding USD 200 billion, is the big fish in a small pond. It accounts for 25% of KOSPI 200 index weight, and more than 30% in the MSCI South Korea Index. The influence of this single company is actually even bigger than the weight percentage indicates, as many of the electronic component suppliers are captive entities under Samsung’s corporate umbrella. While the company had a banner year in 2012 (up 55% in USD terms), it is down 3.5% YTD, providing no support to the market.

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Export-oriented companies dominate the stock universe Many of the largest 200 companies on the Korea

Stock Exchange are geared towards the export market. Table 2 lists foreign sales of the five largest companies as a percentage of total sales; all but one exceeds 50%. The low percentage of POSCO’s foreign sales is misleading, as its steel products are sold to South Korean auto makers and shipbuilders, whose final products are ultimately sold overseas. POSCO has much more foreign market exposure than 25%, albeit indirectly. This applies to many other South Korean industry suppliers. When an auto parts manufacturer or semiconductor supplier sells to Hyundai or Samsung, they gain overseas exposure through their customers.

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Non-financial companies in KOSPI 200 can be grouped into five large segments Each group is led by a couple of national

champions and followed by a group of suppliers.

Electronics: The leading companies are Samsung and LG, as well as a number of components and material suppliers. This segment is a major export powerhouse.

Auto Makers: Kia and Hyundai Motor, then a group of auto parts suppliers, steel products manufacturers and material suppliers. This segment sells most of their products overseas.

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Engineering & Construction: A less noticed export industry in South Korea. Their main export markets are Africa and the Middle East, as well as other Asian countries. The largest company generates 60% of its revenue overseas, while the second largest player has over 80% of its sales from foreign countries.

Chemicals: The percentage of export sales varies among companies, but many of them exceed 40%, including the top two players (74% and 64%, respectively). Compared to Japan’s Chemical industry, most South Korean companies focus on lower value, commodity-type chemicals, but some aspire to expand to the specialty/fine chemicals used in the auto, semiconductor, and clean energy industries, where they compete directly with Japanese peers.

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Shipping: this segment includes shipbuilding and shipping companies. Shipyards sell mostly to shipping companies based in Korea but since the latter group generates significant business overseas, shipbuilding companies by default are tied to the export market.

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What’s Keeping The KOSPI Down There are several factors contributing to

KOSPI’s disappointing YTD performance. We found three major causes, and discuss how our view differs from the consensus.

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1. The North Korean threat North Korea’s provocative actions have stirred fears

among foreign investors. While domestic investors kept buying during and after these incidents, foreign investors have been net sellers of South Korean stocks (one reason KOSPI lags KOSDAQ is foreign selling has largely been in the KOSPI, not the KOSDAQ). Maybe domestic investors have seen it all in the past? When North Korea conducted two rounds of nuclear tests in 2006 and 2009, the KOSPI reacted negatively in ensuing days, but returned double-digit gains six months later. Other hostile incidents in the past also failed to cause too much disruption to the market.

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Aside from the impact of individual geopolitical incidents, we believe the frequency of such events causes fears among foreign investors. Chart 2 shows the frequency of North Korean provocations since 1999; obviously 2013 has set the record even though the year is not even half over.

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2. Sell-off from Vanguard Emerging Market ETF fund In the March Green Book we discussed

Vanguard changing the tracking index of its popular Emerging Market ETF VWO. In the new index, South Korea is not classified as an Emerging Market country, requiring Vanguard to sell all South Korean holdings from its EM fund.

Despite Vanguard’s promise of a smooth transition, we found Vanguard’s sale of South Korean names was surprisingly quick. Table 3 shows the top 20 Vanguard EM fund sales over the past three months. Of the sales, 19 of the names are South Korean companies.

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Judging by the speed of the sales and the remaining holdings of the South Korean names in this fund, we think this selling pressure will persist for another three to four months. By the fourth quarter of this year, these companies should see some relief from the heavy selling.

All VWO’s South Korean holdings are listed on the Korea Stock Exchange (not KOSDAQ), as the fund originally tracked the MSCI South Korea Index. As discussed earlier, the latter index covers 99 stocks, of which 98 are listed on the Korea Stock Exchange. For that reason, the fund rebalance has only impacted KOSPI’s performance, but not the KOSDAQ’s. This is another factor contributing to the YTD performance differentials of the two.

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3. Abenomics and the depreciation of the Yen Newly elected Japanese Prime Minister Shinzo

Abe made headlines this year with his array of policies proposed to raise inflationary expectations and bring his country out of its prolonged recession. With his call for fiscal expansion and monetary easing, the most noticed consequence internationally is the depreciation of the Yen. Unlike the first two risks to the South Korean equity market, the negative impact of a depreciating Yen is harder to dismiss.

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Since October 2012, the Yen has depreciated more than 20% against the Won. Investors are dumping Korean exporters in fear of losing their competitive edge to Japanese competitors. This fear has proven correct in the past. Since 1999, South Korean companies have largely outgrown their Japanese peers in terms of exports (the readings in the middle panel of Chart 3 stayed positive most of the time), except from 2004-2007 when the Yen had a long stretch of depreciation against the Won (upper panel of Chart 3), and from 2001-2002 (due to the bursting of the tech bubble, not the exchange rate). During the 2004-2007 period, Japanese exports caught up to South Korea’s, significantly narrowing the growth differentials. However, there are other interpretations of this chart as well.

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First, despite the narrowing differential with Japan, South Korea grew its exports during 2004-2007 by double digits every quarter. Even though it was losing some edge to Japan, South Korea was gaining market share at the expense of other countries during that period. This is no surprise, judging by Korean companies’ improving product quality and technological innovations.

Second, Japanese exports only started catching up to South Korea’s in the second half of the 2004-2007 Yen depreciation cycle. This indicates that without a prolonged and sustained trend in exchange rates, it’s hard for countries with a depreciating currency to gain any advantages, as companies in these countries may be hesitant to cut export prices for more market share, if the direction of exchange rates could reverse.

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Third, despite the rapid rate of Yen depreciation this time compared to any other period in the past, the South Korean Won to Japanese Yen ratio still stands at an elevated level, merely offsetting the sudden dramatic depreciation of Won during the 2009 crisis. During the 2004-2007 period, Won per Yen started around 11, then declined to 7.6. This time, even after the Yen’s quick 20% fall, Won per Yen stands at 11 today.

Nevertheless, we still cannot be sure that any future currency movement won’t affect South Korea’s export business. The continued downward movement of the Yen, pushing the rate below its long term average, would make us more worried. However, we think this scenario is quite unlikely, as other Asian countries have voiced opposition to the Yen’s recent movement. Even if the exchange rate does change the international competitive landscape, certain areas of South Korean exports should weather the challenge better than others (see next page). A bigger worry is shrinking international trade volume. South Korea’s export volume has been weak in the past four quarters (Chart 4), echoing the trend of our Global Trade Index YOY growth (Chart 5).

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Is It Time For Bargain Hunting In South Korea?

The ultimate question is, with the recent underperformance of KOSPI, should investors take another look at the country and get back in? South Korea’s relative valuations have reached ten year lows (Chart 6), especially compared to other high-flying, smaller EM Asia countries. For investors wishing to keep their exposure to EM Asia, maybe it is time to redeploy capital back to South Korea from countries such as Indonesia, Philippines and Thailand.

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As we’ve discussed, the geopolitical risks and selling pressure from Vanguard should go away eventually, providing relief to KOSPI. However, it is not easy to completely dismiss the impact of the Yen depreciation (despite our counter argument providing food for thought). Investors who worry about the latter risk have two options:

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1. Among the five major business segments within KOSPI, buy Electronics. Samsung and LG compete mainly on quality and technology, and these two companies have gained on their Japanese competitors in the past few years. For this segment, the depreciating Yen may have less of an impact. Shipping is the least favored, not only due to Japanese competition but to shrinking global trade volume concerns as well.

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2. If one wants to avoid the export-heavy KOSPI, look at stocks listed on the KOSDAQ. Most companies there target domestic consumption, providing protection against declining global trade and intensified international competition.

But what about investors who cannot buy individual stocks in South Korea, and cannot tilt their investment towards specific business segments? Don’t worry. Samsung Electronics accounts for more than 30% of the weight of the MSCI South Korea Index. Counting its suppliers, that number is even higher. Any ETFs tracking the MSCI South Korea Index gives you exposure to the safer segment within the country.

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Stocks in South Korea had a positive performance during the last month. South Korea Stock Market (KOSPI), rallied 28 points or 1.42 percent during the last 30 days. From 1980 until 2013, South Korea Stock Market (KOSPI) averaged 809 Index points reaching an all time high of 2229 Index points in May of 2011 and a record low of 93 Index points in January of 1981. The Korea Stock Exchange Composite KOSPI is a major stock market index which tracks the performance of all common shares listed on the Korean Stock Exchange. It is a capitalization-weighted index. The KOSPI Index has a base value of 100 as of January 4, 1980. The KOSPI is a major stock market index which tracks the performance of large companies based in South Korea. This page contains - South Korea Stock Market (KOSPI) - actual values, historical data, forecast, chart, statistics, economic calendar and news

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South Korea has the largest weight in the MSCI EM Index, and has delivered disappointing returns YTD compared to its Asian peers (Table 1). Pressured by a military threat from its northern neighbor, anemic economic growth both at home and abroad, a competitive threat from Japan’s depreciating currency, and a sell-off from a large ETF sponsor, South Korea is facing a perfect storm. Will its stock market decline further or is this an opportunity to do some bargain hunting?

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Who has more influence on Asian Stock Markets around

the Subprime Mortgage Crisis- the U.S. or China?

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The main findings demonstrated that with the application of

traditional symmetric co-integration tests of Engle and Granger

(1987), the subprime mortgage crisis did not reinforce the co-

movement trends between the U.S. and China’s markets and

Asian markets. However, with the application of the Enders-

Siklos threshold co-integration test, there was significant

increase in these asymmetric co-integration relationships

between them during the period of the subprime mortgage

crisis.

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Four different approaches utilized to measure international shock transmission effect by Dornbusch et al. (2000) and Forbes and Rigobon (2001).

Cross-market correlation coefficients (the change of common

trend)

ARCH or GARCH frameworks (volatility spillover effect)

Co-integration techniques (the change of common trend)

Direct estimation of specific transmission mechanisms by using

the Probit model.

Literature Review

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Researchers approaches Findings

King and Wadhwani (1990)

Lee and Kim (1993)

The correlation approach The cross-market correlations increased significantly among the U.S., the U.K., and Japanafter the U.S. stock market collapse in October 1987.

Cha and Oh (2000) The correlation approach The links between the developed markets and the Asian emerging markets had significantly intensified after the U.S. stock market collapse in 1987 and during the Asian Financial Crisis in 1997.

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Forbes and Rigobon (2002)

The correlation coefficients are conditional on market volatility.(heteroskedasticity).

There was virtually noincrease in unconditional correlation coefficients during the 1997 Asian Financial Crisis, 1994 Mexican devaluation, and 1987 U.S. stock market collapse.

Caporale et al. (2005)

The conditional variance bythe application of both heteroskedasticity and endogeneity

The existence of contagion within the stock markets in Hong Kong, Japan, South Korea, Singapore, Taiwan, and Malaysia during the 1997 Asian Financial Crisis.

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Hamao et al. (1990) The GARCH method The volatility spillovers of the stock indices from New York to Tokyo, London to Tokyo, and New York to London after the U.S. stock market collapse in 1987.

Sheng and Tu (2000) The Co-integration method The co-integration did not exist in the eleven Asian stock markets and U.S. stock market before the 1997 Asian FinancialCrisis, but it did during the financial crisis.

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Co-integration relationship → a common trend.

↗ an upward status (positive impact)

asymmetric adjustments

↘ a downward status (negative impact)

Li and Lam (1995), Koutmos (1998), and Chiang (2001)

What is the impact of the Subprime Mortgage Crisis from the U.S. stock markets on the Asian stock markets during the period of the financial crisis?

Exploration of these problems by the asymmetric threshold co-integration model.

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Nonlinear ESTAR Unit root test by Kapetanios et al.(2003) The KSS nonlinear stationary test is based on detecting the

presence of non-stationarity against nonlinear but a globally stationary exponential smooth transition autoregressive model (ESTAR) process:

Methodologies

(1) )]exp(1[ 211 tttt YYY

• Kapetanios et al. (2003) follow Luukkonen et al. (1988) to compute

a first-order Taylor series approximation to the

(2) T,.......... 2, 1, t,1

1

31

tdt

P

iitt YYY

)]exp(1[ 21 tY under the null of 0

by the following auxiliary regression: , and approximate Eqn. (1)

Then, the null hypothesis and alternative hypothesis are expressed0 (non stationarity) against 0 (nonlinear stationarity).

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Enders and Siklos (2001) Threshold Co-integration Model

• The Enders and Siklos (2001) technique extended the Engle and Granger (1987) framework to test non-linear co-integration (Enders and Granger, 1998).

• Enders and Siklos (2001) modifies ε to allow for two types of asymmetric error corrections based on a co-integrating relationship as depicted in OLS.

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(3) 7 , .......... 2 ,1 ,110, iXY titti

• Comparisons of Yi,t and Xt-1 : Yi,t : The variables of the Asian stock markets on period t. Xt-1 : The variables of the U.S. stock market (S&P 500 index) on period t-1.

The study of the co-integration relationships between the current Yi,t

data of the six major Asian stock markets with the following Xt-1 data of the U.S. stock market. (Eun and Shim, 1989; Liu et al., 1998)

Equation (1) : The long-run equilibrium relationship between the U.S. and China and the six major Asian stock markets (Taiwan , Hong Kong, Singapore, Japan, Korea, India).

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],[ ttt MTI

Next, the residuals ε, are used in:

)4( )1(1

11211 tit

p

iittttt II

tI

tT 0

1

1

1

cif

cif

t

t

tM 0

1

1

1

rif

rif

t

t

candr : threshold values

TAR Model

M-TAR Model

, such that: is the Heaviside indicator function, where

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The threshold value is endogenously determined by using the Chan’s (1993) grid search method to find the consistent estimate of the threshold. This method arranges the values, in an ascending order and excludes the smallest and largest 15 percent, and the consistent estimate of the threshold is the parameter that yields the smallest residual sum squares (RSS) over the remaining 70 percent.

We test the null hypothesis of no co-integration relationship by

(5), and test the null hypothesis of symmetric adjustment by (6)

(5) 0: 210 H

(6) : 210 H

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Data

This study chose the S&P500 index to represent the U.S. stock markets and the SSE Composite index to represent the China stock markets.

The other Asian stock markets include Taiwan, Hong Kong, Singapore, Japan, Korea and India, and all observations are taken logarithm, and we only kept the data of synchronized trading days in all stock markets. (Hamao et al., 1990)

The entire sample period : 2004/1/2 to 2010/3/31.

The cutting point : March 13, 2007 (the time when the Subprime Mortgage Crisis of the New Century Financial Corp took place. Gorton, 2008)

The period of “pre Subprime Mortgage Crisis” : 2004/1/2 to 2007/3/13.

The period of “during the Subprime Mortgage Crisis” : 2007/3/14 to 2010/3/31.

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CFAF

Empirical Results

CF AFrCF AF r

CF AF r

Entire period Pre-subprime mortgage crisis During subprime mortgage crisis

Correlation Coefficient of Return 0.0881 0.0724 0.0948

Correlation Coefficient of Volatility of Return 0.4613** 0.0954 0.3792**

Engle-Granger Co-integration -0.704 -2.034 -0.319

Ender-Siklos Threshold Co-integration

4.058 1.589 -0.0187 4.346 1.776 0.0132 3.636 1.121 -0.0246

Relationships between the U.S. and China

Notes: 1. ** denote significance at the 5% significance levels, respectively.

2. The critical values of the Engle-Granger Co-integration are taken from Engle and Yoo (1987).

3. The lag-length of difference Ks selected by minimizing AIC; r is the estimated threshold value.

denote the F-statistics for the null hypothesis of no co-integration and symmetric adjustment. Critical values are taken from4. and

Enders and Siklos (2001).

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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis

Panel A (U.S.)

aiwan 0.2824** 0.2368** 0.3025**

Hong Kong 0.3707** 0.2221** 0.3946**

Singapore 0.3807** 0.1718* 0.4165**

Japan 0.2925** 0.1881* 0.3217**

Korea 0.3367** 0.2376** 0.3753**

India 0.3494** 0.1850* 0.4037**

Panel B (China)

Taiwan 0.2835** 0.0927 0.3657**

Hong Kong 0.4204** 0.1789* 0.4953**

Singapore 0.3203** 0.1574* 0.3715**

Japan 0.2794** 0.1226* 0.3393**

Korea 0.2793** 0.1048 0.3576**

India 0.2665** 0.0513 0.3601**

Results of Correlation Coefficient of Return

Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.

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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis

Panel A (U.S.)

Taiwan 0.6755*** 0.3752** 0.6279***

Hong Kong 0.8694*** 0.5163** 0.8261***

Singapore 0.7179*** 0.4206** 0.6535***

Japan 0.8885*** 0.3480** 0.8884***

Korea 0.8214*** 0.3564** 0.8711***

India 0.5513** 0.3260** 0.5688**

Panel B (China)

Taiwan 0.4280** 0.0464 0.4001**

Hong Kong 0.5572** 0.3075** 0.4682**

Singapore 0.4212** 0.2290** 0.5573**

Japan 0.4790** -0.0018 0.4616**

Korea 0.4032** 0.0232 0.4147**

India 0.5131** 0.2117** 0.5138**Notes: 1. The volatility of return is measured by the conditional variance of return from the ARMA(p,q)-GARCH(p,q) model; the numbers

in the parentheses are the appropriate lag-lengths selected by minimizing AIC.

2. ** and *** denote significance at the 5% and 1% significance levels, respectively.

Results of Correlation Coefficient of Volatility of Return

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.0000

.0004

.0008

.0012

.0016

.0020

.0024

.0028

2004 2005 2006 2007 2008 2009

U.S.

.0000

.0004

.0008

.0012

.0016

.0020

.0024

.0028

2004 2005 2006 2007 2008 2009

TAIWAN

.000

.001

.002

.003

.004

.005

.006

2004 2005 2006 2007 2008 2009

HONGKONG

.000

.001

.002

.003

.004

.005

2004 2005 2006 2007 2008 2009

SINGAPORE

.000

.001

.002

.003

.004

2004 2005 2006 2007 2008 2009

JAPAN

.000

.001

.002

.003

.004

2004 2005 2006 2007 2008 2009

KOREA

.000

.001

.002

.003

.004

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.007

2004 2005 2006 2007 2008 2009

INDIA

.0000

.0004

.0008

.0012

.0016

.0020

.0024

2004 2005 2006 2007 2008 2009

CHINA

The Volatility of Return in 8 Stock Markets

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t Statistics on ̂

Level First difference

U.S. -1.360(2) -18.272(1)***

Taiwan -1.475(1) -18.873(2)***

Hong Kong -1.483(0) -18.433(0)***

Singapore -1.463(2) -17.689(1)***

Japan -1.548(1) -17.653(1)***

Korea -1.294(0) -18.715(2)***

India -1.072(1) -17.531(2)***

China -0.843(3) -16.913(3)***

Results of the Nonlinear Unit Root Test – KSS Test

Notes: 1. The numbers in the parentheses are the appropriate lag-lengths selected by minimize AIC.

2. The simulated critical value for different Ks were tabulated in Kapetanios et al. (2003).

3. *** denote significance at the 1% significance level, respectively.

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Entire period Pre-subprime mortgage crisis During subprime mortgage crisis

Engle-Granger ADF Statistic Engle-Granger ADF Statistic Engle-Granger ADF Statistic

Panel A (U.S.)

Taiwan -1.458 -2.587 -1.443

Hong Kong -1.061 -3.728** -2.104

Singapore -1.292 -2.801 -1.727

Japan -2.032 -1.908 -2.376

Korea -1.232 -1.850 -2.527

India -0.689 -2.999 -1.429

Panel B (China)

Taiwan -2.105 -2.488 -2.379

Hong Kong -2.632 -1.393 -2.521

Singapore -1.953 -1.341 -2.575

Japan -1.235 -1.557 -2.705

Korea -2.352 -1.272 -3.144*

India -1.912 -1.187 -1.959

Results of the Engle-Granger Test for Co-integration

Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.

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CFAF rCF

AF rCFAF r

Entire period Pre-subprime mortgage crisis During subprime mortgage crisis

Panel A (U.S.)

Taiwan 37.302*** 3.310* 0.01349 9.943*** 1.057 -0.00860 50.027*** 6.267*** 0.01537

Hong Kong 48.536*** 3.837** -0.01121 19.888*** 1.336 -0.00934 76.026*** 8.633*** -0.01410

Singapore 76.547*** 1.983 -0.01307 16.869*** 0.773 -0.01182 132.028*** 11.643*** -0.01577

Japan 74.756*** 3.053* 0.01475 16.519*** 2.756* -0.01238 106.267*** 7.262*** -0.01730

Korea 34.294*** 7.987*** -0.00581 22.702*** 1.479 0.01745 46.861*** 8.981*** -0.00564

India 23.808*** 2.792* -0.01604 13.264*** 1.598 0.02396 34.992*** 6.262*** -0.01863

Panel B (China)

Taiwan 4.305 2.042 -0.00784 0.906 1.728 0.01183 8.833** 4.818** 0.01184

Hong Kong 20.340*** 5.154** 0.00379 3.830 0.913 -0.00612 27.475*** 5.887** 0.01932

Singapore 10.648*** 0.561 -0.01112 4.300 0.389 0.00520 10.787*** 5.643** 0.01606

Japan 4.887 5.387** 0.01390 1.130 0.391 0.01402 10.807*** 7.792*** 0.00751

Korea 12.569*** 4.871** -0.00867 0.995 1.778 0.01406 15.028*** 6.746*** -0.00564

India 6.850** 0.617 -0.01734 1.153 2.312 0.01641 9.331*** 4.237** -0.02235

Results of the Ender-Siklos Test for Threshold Co-integration

Notes: *, ** and *** denote significance at the 10%, 5% and 1% significance levels, respectively.

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Conclusions

There are four major findings in this research:

First, there are significant increases in correlation coefficients of return

between the U.S. and Asian markets and between China and the Asian

markets during the financial crisis.

Secondly, there are significant increases in correlation coefficients of

volatility of return between the U.S. and Asian markets and between

China and the Asian markets during the crisis. (volatility spillovers).

Third, there are asymmetric co-integration relationships between the

U.S. and Asian markets (except the China market) around the crisis,

and the asymmetry in these co-integration relationships has

significantly increased during the crisis.

China has no co-integration relationship with the Asian markets before

the crisis, but, during the crisis, the asymmetric co-integration

relationship between China and the Asian markets appeared.

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The stock market co-movement between China and the Asian stock

markets increased during the financial crisis. Based on the

empirical results, this shows China has had more influence on the

Asian markets recently.

Finally, the subprime mortgage crisis has weakened the effect of

international portfolio diversification. But investors can somewhat

diversify risks by investing in U.S. and China simultaneously.

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The End

Thank you for your attention