Stock Market in South Korea

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

2. 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).

3.  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 flatYTD, 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.

5.  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.

6. 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.

7. 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.

9. 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.

10.  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.

11.  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.

12. What’s Keeping The KOSPI Down
 There are several factors contributing to KOSPI’s
disappointingYTD performance.We found three major
causes, and discuss how our view differs from the
consensus.

13. 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.

14.  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.

16. 2. Sell-off from Vanguard Emerging
Market ETF fund
 In the March Green Book we discussedVanguard
changing the tracking index of its popular Emerging
Market ETFVWO. 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.
 DespiteVanguard’s promise of a smooth transition, we
foundVanguard’s sale of South Korean names was
surprisingly quick.Table 3 shows the top 20Vanguard EM
fund sales over the past three months. Of the sales, 19 of
the names are South Korean companies.

17.  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.

19. 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.

20.  Since October 2012, theYen 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 theYen
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.

22.  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 2004-2007Yen
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.

24.  Third, despite the rapid rate ofYen depreciation this time
compared to any other period in the past, the South Korean
Won to JapaneseYen 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 perYen started
around 11, then declined to 7.6.This time, even after theYen’s
quick 20% fall,Won perYen 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 theYen, 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 theYen’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 IndexYOY
growth (Chart 5).

26. 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 andThailand.

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

29.  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 depreciatingYen
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.

30.  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.

31.  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

33.  South Korea has the largest weight in the MSCI EM Index,
and has delivered disappointing returnsYTD 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?

36. 36
Who has more influence on Asian Stock Markets around the
Subprime Mortgage Crisis－the U.S. or China?

37. 37
 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.

38. 38
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

39. 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 Japan
after 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.
39

40. 40
Forbes and Rigobon
(2002)
The correlation coefficients
are conditional on market
volatility.
(heteroskedasticity).
There was virtually no
increase 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 by
the 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.

41. 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 Financial
Crisis, but it did during
the financial crisis.
41

42. 42
 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.

43. 43
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[ 2
11 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
3
1  


  tdt
P
i
itt YYY 
)]exp(1[ 2
1 tY under the null of 0
by the following auxiliary regression:
, and approximate Eqn. (1)
Then, the null hypothesis and alternative hypothesis are expressed
0 (non stationarity) against 0 (nonlinear stationarity).

44. 44
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.

45. (3)7,..........2,1,110,   iXY titti 
45
• Comparisons ofYi,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 currentYi,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).

46. 46
],[ ttt MTI 
Next, the residuals ε, are used in:
)4()1(
1
1
1211 tit
p
i
ittttt 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

47. 47
 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

48. 48
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.

49. 49
CF AF
Empirical Results
CF AFrCF AF rCF 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).

50. 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**
50
Results of Correlation Coefficient of Return
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.

51. 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**
51
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 ofVolatility of Return

52. 52
.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
.005
.006
.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

53. 53
t Statistics on ˆ
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 RootTest – KSSTest
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.

54. 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
54
Results of the Engle-GrangerTest for Co-integration
Notes: * and ** denote significance at the 10% and 5% significance levels, respectively.

55. 55
CF AF rCF AF rCF AF 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-SiklosTest forThreshold Co-integration
Notes: *, ** and *** denote significance at the 10%, 5% and 1% significance levels, respectively.

56. Conclusions
56
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