Contagion Effects of the U.S. Subprime Crisis on ... · trade. The contagious effect of financial crises1 has been of great concern to academics because ... Mechanism crisis (1992),

Contagion Effects of the U.S. Subprime Crisis

on International Stock Markets

Abstract

This paper examines the contagion effects of the U.S. subprime crisis on international

stock markets using a DCC-GARCH model on 38 country data. We find evidence of

financial contagion not only in emerging markets but also in developed markets during

the U.S. subprime crisis. We also find evidence of a spillover effect of news concern-

ing sovereign credit ratings during the U.S. subprime crisis. The spillover effects are

significant for both positive and negative rating news. In addition, while the spillover

for positive rating changes indicates differential effect, that for negative rating changes

indicates common information effect.

JEL: F30; G01; G14; G15; G24

KEYWORDS: Financial contagion; U.S. subprime crisis; Dynamic conditional corre-

lation generalized autoregressive conditionally heteroskedastic (DCC-GARCH) model;

Sovereign ratings; International stock markets

1

1. Introduction

During the past years, financial markets have been stricken by the U.S. financial crisis trig-

gered by the bursting of the U.S. mortgage bubble. In the early stages of the crisis, the

securities backed with subprime mortgages held by many financial institutions rapidly lost

most of their market value because of a dramatic rise in these mortgages’ delinquencies and

foreclosures in the United States. This led to the reorganizations, liquidations, and govern-

ment bailouts of major U.S. financial institutions (e.g., Bear Stearns, Lehman Brothers, and

the American International Group) because their capital largely vanished.

A prominent feature of the U.S. subprime crisis is that the U.S. subprime crisis started

in the U.S. financial sector and rapidly spread, spilling over into not only other sectors of the

economy but also other countries. These events resulted in a collapse of the banking industry,

stock market crashes, a large decrease in liquidity on the credit market, economic recession,

and furthermore they have engulfed sovereign insolvency in almost all countries. Moreover,

this crisis affected real economies as well as financial markets, resulting, for example, in drops

in productivity growth, increases in unemployment rate, and a decrease in international

trade.

The contagious effect of financial crises1 has been of great concern to academics because

of its important consequences for the global economy in relation to monetary policy, opti-

mal asset allocation, risk measurement, capital adequacy, and asset pricing. Even though

financial market participants seem to obviously perceive financial contagion during periods

of financial crises and there exist extensive theoretical analyses2 of financial contagion, it is

1During the past 20 years, there have been many financial crises, such as the European Exchange RateMechanism crisis (1992), the Mexican devaluation (1994), the East Asian crisis (1997), the Russian default(1998), the devaluation of the Brazilian real (1999), the Internet bubble collapse (2000), the Brazilian stockmarket crash (2002), and more recently, the U.S. subprime crisis (2007).

2The following are important recent articles on several transmission channels of financial contagion:linkages among financial intermediaries by Allen and Gale (2000, 2004), wealth effects by Kyle and Xiong(2001), linkages between financial shocks and real activity by Kroszner et al. (2007), correlated informationshocks by King and Wadhwani (1990), cross market-hedging by Kaminsky and Reinhart (1999) and Kodresand Pritsker (2002), borrowing constraints by Yuan (2005), herding by Calvo and Mendoza (2000), monsoonaleffects by Masson (1999b), correlated liquidity shocks by Calvo (1999), market liquidity by Brunnermeierand Pedersen (2005, 2009), and heterogeneity of private information by Pasquariello (2007).

2

difficult to investigate empirically. Even nowadays there is little consensus on empirically

defining financial contagion, let alone its existence.

The issue of the existence of financial contagion in relation to financial crisis is related

to the statistical definition of financial contagion and how to measure the spread of market

disturbances. With recent empirical studies having colligated its definition, financial conta-

gion, in general, refers to the comovement of exchange rates, stock prices, sovereign spreads

and capital flows in one market as a result of a financial crisis in another markets. Hence,

financial contagion is used to refer to the spread of market shocks (mostly, on the downside)

from one country to another.3 Many researchers have focused on simple historical corre-

lation as a measurement of co-movement or dependence structure. To verify the existence

of financial contagion between countries, many studies have attempted to test whether the

correlations significantly change between tranquil and turmoil periods.

The results from studies on the existence of financial contagion are not compatible. King

and Wadhwani (1990) find a significant increase in the cross-country correlation coefficients

of stock returns during the 1987 U.S. market crash. Lee and Kim (1993) add developing

countries to the study and also find evidence of contagion. Calvo and Reinhart (1996) show

evidence of financial contagion during the Mexican crisis. The authors find that correla-

tions increased across weekly equity and Brady bond returns for emerging markets in Latin

America during the turbulence period of the Mexican crisis. Baig and Goldfajn (1999) also

find evidence of cross-country contagion in the currency and equity markets during the East

Asian crisis. However, Forbes and Rigobon (2002) show that tests for contagion based on

correlation coefficients are biased due to heteroskedasticity and find no increase in correlation

coefficients during the East Asian crisis, Mexican crisis, or 1987 U.S. market crash. Instead,

they find a continued high level of correlation in more tranquil periods and thus conclude

that these crises are not the result of contagion but rather of interdependence. Based on

their standard factor model, Corsetti et al. (2005) find evidence of contagion for only a few

3This definition is from Dornbusch et al. (2001). Other conceptual contributions are due to Masson(1999a), Kodres and Pritsker (2002) and Forbes and Rigobon (2002).

3

countries during the East Asian crisis.

The most important limitation of these studies on financial contagion is that contagion

effects are mostly estimated using correlation coefficients.4 If dependence between financial

markets is not normal, a correlation coefficient is not a sufficient measure of the depen-

dence structure because it is based on a linear measure of the dependence structure among

distributions. One example of non-normal dependence is contagion, which means that mar-

kets exhibit greater correlation during the turmoil period than during the tranquil period.

Also, as pointed out by Corsetti et al. (2005) and Forbes and Rigobon (2002), estimation

of cross-country correlation coefficients can be biased because of heteroskedasticity or the

omitted variable problem.5 Even if correlation coefficients are adjusted by the methodolo-

gies of Forbes and Rigobon (2002) or Corsetti et al. (2005), Billio and Pelizzon (2003) find

that the tests for financial contagion are highly affected by the source of the crisis and the

windows used.

A number of recent studies have tried to overcome this limitation using various methods to

measure financial contagion. Some studies developed models based on extreme value theory

(e.g., Hartman et al., 2001; Longin and Solnik, 2001; Bae et al., 2003), while others extended

multivariate generalized autoregressive conditionally heteroskedastic (GARCH) models with

asymmetry, Markov regime-switching and time-varying conditional correlations (e.g., Ang

and Chen, 2002; Ang and Bekaert, 2002; Cappiello et al., 2006; Chiang et al., 2007). Instead

of building new models of asset return, some studies measure the dependence between asset

returns using a Copula approach (e.g., Rodriguez, 2007; Horta et al., 2008).

Another issue of the empirical study of financial contagion is related to the spillover

effects of news about sovereign credit ratings across markets during specific crisis periods. A

sovereign credit rating is a rating agency’s evaluation of the potential ability of a sovereign

4Pukthuanthong and Roll (2009) show that the correlation across markets is a poor measure for estimatingglobal market integration; perfectly integrated markets can exhibit weak correlation.

5The conclusions of Forbes and Rigobon (2002) are criticized at least by Corsetti et al. (2005) andBartram and Wang (2005). They argue that the results of Forbes and Rigobon (2002) are caused by themodel they assumed.

4

obligor to assure timely and accurate payments of debt service. News concerning sovereign

credit ratings has far-reaching implications for investors because the sovereign bond market

is regarded as a benchmark for all other bond markets in the local economy, as well as the

fact that sovereign credit ratings basically indicate the risk level of a country’s investing

environment.

As classified by Gande and Parsley (2005), the spillover effects of the sovereign credit

rating or credit outlook changes of one country (labeled the event country) to the financial

markets of all other countries (labeled the non-event countries) can be divided into two

conceptual categories: common information effects and differential effects. While common

information spillovers mean that the financial markets of event and non-event countries

move in tandem, differential spillovers imply that the effects of rating events lead to opposing

results across countries. For example, a positive rating event, such as an explicit upgrade of a

credit rating or a favorable revision in a country’s credit outlook, could signal a widespread

common trend, thus providing a positive signal to all other countries. In this case, the

financial markets across countries would be more correlated. This case is referred to as

common information spillover. Alternatively, such positive events can reveal that the event

country’s financial market has been more attractive to investors than those of all the other

countries, providing a negative signal to these other countries. In this case, the correlation

between event and non-event countries would decrease. This case is referred to as differential

spillover (Gande and Parsley, 2005).

Earlier literature related to the spillover effects of sovereign credit rating changes mainly

focuses on asset returns. Brooks et al. (2004) examine the impact of a country’s sovereign

rating change on its own stock market returns. Gande and Parsley (2005) study the effect

of one country’s sovereign credit rating change on the sovereign credit spreads of other

countries. Ferreira and Gama (2007) examine the spillover effects of sovereign credit rating

changes on international stock market returns. All these studies show that the effect is

asymmetric: negative rating events have a significant effect, whereas positive rating events

5

have no significant effect. Kaminsky and Schmukler (2002) investigate the spillover effects

of sovereign credit rating changes on stock and bond markets in emerging economies during

periods including financial crises. They find that the effects of sovereign credit rating changes

are stronger during crises, in nontransparent economies, and in neighboring countries.

Our ultimate objective is to shed some light on the literature of financial contagion, es-

pecially as it relates to the U.S. subprime crisis. This paper has two main objectives: to

determine, first, the existence of financial contagion during the U.S. subprime crisis and,

second, whether one country’s sovereign rating news affected the cross-country correlations

of other countries’ stock market returns during the U.S. subprime crisis. Since simple corre-

lations have obvious limitations in the study of financial contagion, we employ the dynamic

conditional correlation (DCC) GARCH model proposed by Engle (2002). This framework

allows us to estimate time-varying conditional correlations without heteroskedasticity prob-

lems and an arbitrary turmoil period.

Our paper’s major findings can be summarized as follows. First, we confirm that the

correlation analysis employed in previous studies of financial contagion is not adequate for

the research of financial contagion during the U.S. subprime crisis. Thus, we apply a DCC-

GARCH model to capture the dynamics of conditional correlation. Second, we find evidence

of financial contagion during the U.S. subprime crisis. Evidence of a contagion effect is

observed not only in emerging markets but also in developed markets. The contagion effect

of the U.S. subprime crisis is more severe than that of the East Asian crisis. Third, we find

evidence of the spillover effect of news concerning sovereign credit ratings during the U.S.

subprime crisis. Contrary to the findings in earlier literature, positive as well as negative

rating events have significant effects on the correlations of stock market returns. Moreover,

the characteristics of spillover effects for positive rating changes differ from those for negative

rating changes. While the spillovers for positive rating changes indicate differential effects,

those for negative rating changes indicate common information effects.

This paper makes several important contributions to the recent literature on financial

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contagion. First, it examines financial contagion during the most recent crisis, that is the U.S.

subprime crisis. Previous literature on the existence of financial contagion largely focuses on

crises that took place during the 1990’s, especially the East Asian crisis. This paper provides

a broader understanding of the U.S. subprime crisis in terms of financial contagion. Second,

we consider a larger set of countries, including not only emerging markets (15 countries)

but also developed markets (23 countries), because, unlike previous financial crises, the U.S.

subprime crisis highly affects developed as well as emerging markets. Third, this paper

examines transmission across conditional correlations in response to sovereign credit rating

adjustments whereas previous literature exploring the spillover of news concerning sovereign

credit ratings largely focuses on transmission across asset returns and conditional volatilities.

These studies are feasible by estimating time-varying conditional correlations using a DCC-

GARCH model.

The rest of the paper is organized as follows. Section 2 describes the data set employed

and data descriptive statistics in this paper. Section 3 performs a preliminary correlation

analysis. Section 4 discusses the econometric specifications. Section 5 presents our empirical

results. Section 6 presents our conclusions.

2. Data

We examine financial contagion across three different market segments during the U.S. sub-

prime crisis by comparing it with that during the East Asian crisis. The primary data

we employ in this paper are stock indexes from the TF Datastream Global Equity Indices

database. The data cover two different crisis periods: from January 1, 2005 to August 31,

2009 (U.S. subprime crisis) and from January 1, 1996 to December 31, 2003 (East Asian

crisis). We break up these both sample periods into two sub-periods: a tranquil period and

a turmoil period. We define the tranquil and turmoil periods in the U.S. subprime crisis

period as extending from January 1, 2005 to July 31, 2007 and from August 1, 2007 to

7

August 31, 2009, respectively. We assume that the bursting of the U.S. mortgage bubble

occurred on August 1, 2007. In addition, we define the tranquil and turmoil periods in the

East Asian crisis period as extending from January 1, 1996 to July 1, 1997 and from July 2,

1997 to December 31, 2003, respectively, because the devaluation of the Thai baht on July

2, 1997 was a significant factor in triggering the East Asian crisis.

The data consist of three parts, each with different levels of market segments: a country-

level, a region-level, and a market development status. The country-level data cover 38

countries: Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Denmark,

Finland, France, Germany, Greece, Hong Kong, Indonesia, Ireland, Italy, Japan, Korea,

Malaysia, Mexico, Netherlands, New Zealand, Norway, Philippines, Poland, Portugal, Sin-

gapore, South Africa, Spain, Sweden, Switzerland, Taiwan, Thailand, Turkey, the United

Kingdom, the United States, and Venezuela. These countries cover various regions: ten

Asian countries, five Latin American countries, 18 European countries, two North American

countries, two Australasian countries, and one African country. Moreover, these data include

not only emerging markets but also developed markets.6 The region-level data contain four

regions: Asia, Australasia, Europe, and Latin America. The data divided by status of mar-

ket development consist of emerging market and developed market indexes. All indexes are

dollar denominated and calculated using dividend-unadjusted daily closing prices. Where

data were missing or unavailable, interpolated estimates were substituted. To adjust for the

effects of time-zone differences between the locations of exchanges in different countries, we

calculate stock market returns as rolling-average, two-day [0, 1] returns or weekly returns.

When we calculate weekly returns, we use Thursday-to-Thursday closing prices to avoid any

end-of-week effects.

[Insert Table 1 about here]

6These data contain 15 emerging markets (Argentina, Brazil, Chile, China, Indonesia, Korea, Malaysia,Mexico, Philippines, Poland, South Africa, Taiwan, Thailand, Turkey, and Venezuela) and 23 developedmarkets (Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Greece, Hong Kong,Ireland, Italy, Japan, Netherlands, New Zealand, Norway, Portugal, Singapore, Spain, Sweden, Switzerland,the United Kingdom, and the United States). We classify these countries into emerging and developedmarkets by following the criteria of the FTSE Group and Morgan Stanley Capital International.

8

Panel A in Table 1 presents descriptive statistics for two-day [0, 1] stock index returns

over the entire period of the U.S. subprime crisis period. These data exhibit the standard

properties of financial returns over the period from January 1, 2005 to August 31, 2009.

The average returns range from -0.045% (Ireland) to 0.093% (Brazil). Standard deviations

range from 0.800% (Malaysia) to 1.929% (Turkey). Most of these stock returns have negative

skewness and positive kurtosis, which means that the stock returns may not be normally

distributed. Also, the Jarque-Bera test strongly rejects the normality of the stock returns

series. To test for the presence of serial correlation in the returns, we consider Ljung-Box

statistics. The Ljung-Box statistics for the returns indicate significant serial correlations for

all markets investigated.

Panels B and C in Table 1 present descriptive statistics for two-day [0, 1] stock index

returns over the tranquil and turmoil periods, respectively, in the U.S. subprime crisis period.

Comparing the first two moments for these two sub-periods, we find that means are higher

during the tranquil period whereas standard deviations are higher during the turmoil period.

The differences of the average mean and standard deviation of country-level stock index

returns between the tranquil and turmoil periods are -0.15% and 0.85%, respectively. The

differences of the returns range from -0.263% (China) to -0.088% (Venezuela). The differences

of the standard deviations range from -1.34% (Venezuela) to 1.37% (Korea).7 Comparing

the high-order moments for these two sub-periods in the U.S. subprime crisis period, it is

remarkable that kurtosis is higher during the turmoil period. This finding suggests that

the empirical distribution has a fatter tail during the turmoil period. In other words, large

shocks are more likely to be present during the turmoil period and therefore, stock returns

are more likely to be non-normal during the turmoil period. As expected, not only stock

returns series during the tranquil period but also those during the turmoil period are non-

normal, as shown by the Jarque-Bera test. Not surprisingly, all stock returns demonstrate

7The differences of the average mean and standard deviation of region-level stock index returns betweenthe tranquil and turmoil periods are -0.14% and 0.92%, respectively. The differences of the returns rangefrom -0.162% (Latin America) to -0.113% (Asia). The differences of the standard deviations range from0.64% (Asia) to 1.22% (Australasia).

9

significant serial correlation.

[Insert Figure 1 about here]

Figure 1 shows region-level stock index returns during the period from January 1, 2005

to August 31, 2009. Figure 1 also shows U.S. stock index returns as the source country

of contagion. The vertical line in each of the panels indicates the start date of the turmoil

period, August 1, 2007. A volatility clustering effect can be observed for all these stock index

returns. Notably, many high peak clusterings are found during the turmoil period. To more

accurately capture this volatility clustering effect, we use a multivariate GARCH framework

for the estimation in this paper.

3. Preliminary analysis


First, we analyze the tests based on the correlation analysis, since previous studies of financial

contagion focus on structural shifts in correlation coefficients. Table 2 reports the uncon-

ditional correlations between stock index returns estimated over the entire U.S. subprime

crisis period. Weekly returns are used when unconditional correlations are calculated.8 The

correlation between region-level stock index returns is relatively high: the median of the cor-

relations is 0.841, the lowest correlation is 0.737 (between Asia and Latin America), whereas

the highest correlation is 0.857 (between Europe and Latin America). The correlation be-

tween country-level stock index returns is relatively low: the median of the correlations is

0.684, the lowest correlation is 0.008 (between Poland and Venezuela), and the highest cor-

relation is 0.957 (between France and Germany). Correlations between countries within a

8In this section, we use weekly returns instead of rolling-average, two-day [0, 1] returns because the formerare more effective in adjusting for time-zone difference effects. Unlike other studies of contagion, which focuson particular regions or countries, the data employed in this paper are widely distributed over various regionsand countries. Also, the results are similar and qualitatively unchanged when rolling-average, two-day [0, 1]returns are used in these analyses. The results are available upon request.

10

region are higher than those between countries in different regions. The median and mean

of the correlations between countries within one region are 0.785 and 0.752, respectively,

while those of the correlations between countries in different regions are 0.628 and 0.596,

respectively. Overall, the U.S. stock markets are fairly correlated with other stock markets.

Correlations between U.S. and region-level stock index returns range from 0.631 (between

the United States and Asia) to 0.798 (between the United States and Europe).

In this section, we test hypotheses of changes in correlation coefficients across the tranquil

and turmoil periods. As pointed out by Corsetti et al. (2005) and Forbes and Rigobon (2002),

estimation of cross-market correlation coefficients is biased because of heteroskedasticity

in market returns. In other words, correlation coefficients tend to increase during more

volatile periods. To take into account heteroskedasticity, we adjust correlation coefficients

as proposed by Forbes and Rigobon (2002). The adjusted correlation coefficient (ρ*) can be

written as

ρ∗ =ρ√

1 + δ[1− ρ2], (1)

where δ ≡ σhsource

σlsource

−1 is the relative increase in the variance of the source country and ρ is the

conditional correlation coefficient.9 For comparison, we also consider the East Asian crisis

period. We assume that the United States is the source country of financial contagion in the

U.S. subprime crisis period and that Thailand is the source country of financial contagion

in the East Asian crisis period.


Table 3 reports the results of tests of structural shifts in correlation coefficients. Although

the tests for financial contagion based on adjusted correlation coefficients are not as signifi-

9We use a test statistic for the null hypothesis of no increase in correlation coefficients. Morrison (1983)

proposes the test statistic: T = (Z0 − Z1)/√

1/(N0 − 3) + 1/(N1 − 3), where Z0 = 1/2 ln [(1 + ρ0)/(1− ρ0)]

and Z1 = 1/2 ln [(1 + ρ1)/(1− ρ1)] are the Fisher transformations of the correlation coefficients during thetranquil and turmoil periods, respectively. N0 = 134 , N1 = 108 are the number of observations before andafter the crisis. This test statistic is also used in Basu (2002), Corsetti et al. (2005), and Chiang et al.(2007).

11

cant as those based on unadjusted correlation coefficients, some results show that correlation

coefficients, especially between Asian countries, significantly increased during the East Asian

crisis.10 However, the results show that there was no increase in correlation coefficients dur-

ing the U.S. subprime crisis. The null hypothesis of no increase in correlation is not rejected

by all cases during the U.S. subprime crisis. Moreover, adjusted unconditional correlations

are lower than correlations during the tranquil period in most of cases (China, Norway, and

New Zealand being the exception) during the U.S. subprime crisis. These findings can re-

sult not only from a lack of evidence of financial contagion during the U.S. subprime but

also because of the distortion of the adjusted correlation coefficient proposed by Forbes and

Rigobon (2002) when the main source of heteroskedasticity is the idiosyncratic component

of the source market.

This correlation analysis based on correlation coefficients demonstrates the importance

of bias in tests of changes in correlation due to heteroskedasticity. Also, one can realize

that correlation analysis is not enough to investigate financial contagion during the U.S.

subprime crisis. In the next section, we discuss the econometric methodology that enables

us to investigate financial contagion during the U.S. subprime crisis.

4. Econometric methodology

We employ a multivariate DCC-GARCHmodel for the study of financial contagion during the

U.S. subprime crisis. The DCC-GARCH model proposed by Engle (2002) is a generalization

of Bollerslev et al.’s (1992) constant conditional correlation (CCC) model. This model

allows for not only heteroskedasticity in market returns but also time-varying correlation

processes. In particular, this model has computational advantages over other multivariate

GARCH models in the study of financial contagion because the number of parameters to be

estimated in the correlation process is independent of the number of series to be correlated.

Thus, it is well-designed for higher-dimension applications.

10This result is consistent with those of Corsetti et al. (2005) and Chiang et al. (2007).

12

Following the conventional approach, we use the model specification proposed by Chiang

et al. (2007).11 The returns equation is specified as

rt = γ0 + γ1rt−1 + γ2rU.S.t−1 + εt, (2)

εt|ℑt−1 ∼ N(0, Ht), (3)

where rt is an n×1 vector of stock returns, rU.S.t is the U.S. stock index return, and ℑt−1 is the

information set at time t-1. The AR(1) term is used to take into account the autocorrelation

of stock returns. Lagged U.S. stock index returns are usually used as a global factor.12

All DCC-GARCH models including the CCC-GARCH model use the fact that Ht can

be decomposed as

Ht ≡ DtRtDt, (4)

whereDt = diag{√hit} is the n×n diagonal matrix of time-varying standard deviations from

univariate GARCH models, and Rt is the n× n time-varying correlation matrix. The DCC-

GARCH model is designed to allow for a two-stage estimation of the conditional covariance

matrix Ht. In the first stage, univariate volatility models are fitted to each of the stock

return residuals and estimates of√hit are obtained. In this paper, we use a GARCH (1, 1)

model.

In the second stage, stock return residuals are transformed by their estimated standard

deviations as ui,t = εi,t/√

hii,t. Then, ui,t is used to estimate the correlation parameters. The

evolution of the correlation in the standard DCC-GARCH model is given by

Qt = (1− a− b)Q+ aut−1u′t−1 + bQt−1, (5)

11We also use the model specification proposed by Cappiello et al. (2006). This DCC model permitsconditional asymmetries on correlation dynamics. The results are similar and qualitatively unchanged. Theresults are available upon request.

12For example, by using a vector autoregression (VAR) model, Eun and Shim (1989) show that U.S. marketreturns significantly lead and explain other developed market returns.

13

Rt = Q∗t−1QtQ

∗t−1, (6)

where Qt = [qij,t] is the n × n time-varying covariance matrix of ut, Q = E[utu′t] is the

n × n unconditional variance matrix of ut, and a and b are scalars such that a + b < 1.

Q∗t = [q∗ii,t] = [

√qii,t] is a diagonal matrix with the square root of the ith diagonal element

of Qt on its ith diagonal position. As long as Qt is positive definite, Q∗t guarantees that Rt

is a correlation matrix with ones on the diagonal and the absolute values of all the other

elements less than 1.

As proposed by Engle (2002), the DCC-GARCH model can be estimated by using a two-

step approach to maximize the log-likelihood function. Let the parameters in D be denoted

by θ and the additional parameters in R be denoted by φ. The log likelihood function can

be written as the sum of a volatility part and a correlation part:

L(θ, φ) = LV (θ) + LC(θ, φ)

=[−1

2

∑t(n log(2π) + log |Dt|2 + ε′tD

−2t εt)

]+[−1

2

∑t(log |Rt|+ u′

tR−1t ut − u′

tut)].

(7)

The volatility part of the likelihood is the sum of individual GARCH likelihoods. In the first

stage, the volatility part of the likelihood is maximized to find

θ = argmax[LV (θ)], (8)

and the correlation part is then maximized in the second stage, given the estimated value in

the first stage

maxφ

[LC(θ, φ)]. (9)

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5. Empirical results

5.1 Existence of financial contagion during the U.S. subprime crisis


Table 4 reports the estimation results of the DCC-GARCH model using region-level and

developed/emerging market stock returns.13 While the coefficient of the AR(1) term in

the mean equation is insignificant except for Europe during the East Asian crisis period,

it is significantly negative for all region-level index returns during the U.S. subprime crisis

period. These strong negative serial correlations indicate the existence of positive feedback

trading in the U.S. subprime crisis period. Developed as well as emerging markets are

negatively autocorrelated during the U.S. subprime crisis period. Moreover, the negative

serial correlation in developed markets is more significant than that in emerging markets

during the U.S. subprime crisis period.

The coefficients of lagged U.S. stock returns are significant during both crisis periods,

which is consistent with the empirical finding that U.S. stock return is an important deter-

minant of stock returns in other countries. However, the coefficients of lagged U.S. stock

returns are more significant and greater during the U.S. subprime crisis period than dur-

ing the East Asian crisis period. In particular, the effect of U.S. stock returns on Europe,

which has more developed markets than other regions, is significantly positive at the 1%

level during the U.S. subprime crisis period, whereas it is insignificant during the East Asian

crisis period. This result indicates that the United States is the source country of the U.S.

subprime crisis, consistent with the results of Panel B in Table 4. The coefficients of lagged

U.S. stock returns are highly significant in developed as well as emerging markets during the

U.S. subprime crisis.

The coefficients of lagged conditional variance and squared innovations terms are highly

13We also estimate the DCC-GARCH model using country-level stock index returns. These data aremainly used in Section 5.2. The results are similar and qualitatively unchanged. The results are availableupon request.

15

significant for all returns. This result is consistent with the volatility clustering phenomenon

observed in most financial time series.

[Insert Figure 2 about here]

Figure 2 shows conditional correlations obtained from the DCC-GARCH model between

U.S. and region-level stock returns, and developed/emerging market stock returns during

the U.S. subprime and East Asian crisis periods.

While the conditional correlations increase in the short period close to the start of the

turmoil period during the U.S. subprime crisis period, they significantly decrease during the

early stage of the turmoil period. However, the correlations rise very quickly and reach their

peak during the second half of 2008. Although the conditional correlations decline and are

steady after the peak, the average levels of conditional correlations are higher than those

before the sudden surge in conditional correlations.

These results can be explained by Chiang et al.’s (2007) interpretation of the East Asian

crisis. The results indicate evidence of financial contagion in the early stages of the U.S.

subprime crisis and then a transition to herding behavior in latter stages. In the early

stages, investors did not recognize the financial crisis or view its source as a local country

problem. For this reason, conditional correlations decreased during the early stages of the

turmoil period, because investors rebalanced their portfolio from risky assets directly related

to the source of the crisis to other risky assets, instead of from risky assets to risk-free

assets. As the crisis evolved and most risky asset prices declined due to the contagion effect

spreading through various channels, investors panicked and rebalanced their portfolios from

risky assets to risk-free assets. This investor behavior can result in sudden increases in

correlations between international stock market returns.

As the crisis was recognized by most market participants, investor decisions converged

because the cost of collecting credible information was relatively high during the crisis.

Investors tended to follow major investors in making decisions about investments and over-

16

reacted to information, interpreting news about one country as news about a whole region.

This investor behavior leads to the persistence of high correlations after their sudden increase.

Compared with the results of Panel B in Figure 2 and previous literatures about the

East Asian crisis, during the U.S. subprime crisis the period of decreasing correlations dur-

ing the early stages of the turmoil period is relatively longer, the sudden period of rising

correlations is quite short, and the pace of increasing correlations is extremely fast. The

U.S. subprime crisis became apparent in 2007 when U.S. housing prices began to decline

steeply and mortgage delinquencies in the United States soared. Since securities backed

with subprime mortgages lost their value, the financial institutions that held these securities

suffered heavy losses. However, the important thing is that no investor could estimate the

loss of these financial institutions due to a lack of information and regulatory control about

such securities. It was not until several major financial institutions began going bankrupt

and announced the reduced value of their securities that investors recognized the seriousness

of the U.S. subprime crisis.14 Compared with the East Asian crisis, investors were relatively

late in recognizing the U.S. subprime crisis. Therefore, the shock during the U.S. subprime

crisis had a larger surprise component than that during the East Asian crisis.

To clearly demonstrate evidence of a contagion effect during the U.S. subprime crisis,

using dummy variable regression, we test whether the conditional correlations obtained from

the DCC-GARCH model significantly increase in the turmoil period. The regression model

is AR(1)-GARCH(1,1) using a dummy variable specified as

ρij,t = φ0 + φ1ρij,t−1 + dijDt + eij,t,

hρij,t = ϖρ

ij + αρije

2ij,t−1 + βρ

ijhρij,t−1,

(10)

where ρij,t is the conditional correlation among U.S., region-level, and developed/emerging

14For example, the five largest U.S. investment banks were failed during 2008. (Lehman Brothers wentbankrupt, Bear Stearns and Merrill Lynch were taken over by other companies, and Goldman Sachs andMorgan Stanley were bailed-out by the U.S. government) In addition, Government-sponsored enterprises(GSE) Fannie Mae and Freddie Mac either directly owed or guaranteed nearly $5 trillion in mortgageobligations in September 2008.

17

market stock returns during the U.S. subprime and East Asian crisis periods, Dt is a dummy

variable for the turmoil period, and hρij,t is the conditional variance of eij,t.


Table 5 reports the estimations of the regression model on structural shifts in the con-

ditional correlations obtained in the DCC-GARCH model.15 These results show evidence of

financial contagion during the U.S. subprime crisis, during which, the correlations between

U.S. and region-level stock returns significantly increase at the 15% level of significance. Asia

and Australasia are more correlated with U.S. stock returns in the turmoil period than in

the tranquil period relative to Europe and Latin America during the U.S. subprime crisis.

Compared with the East Asian crisis period, the increased levels of correlation between U.S.

and region-level stock returns during the U.S. subprime crisis are larger and more significant

than those during the East Asian crisis period. During the East Asian crisis period, the

correlations between U.S. and region-level stock returns increase but are insignificant. In

addition, the increased level of correlation between U.S. stock returns and developed market

returns is greater than that between U.S. stock returns and emerging market returns during

the U.S. subprime crisis, whereas emerging market returns are more strongly correlated with

U.S. stock returns in the turmoil period than in the tranquil period relative to developed

market returns during the East Asian crisis.

5.2 News spillover effects on conditional correlation

In the Section 5.1, we find evidence of financial contagion during the U.S. subprime crisis.

In this section, we examine the spillover effects of sovereign rating changes on correlations

between international stock markets during the U.S. subprime crisis. Standard & Poor’s

(S&P) foreign currency long-term sovereign ratings and credit outlooks are used as sovereign

15The empirical results are robust to alternative data specifications. For instance, we have changed themethods of treating missing or unavailable data. Instead of using interpolated estimates, we get rid of missingdata in our data set or put the same values as those of the previous trading day. The results are similar andqualitatively unchanged.

18

credit rating data because their credit ratings are more suitable for this paper than those of

other rating agencies. As reported by Kaminsky and Schmukler (2002), Brooks et al. (2004),

Gande and Parsley (2005), and Ferreira and Gama (2007), S&P credit ratings are more

active in making credit rating revisions and lead other rating agencies’ ratings. Moreover,

S&P rating changes seem to be more informative than those of other rating agencies because

S&P credit rating changes have a greater impact on the financial market.

To find out credit rating changes, we quantify credit ratings by calculating a compre-

hensive credit rating (CCR) measure used in Kaminsky and Schmukler (2002), Gande and

Parsley (2005), and Ferreira and Gama (2007). This CCR measure is simply defined by a

standard linear transformation. Appendix A presents the construction of the CCR measure.

First, letter ratings are assigned to the numerical values, ranging from 0 (the lowest rating,

being SD/D) to 20 (the highest rating, being AAA). Second, a credit outlook is assigned

to the numerical values, ranging from -1 (for a negative credit outlook) to 1 (for a positive

credit outlook). Then, the CCR measure is produced by adding the two numerical values ob-

tained from the first and second steps. The changes in CCR represent credit rating changes;

positive CCR changes indicate credit rating upgrades and negative changes indicate credit

rating downgrades.

We adopt the methodology proposed by Gande and Parsley (2005) and Ferreira and Gama

(2007) to study the effect of one country’s sovereign credit rating change on correlations

between U.S. and other countries’ stock returns during the U.S. subprime crisis. We pool

the data for all countries (j) excluding the event country (i) on each event day, and estimate

the following regression separately for upgrades and downgrades:

∆ |ρUS,j,t| = α+ β1Eventi,t + β2PriorEventi,t +∑k

βkXk + εij,t,∀j = i, (11)

where ρUS,j,t represents conditional correlations between U.S. and non-event country j stock

returns obtained from the DCC-GARCH model. Here, Eventi,t is defined as the value of

19

change in the CCR measure of event country i on the event day and PriorEventi,t is defined

as the net change in the CCR of a non-event country during the two weeks preceding the

event. Matrix X contains full sets of year and country dummies and the event and non-event

countries’ CCR values.

The most important advantage of this model specification is that we can control the

potential impact of the temporal clustering of events. This control is possible through the

use of short windows of two-days, instead of longer windows, such as 30 days, in measuring

the dependent variable as the change in correlation. Also, this model includes an additional

independent variable (PriorEventi,t) to control for the intensity of past events.


Table 6 reports the estimations of the regression model for cross-country spillover effects

on conditional correlations.16 We find evidence of spillover effects on conditional correlations

for both upgrades and downgrades of sovereign credit ratings during the U.S. subprime crisis.

However, the characteristics of the spillover effects for positive rating changes differ from

those for negative rating changes. While the spillovers for positive rating changes indicate

differential effects, those for negative rating changes indicate common information effects. If

the sovereign credit rating of one country is upgraded, other countries’ stock returns are less

correlated with U.S. stock returns. On the other hand, if a sovereign credit rating of one

country is downgraded, other countries’ stock returns are more correlated with U.S. stock

returns.

The spillover effects on conditional correlations during the U.S. subprime crisis are sta-

tistically significant at the 1% level for not only upgrades but also downgrades. In addition,

the estimated coefficients of β1 in Eq. (11) show that a one-notch drop in the CCR of one

country is associated with a 0.24 increase in correlations between U.S. and other countries’

16The empirical results are robust to currency effects. By investigating the influences of currency effectson this study, we also use local currency-denominated data instead of dollar-denominated data. The resultsare similar and qualitatively unchanged.

20

stock returns, whereas a one-notch rise in the CCR of one country is associated with a 0.28

decrease in correlations between U.S. and other countries’ stock returns.

The estimated coefficients of PriorEventi,t are significant for both upgrades and down-

grades. This result confirms that recent ratings activities can affect the spillover effect of

news about sovereign credit ratings across countries. Moreover, the estimated coefficients

of the event and non-event country CCRs are significant only for upgrades. This result

means that the higher the event and non-event country CCRs, the lower the correlations

between U.S. and non-event countries’ stock returns on positive news events. This suggests

that, during the U.S. subprime crisis, the positive news events of developed markets have

a greater impact on cross-country correlations and developed markets are easily influenced

by the positive news events of other countries. This finding is compatible with the result

in the Section 5.1, which reports that developed markets were more seriously affected than

emerging markets during the U.S. subprime crisis.

To summarize, we find evidence of spillover effects on conditional correlations during

the U.S. subprime crisis. The spillover effects are significant for both positive and negative

rating news. In addition, the spillovers for positive rating changes indicate differential effects

while those for negative rating changes indicate common information effects. These findings

suggest that not only negative rating news but also positive rating news was informative

during the U.S. subprime crisis. This means that positive news events as well as negative

news events were not anticipated by the market participants. These findings also imply

that the market participants tended to consider upgrades as local country information and

downgrades as common trend information, such as a wake-up call, during the U.S. subprime

crisis.

These findings are not consistent with previous literatures, which reports asymmetric

spillover effects: negative rating events abroad have a significant common information effect

while positive ratings events have no discernible impact as in the estimation result of Eq.

(11) during the East Asian crisis reported in Table 6. These inconsistent results reveal the

21

severity of the U.S. subprime crisis. Previous literature explains the asymmetric spillover

effect as being an incentive for the event country’s government to leak information about

positive ratings news and a reason for rating agencies to be reluctant to lower sovereign credit

ratings. However, there is a severe level of uncertainty about the future economy induced by

the high cost of gathering credible information during the U.S. subprime crisis. Thus, any

news about sovereign credit ratings, both positive and negative rating events, could have a

greater impact on the financial market in terms of the resolution of uncertainty.

6. Conclusions

This paper examines the contagion effects of the U.S. subprime crisis on international stock

markets. The main research questions of this paper are whether financial contagion ex-

ists during the U.S. subprime crisis and whether spillover effects of sovereign rating news

on cross-country correlations are significant during the U.S. subprime crisis. We employ

DCC-GARCH model to capture the dynamics of correlation because we confirm that the

correlation analysis widely used in the research of financial contagion has obvious limitations

in the study of any contagion effect during the U.S. subprime crisis.

We find evidence of financial contagion during the U.S. subprime crisis. The conditional

correlations significantly increase during the U.S. subprime crisis and these higher levels

persist for the remaining period of the U.S. subprime crisis. This finding indicates that there

is financial contagion in the early stages of the U.S. subprime crisis and then a transition to

herding behavior for the rest of the crisis. The evidence of a contagion effect is observed not

only in emerging markets but also in developed markets. The contagion effect of the U.S.

subprime crisis is more severe than that of the East Asian crisis.

We also show evidence of a spillover effect of news concerning sovereign credit ratings

during the U.S. subprime crisis. Positive as well as negative rating events have a significant

effect on the correlations of stock market returns. Moreover, the characteristics of spillover

22

effects for positive rating changes differ from those for negative rating changes. While the

spillover for positive rating changes indicates differential effect, that for negative rating

changes indicates common information effect.

Our findings provide some insights into the nature of the U.S. subprime crisis. These

findings imply that the U.S. subprime crisis has a larger surprise component than any previ-

ous crisis, such as the East Asian crisis. Also, the U.S. subprime crisis seems to support the

wake-up call hypothesis as a transmission channel of financial contagion. To explore these

issues further, one should study which factors explain the degree of contagion effect and

news spillover effect on international stock markets in financial crises using microeconomic

data related to each market as well as macroeconomic data. These studies will help us un-

derstand not only the channel or mechanism of financial contagion but also the vulnerability

of a financial system to financial contagion.

23

Appendix A. Construction of the CCR measure

Explicit credit rating (ECR) Credit outlook

Rating Value Information Add to ECR

AAA 20 Positive 1

AA+ 19 CW - Pos 0.5

AA 18 Stable/CW - Dev 0

AA- 17 CW- Neg −0.5

A+ 16 Negative −1

A 15

A- 14

BBB+ 13

BBB 12

BBB- 11

BB+ 10

BB 9

BB- 8

B+ 7

B 6

B- 5

CCC+ 4

CCC 3

CCC- 2

CC/C 1

SD/D 0

This table presents the construction of the CCR measure. Each country’s letter ratings are assigned to

the numerical values, ranging from 0 to 20 to obtain the explicit credit rating (ECR). A credit outlook is

also assigned to the numerical values, ranging from -1 to 1. Then, the CCR measure is produced by adding

the these two numerical values. For example, if a country is rated BBB+ with stable credit outlook, the

ECR and CCR is 13. If Standard & Poor’s now change the country’s credit outlook from stable to positive,

the ECR is still 13. However, its CCR is 14. Here CW-Pos, CW-Dev, and CW-Neg represent credit watch

- positive, credit watch - developing, and credit watch - negative, respectively.

24

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Table 1 Descriptive statistics for stock returns during the U.S. subprime crisis period. This tableshows the descriptive statistics on stock returns during the U.S. subprime crisis period. To adjustthe effects of time-zone differences between the locations of exchanges in different countries, wecalculate stock market returns as rolling-average, two-day [0, 1] returns. Panel A uses a sample ofthe entire period in the U.S. subprime crisis period, which covers from January 1, 2005 to August31, 2009, a total of 1213 observations. The entire period is divided into two sub-periods: tranquilperiod and turmoil period. Panel B uses only the sample of the tranquil period. Panel C uses onlythe sample of the turmoil period. The tranquil period in the U.S. subprime crisis period coversfrom January 1, 2005 to July 31, 2007, a total of 671 observations. The turmoil period in the U.S.subprime crisis period covers from August 1, 2007 to August 31, 2009, a total of 542 observations.Jarque-Bera and LB(10) refer to Jarque-Bera test and Ljung-Box Q-test statistics with up to 10-daylags, respectively. Under the null of normality, the Jarque-Bera test statistics follow a chi-squareddistribution with two degrees of freedom. Here *, **, and *** denote statistical significance at the10%, 5% and 1% levels, respectively.

Panel A: Entire period (from January 1, 2005 to August 31, 2009)

MeanStandarddeviation

Skewness Kurtosis Jarque-Bera LB(10)

Asia 0.013% 0.010 -0.478 7.954 1286.678*** 315.082***Australasia 0.019% 0.014 -0.579 8.901 1827.785*** 317.797***Europe 0.004% 0.011 -0.419 8.951 1825.483*** 304.684***Latin America 0.075% 0.014 -0.719 8.776 1790.987*** 338.627***Developed market 0.004% 0.010 -0.334 8.913 1789.405*** 342.548***Emerging market 0.043% 0.012 -0.826 8.976 1942.677*** 451.650***

China 0.086% 0.015 -0.109 4.835 172.656*** 322.973***Hong Kong 0.029% 0.012 -0.446 9.061 1896.679*** 301.551***Indonesia 0.049% 0.017 -0.922 11.743 4035.048*** 497.259***Japan -0.005% 0.010 0.020 7.267 920.294*** 187.694***Korea 0.027% 0.017 -0.489 15.033 7366.154*** 373.183***Malaysia 0.028% 0.008 -0.692 7.453 1098.881*** 385.404***Philippines 0.074% 0.012 -0.424 6.244 568.392*** 440.402***Singapore 0.043% 0.011 -0.379 7.388 1002.218*** 373.162***Taiwan 0.002% 0.012 0.158 6.580 652.732*** 396.637***Thailand 0.019% 0.013 -0.894 11.172 3536.860*** 368.202***Argentina 0.000% 0.011 -1.051 9.588 2417.219*** 441.720***Brazil 0.093% 0.018 -0.593 8.312 1497.235*** 313.448***Chile 0.046% 0.010 -0.396 10.727 3049.624*** 379.139***Mexico 0.052% 0.014 -0.478 8.649 1659.246*** 378.521***Venezuela 0.018% 0.015 1.167 33.186 46329.545*** 195.867***Austria 0.004% 0.014 -0.629 9.636 2305.987*** 342.350***Belgium -0.009% 0.012 -0.868 8.768 1833.921*** 360.498***Denmark 0.017% 0.013 -0.583 9.764 2380.993*** 339.026***Finland 0.006% 0.013 -0.141 6.430 598.802*** 285.851***France 0.010% 0.012 -0.219 9.114 1898.850*** 265.838***Germany 0.014% 0.011 -0.051 10.660 2966.374*** 294.156***Greece -0.001% 0.013 -0.555 8.236 1447.863*** 369.994***Ireland -0.045% 0.014 -0.705 7.459 1105.208*** 322.778***Italy -0.018% 0.012 -0.336 9.075 1888.045*** 302.144***Netherlands -0.001% 0.012 -0.795 9.179 2057.439*** 316.577***Norway 0.023% 0.018 -0.639 7.115 938.428*** 278.323***Poland 0.021% 0.016 -0.283 7.395 992.589*** 368.820***Portugal 0.006% 0.011 -0.385 11.848 3987.130*** 324.543***Spain 0.017% 0.011 -0.347 8.913 1791.427*** 298.015***Sweden 0.014% 0.015 0.022 7.346 954.558*** 254.578***Switzerland 0.018% 0.009 -0.315 8.873 1763.077*** 266.687***Turkey 0.039% 0.019 -0.455 5.960 484.552*** 353.145***U.K. -0.007% 0.012 -0.273 10.077 2546.605*** 268.242***Canada 0.026% 0.013 -0.712 10.845 3212.819*** 287.747***Australia 0.019% 0.014 -0.571 8.913 1832.936*** 315.130***New Zealand -0.017% 0.011 -0.664 7.206 983.402*** 337.896***South Africa 0.026% 0.016 -0.480 7.803 1212.220*** 312.349***U.S. -0.004% 0.010 -0.500 10.247 2704.544*** 189.208***

29

Table 1 (continued)

Panel B: Tranquil period (from January 1, 2005 to July 31, 2007)



Asia 0.064% 0.006 -0.491 4.718 109.557*** 166.265***Australasia 0.076% 0.007 -0.733 4.925 163.634*** 171.140***Europe 0.073% 0.006 -0.441 5.194 156.354*** 202.994***Latin America 0.148% 0.009 -0.626 4.528 109.090*** 206.972***Developed market 0.063% 0.005 -0.364 5.051 132.427*** 207.443***Emerging market 0.118% 0.007 -1.022 6.020 371.899*** 251.325***China 0.203% 0.012 -0.104 4.907 102.863*** 174.641***Hong Kong 0.080% 0.006 -0.548 4.069 65.561*** 163.223***Indonesia 0.111% 0.011 -0.845 7.172 566.507*** 177.262***Japan 0.035% 0.008 -0.228 4.265 50.552*** 150.172***Korea 0.118% 0.009 -0.522 4.030 60.095*** 179.271***Malaysia 0.081% 0.006 -0.671 7.950 735.489*** 242.618***Philippines 0.134% 0.009 -0.566 5.349 190.175*** 200.542***Singapore 0.101% 0.006 -0.852 5.792 299.088*** 168.209***Taiwan 0.044% 0.008 -0.658 4.732 132.283*** 187.319***Thailand 0.072% 0.010 -1.530 18.055 6598.993*** 166.015***Argentina 0.066% 0.009 -0.699 4.716 136.982*** 208.333***Brazil 0.179% 0.012 -0.520 3.988 57.476*** 192.086***Chile 0.091% 0.007 -0.567 4.166 73.874*** 195.143***Mexico 0.127% 0.009 -0.302 4.729 93.831*** 204.181***Venezuela 0.057% 0.020 0.991 21.126 9295.859*** 101.237***Austria 0.089% 0.007 -0.435 5.008 133.941*** 216.061***Belgium 0.063% 0.006 -0.387 4.869 114.340*** 199.034***Denmark 0.090% 0.007 -0.669 4.637 125.011*** 205.703***Finland 0.094% 0.008 -0.216 4.573 74.372*** 200.476***France 0.074% 0.006 -0.296 4.803 100.688*** 195.597***Germany 0.080% 0.006 -0.344 4.750 98.820*** 203.164***Greece 0.082% 0.008 -0.291 4.269 54.525*** 222.387***Ireland 0.051% 0.008 -0.563 5.390 195.182*** 201.518***Italy 0.048% 0.006 -0.284 4.143 45.564*** 187.759***Netherlands 0.078% 0.006 -0.270 4.669 85.983*** 203.135***Norway 0.118% 0.011 -0.713 4.304 104.422*** 182.225***Poland 0.122% 0.011 -0.249 3.725 21.622*** 219.815***Portugal 0.091% 0.005 -0.073 3.791 18.073*** 210.518***Spain 0.080% 0.006 -0.185 4.168 41.958*** 187.732***Sweden 0.084% 0.008 -0.574 5.569 221.341*** 162.186***Switzerland 0.067% 0.006 -0.122 4.703 82.744*** 195.496***Turkey 0.106% 0.016 -0.723 4.910 160.511*** 201.919***U.K. 0.057% 0.006 -0.334 4.800 103.104*** 176.783***Canada 0.080% 0.007 -0.549 3.699 47.414*** 197.806***Australia 0.075% 0.007 -0.751 4.879 161.897*** 165.532***New Zealand 0.040% 0.006 -0.654 4.767 135.086*** 204.169***South Africa 0.072% 0.012 -0.652 4.813 139.464*** 171.773***U.S. 0.038% 0.005 -0.278 4.102 42.631*** 153.288***

30

Table 1 (continued)

Panel C: Turmoil period (from August 1, 2007 to August 31, 2009)



Asia -0.049% 0.013 -0.297 5.741 177.664*** 141.189***Australasia -0.05% 0.019 -0.350 5.264 126.779*** 142.035***Europe -0.081% 0.015 -0.189 5.463 140.176*** 130.232***Latin America -0.015% 0.018 -0.522 6.442 292.162*** 145.562***Developed market -0.068% 0.013 -0.131 5.659 161.269*** 149.036***Emerging market -0.049% 0.015 -0.526 6.036 233.190*** 199.952***China -0.060% 0.017 0.033 4.073 26.101*** 142.960***Hong Kong -0.035% 0.017 -0.243 5.390 134.333*** 134.588***Indonesia -0.028% 0.022 -0.723 8.537 739.740*** 251.077***Japan -0.055% 0.013 0.160 6.162 228.175*** 68.498***Korea -0.086% 0.023 -0.266 9.518 965.811*** 169.856***Malaysia -0.039% 0.010 -0.472 5.291 138.701*** 161.526***Philippines 0.000% 0.015 -0.252 5.116 106.847*** 212.762***Singapore -0.029% 0.014 -0.140 4.812 75.906*** 172.279***Taiwan -0.051% 0.015 0.341 4.781 82.141*** 184.634***Thailand -0.047% 0.015 -0.517 7.595 501.001*** 180.975***Argentina -0.082% 0.013 -0.996 8.915 879.702*** 212.770***Brazil -0.012% 0.023 -0.427 6.305 263.174*** 134.474***Chile -0.011% 0.014 -0.214 7.631 488.417*** 172.609***Mexico -0.041% 0.017 -0.340 6.688 317.567*** 169.626***Venezuela -0.031% 0.006 -2.676 33.490 21641.977*** 173.582***Austria -0.102% 0.018 -0.379 6.013 217.990*** 147.809***Belgium -0.099% 0.016 -0.570 5.149 133.647*** 160.107***Denmark -0.073% 0.017 -0.332 6.152 234.343*** 147.686***Finland -0.102% 0.018 0.039 4.238 34.734*** 118.754***France -0.069% 0.016 -0.042 5.693 163.915*** 110.956***Germany -0.067% 0.015 0.119 6.920 348.373*** 123.917***Greece -0.102% 0.017 -0.369 5.788 187.794*** 160.145***Ireland -0.164% 0.019 -0.418 4.498 66.415*** 139.143***Italy -0.101% 0.016 -0.135 5.374 128.956*** 131.563***Netherlands -0.099% 0.017 -0.501 5.314 143.628*** 136.912***Norway -0.095% 0.023 -0.405 4.806 88.448*** 117.726***Poland -0.104% 0.021 -0.124 5.648 159.772*** 159.281***Portugal -0.099% 0.015 -0.129 6.980 359.316*** 139.238***Spain -0.060% 0.015 -0.165 5.600 155.083*** 128.805***Sweden -0.074% 0.021 0.169 4.562 57.709*** 109.970***Switzerland -0.044% 0.012 -0.168 6.048 212.312*** 109.534***Turkey -0.045% 0.023 -0.259 5.359 131.785*** 154.726***U.K. -0.085% 0.017 -0.085 5.886 188.720*** 115.984***Canada -0.041% 0.018 -0.478 6.592 311.997*** 123.085***Australia -0.049% 0.019 -0.344 5.273 127.349*** 141.447***New Zealand -0.088% 0.014 -0.431 4.755 86.346*** 146.445***South Africa -0.032% 0.020 -0.329 6.393 269.760*** 139.704***U.S. -0.056% 0.014 -0.294 5.775 181.726*** 79.650***

31

Table 2 Unconditional correlations during the U.S. subprime crisis period. This tablepresents unconditional correlations between international stock markets over the entire pe-riod in the U.S. subprime crisis period. The unconditional correlations are calculated byusing weekly returns. The U.S. subprime crisis period covers from January 1, 2005 to Au-gust 31, 2009, a total of 242 observations. Panel A uses region-level stock index returns.Panel B uses developed and emerging market stock returns. And Panel C uses country-levelstock index returns. Here CH, HK, ID, JP, KO, MY, PH, SG, TA, TH, AR ,BR, CL, MX,VE, OE, BG, DK, FN, FR, BD, GR, IR, IT, NL, NW, PO, PT, ES, SD, SW, TK, UK,CN, AU, NZ, SA, and US represent the stock index returns of China, Hong Kong, Indone-sia, Japan, Korea, Malaysia, Philippines, Singapore, Taiwan, Thailand, Argentina, Brazil,Chile, Mexico, Venezuela, Austria, Belgium, Denmark, Finland, France, Germany, Greece,Ireland, Italy, Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, Turkey,the United Kingdom, Canada, Australia, New Zealand, South Africa, and the United States,respectively.

Panel A: Region-level stock index returnsAsia Australasia Europe Latin America US

Asia 1 0.841 0.800 0.737 0.631Australasia 1 0.856 0.778 0.698Europe 1 0.857 0.798Latin America 1 0.769US 1

Panel B: Developed and emerging market stock returnsDeveloped market Emerging market US

Developed market 1 0.920 0.782Emerging market 1 0.724US 1

32

Tab

le2(con

tinued)

Pan

elC:Cou

ntry-level

stock

index

returns

CH

HK

IDJP

KO

MY

PH

SG

TA

TH

AR

BR

CL

MX

VE

OE

BG

DK

FN

FR

BD

GR

IRIT

NL

NW

PO

PT

ES

SD

SW

TK

UK

CN

AU

NZ

SA

US

CH

10.46

0.41

0.25

0.32

0.43

0.38

0.41

0.36

0.28

0.24

0.34

0.36

0.28

0.06

0.34

0.33

0.32

0.22

0.29

0.30

0.37

0.31

0.31

0.32

0.29

0.27

0.31

0.27

0.26

0.24

0.28

0.32

0.26

0.35

0.34

0.31

0.24

HK

10.76

0.67

0.77

0.73

0.69

0.88

0.75

0.68

0.59

0.67

0.60

0.68

0.07

0.75

0.68

0.71

0.61

0.69

0.65

0.71

0.69

0.70

0.72

0.66

0.59

0.67

0.69

0.67

0.62

0.61

0.73

0.65

0.78

0.68

0.65

0.64

ID1

0.65

0.73

0.67

0.66

0.81

0.65

0.70

0.50

0.64

0.58

0.61

0.04

0.70

0.64

0.71

0.60

0.61

0.57

0.61

0.59

0.63

0.69

0.65

0.51

0.62

0.60

0.64

0.59

0.53

0.60

0.61

0.75

0.63

0.58

0.52

JP

10.73

0.57

0.56

0.72

0.61

0.63

0.53

0.57

0.54

0.58

0.03

0.68

0.62

0.68

0.57

0.66

0.62

0.62

0.58

0.66

0.66

0.65

0.53

0.64

0.64

0.59

0.67

0.55

0.59

0.58

0.73

0.66

0.60

0.51

KO

10.63

0.63

0.80

0.72

0.69

0.54

0.61

0.57

0.64

0.04

0.73

0.65

0.70

0.58

0.64

0.61

0.66

0.62

0.66

0.68

0.62

0.53

0.67

0.61

0.65

0.59

0.61

0.62

0.57

0.75

0.70

0.63

0.52

MY

10.72

0.79

0.63

0.66

0.59

0.51

0.51

0.51

0.06

0.63

0.61

0.60

0.54

0.61

0.56

0.64

0.56

0.64

0.63

0.58

0.51

0.59

0.64

0.56

0.55

0.55

0.59

0.55

0.69

0.62

0.53

0.47

PH

10.72

0.61

0.62

0.56

0.55

0.52

0.52

0.10

0.61

0.61

0.62

0.55

0.59

0.54

0.63

0.58

0.59

0.64

0.56

0.51

0.55

0.60

0.60

0.56

0.58

0.60

0.55

0.65

0.60

0.51

0.51

SG

10.77

0.73

0.62

0.69

0.62

0.71

0.05

0.82

0.76

0.77

0.69

0.76

0.73

0.78

0.73

0.78

0.81

0.72

0.68

0.73

0.76

0.74

0.69

0.66

0.77

0.72

0.83

0.72

0.72

0.66

TA

10.63

0.50

0.54

0.50

0.56

0.02

0.67

0.63

0.65

0.59

0.63

0.59

0.63

0.63

0.62

0.67

0.59

0.50

0.62

0.62

0.62

0.55

0.53

0.63

0.59

0.71

0.60

0.56

0.54

TH

10.54

0.58

0.59

0.56

0.08

0.67

0.62

0.68

0.57

0.61

0.56

0.59

0.58

0.63

0.64

0.66

0.50

0.61

0.61

0.62

0.59

0.55

0.59

0.60

0.71

0.62

0.54

0.48

AR

10.55

0.54

0.57

0.08

0.58

0.56

0.60

0.54

0.61

0.57

0.58

0.55

0.66

0.62

0.54

0.47

0.55

0.62

0.52

0.57

0.55

0.58

0.55

0.58

0.53

0.52

0.50

BR

10.72

0.82

0.07

0.77

0.69

0.78

0.75

0.78

0.79

0.69

0.64

0.75

0.77

0.74

0.67

0.74

0.71

0.75

0.69

0.72

0.79

0.80

0.74

0.65

0.77

0.73

CL

10.70

0.02

0.64

0.67

0.69

0.61

0.66

0.65

0.59

0.60

0.66

0.69

0.57

0.50

0.69

0.61

0.63

0.61

0.56

0.65

0.63

0.68

0.62

0.58

0.59

MX

10.09

0.73

0.68

0.73

0.70

0.75

0.77

0.66

0.66

0.74

0.75

0.61

0.67

0.72

0.71

0.72

0.69

0.67

0.74

0.71

0.70

0.62

0.72

0.76

VE

10.07

0.07

0.05

0.06

0.06

0.07

0.07

0.04

0.08

0.03

0.02

0.01

0.05

0.09

0.07

0.06

0.10

0.04

0.05

0.03

0.05

0.03

0.06

OE

10.86

0.88

0.83

0.90

0.87

0.85

0.82

0.90

0.91

0.85

0.78

0.84

0.87

0.86

0.82

0.73

0.87

0.81

0.82

0.74

0.77

0.71

BG

10.83

0.77

0.89

0.85

0.82

0.82

0.85

0.91

0.75

0.71

0.80

0.85

0.84

0.83

0.68

0.86

0.77

0.77

0.70

0.73

0.71

DK

10.84

0.87

0.82

0.78

0.78

0.87

0.89

0.84

0.64

0.82

0.83

0.87

0.83

0.66

0.86

0.80

0.82

0.73

0.74

0.69

FN

10.88

0.87

0.77

0.73

0.86

0.87

0.78

0.69

0.76

0.84

0.87

0.80

0.63

0.83

0.81

0.76

0.69

0.71

0.73

FR

10.96

0.85

0.82

0.95

0.94

0.81

0.75

0.85

0.94

0.90

0.90

0.72

0.92

0.83

0.82

0.76

0.80

0.78

BD

10.83

0.78

0.91

0.91

0.77

0.77

0.84

0.91

0.87

0.86

0.71

0.89

0.81

0.77

0.71

0.81

0.77

GR

10.74

0.83

0.85

0.75

0.76

0.78

0.82

0.79

0.80

0.72

0.81

0.75

0.77

0.71

0.77

0.62

IR1

0.80

0.82

0.67

0.62

0.74

0.78

0.79

0.74

0.61

0.85

0.71

0.72

0.64

0.64

0.71

IT1

0.93

0.81

0.73

0.83

0.92

0.86

0.86

0.69

0.90

0.80

0.83

0.76

0.79

0.74

NL

10.83

0.75

0.82

0.91

0.90

0.86

0.72

0.92

0.85

0.84

0.77

0.79

0.78

NW

10.70

0.73

0.78

0.80

0.75

0.66

0.80

0.83

0.80

0.69

0.74

0.64

PO

10.68

0.75

0.71

0.69

0.71

0.73

0.69

0.65

0.59

0.75

0.61

PT

10.83

0.78

0.76

0.63

0.78

0.72

0.76

0.71

0.72

0.65

ES

10.85

0.84

0.70

0.87

0.77

0.78

0.72

0.75

0.72

SD

10.84

0.68

0.88

0.80

0.78

0.71

0.75

0.76

SW

10.66

0.84

0.75

0.78

0.71

0.75

0.71

TK

10.72

0.64

0.64

0.61

0.73

0.62

UK

10.86

0.80

0.72

0.79

0.80

CN

10.79

0.67

0.75

0.82

AU

10.85

0.74

0.70

NZ

10.68

0.60

SA

10.64

US

1

33

Table 3 Correlation analysis. This table presents the results of tests of an increase incorrelation coefficients during the U.S. subprime crisis and the East Asian crisis after takinginto account heteroskedasticity bias of correlation coefficient. All stock index returns arecalculated as weekly returns. The adjusted correlation coefficient is calculated as proposedby Forbes and Rigobon (2002). The Z-statistics are calculated as proposed by Morrison(1983). The tranquil period in the U.S. subprime crisis period covers from January 1, 2005to July 31, 2007, a total of 134 observations. The turmoil period in the U.S. subprime crisisperiod covers from August 1, 2007 to August 31, 2009, a total of 108 observations. Thetranquil period in the East Asian crisis period covers from January 1, 1996 to July 1, 1997,a total of 78 observations. The turmoil period in the East Asian crisis period covers fromJuly 2, 1997 to December 31, 2003, a total of 339 observations. We assume that the UnitedStates is the source country of financial contagion in the U.S. subprime crisis period and thatThailand is the source country of financial contagion in the East Asian crisis period. PanelA uses region-level stock index returns. Panel B uses developed and emerging market stockreturns. And Panel C uses country-level stock index returns. Here CH, HK, ID, JP, KO,MY, PH, SG, TA, TH, AR ,BR, CL, MX, VE, OE, BG, DK, FN, FR, BD, GR, IR, IT, NL,NW, PO, PT, ES, SD, SW, TK, UK, CN, AU, NZ, SA, and US represent the stock indexreturns of China, Hong Kong, Indonesia, Japan, Korea, Malaysia, Philippines, Singapore,Taiwan, Thailand, Argentina, Brazil, Chile, Mexico, Venezuela, Austria, Belgium, Denmark,Finland, France, Germany, Greece, Ireland, Italy, Netherlands, Norway, Poland, Portugal,Spain, Sweden, Switzerland, Turkey, the United Kingdom, Canada, Australia, New Zealand,South Africa, and the United States, respectively. Here *, **, and *** denote statisticalsignificance at the 10%, 5% and 1% levels, respectively.

34

Table 3 (continued)

Panel A: Region-level stock index returnsU.S. subprime crisis

Correlationduring the

tranquil period


turmoil period

Adjustedunconditionalcorrelation

Z-statistics(unadjusted)

Z-statistics(adjusted)

US as the sourceAsia 0.491 0.665 0.293 -1.321*** 1.518Australasia 0.433 0.744 0.357 -2.374*** 0.574Europe 0.643 0.826 0.450 -1.394*** 1.476Latin America 0.651 0.803 0.420 -1.155*** 1.765

East Asian crisisCorrelationduring the

tranquil period


turmoil period




TH as the sourceAsia 0.112 0.417 0.329 -2.388*** -1.703**Australasia 0.184 0.417 0.329 -1.822*** -1.137**Europe 0.070 0.284 0.220 -1.677*** -1.174**Latin America 0.094 0.320 0.249 -1.765*** -1.209**

Panel B: Developed and emerging market stock returnsU.S. subprime crisis


tranquil period


turmoil period




US as the sourceDevelopedmarket

0.627 0.811 0.431 -1.405* 1.500

Emergingmarket

0.583 0.756 0.370 -1.322* 1.631

East Asian crisisCorrelationduring the

tranquil period


turmoil period




TH as the sourceDevelopedmarket

0.067 0.355 0.277 -2.256** -1.650**

Emergingmarket

0.353 0.602 0.498 -1.950** -1.131**

35

Table 3 (continued)

Panel C: Country-level stock index returns

U.S. subprime crisis


tranquil period


turmoil period




US as the sourceCH 0.097 0.273 0.097 -1.347*** -0.002HK 0.513 0.656 0.287 -1.094*** -1.728ID 0.395 0.550 0.221 -1.182*** -1.330JP 0.368 0.563 0.228 -1.486*** -1.068KO 0.480 0.520 0.205 -0.310*** -2.096MY 0.423 0.492 0.191 -0.525*** -1.773PH 0.472 0.534 0.212 -0.473*** -1.981SG 0.461 0.696 0.316 -1.792*** -1.106TA 0.423 0.562 0.228 -1.066*** -1.489TH 0.318 0.544 0.217 -1.724*** -0.766AR 0.544 0.497 0.193 -0.360*** -2.678BR 0.552 0.781 0.395 -1.749*** -1.196CL 0.477 0.608 0.255 -1.003*** -1.695MX 0.710 0.789 0.405 -0.606*** -2.332VE 0.183 -0.014 -0.005 -1.502*** -1.433OE 0.542 0.740 0.354 -1.517*** -1.430BG 0.580 0.728 0.343 -1.132*** -1.806DK 0.527 0.724 0.340 -1.501*** -1.433FN 0.590 0.752 0.366 -1.242*** -1.710FR 0.653 0.810 0.430 -1.198*** -1.709BD 0.642 0.791 0.406 -1.139*** -1.798GR 0.419 0.667 0.294 -1.894*** -0.951IR 0.510 0.743 0.356 -1.776*** -1.172IT 0.610 0.762 0.376 -1.161*** -1.791NL 0.675 0.800 0.417 -0.951*** -1.974NW 0.329 0.712 0.330 -2.925*** -0.004PO 0.356 0.670 0.297 -2.400*** -0.451PT 0.472 0.669 0.296 -1.502*** -1.346ES 0.602 0.741 0.355 -1.056*** -1.891SD 0.598 0.789 0.404 -1.456*** -1.482SW 0.562 0.753 0.366 -1.462*** -1.490TK 0.420 0.708 0.326 -2.198*** -0.717UK 0.636 0.825 0.449 -1.440*** -1.431CN 0.626 0.850 0.485 -1.711*** -1.073AU 0.438 0.743 0.357 -2.330*** -0.618NZ 0.279 0.681 0.305 -3.067*** -0.195SA 0.467 0.708 0.326 -1.837*** -1.079

36

Table 3 (continued)

East Asian crisis


tranquil period


turmoil period




TH as the sourceCH 0.090 0.004 0.003 -0.672*** -0.680***HK 0.203 0.470 0.375 -2.086*** -1.346***ID 0.326 0.459 0.366 -1.038*** -0.308***JP 0.019 0.260 0.200 -1.885*** -1.421***KO 0.131 0.387 0.304 -2.001*** -1.353***MY 0.230 0.471 0.376 -1.886*** -1.144***PH 0.171 0.538 0.436 -2.875*** -2.082***SG 0.187 0.589 0.485 -3.145*** -2.330***TA -0.060 0.406 0.320 -3.650*** -2.978***AR 0.094 0.265 0.204 -1.337*** -0.865***BR -0.096 0.227 0.175 -2.533*** -2.122***CL 0.152 0.315 0.245 -1.279*** -0.729***MX 0.281 0.295 0.228 -0.108*** -0.411***VE 0.066 0.134 0.102 -0.531*** -0.283***OE 0.048 0.198 0.152 -1.180*** -0.818***BG 0.120 0.114 0.087 -0.048*** -0.260***DK -0.009 0.151 0.115 -1.250*** -0.971***FN 0.052 0.191 0.146 -1.085*** -0.736***FR 0.042 0.237 0.183 -1.527*** -1.099***BD -0.096 0.284 0.219 -2.971*** -2.469***GR 0.065 0.190 0.145 -0.980*** -0.633***IR 0.080 0.256 0.197 -1.374*** -0.916***IT 0.141 0.237 0.183 -0.751*** -0.323***NL -0.002 0.244 0.188 -1.927*** -1.489***NW 0.125 0.265 0.205 -1.094*** -0.621***PO 0.104 0.291 0.226 -1.471*** -0.957***PT -0.214 0.114 0.087 -2.564*** -2.353***ES 0.094 0.271 0.209 -1.386*** -0.904***SD -0.006 0.240 0.185 -1.923*** -1.491***SW -0.088 0.198 0.152 -2.243*** -1.882***TK 0.271 0.195 0.150 -0.591*** -0.947***UK 0.107 0.267 0.207 -1.256*** -0.779***CN 0.185 0.323 0.251 -1.085*** -0.524***AU 0.175 0.409 0.322 -1.825*** -1.150***NZ 0.151 0.386 0.303 -1.834*** -1.187***SA 0.122 0.385 0.303 -2.059*** -1.412***US 0.062 0.284 0.220 -1.742*** -1.239***

37

Table 4 Estimation results of DCC-GARCH model. This table tabulates the estimatedcoefficients of the DCC-GARCH model explained in Section 4. The model specifications ofthe mean and variance equation are as follows:

rt = γ0 + γ1rt−1 + γ2rU.S.t−1 + εt, hii,t = ϖi + αiε

2i,t−1 + βihii,t−1.

All stock index returns are calculated as weekly returns. We estimate the DCC-GARCHmodel over two different periods; one including the U.S. subprime crisis and another includingthe East Asian crisis. The U.S. subprime crisis period covers from January 1, 2005 to August31, 2009, a total of 242 observations. The East Asian crisis period covers from January 1,1996 to December 31, 2003, a total of 417 observations. Panel A uses region-level stockindex returns. Panel B uses developed and emerging market stock returns. In Panel A, theparameters of the conditional correlation processes are a = 0.016 and b = 0.909 (duringthe U.S. subprime crisis period) and a = 0.006 and b = 0.557 (during the East Asian crisisperiod), respectively. In Panel B, the parameters of the conditional correlation processesare a = 0.025 and b = 0.918 (during the U.S. subprime crisis period) and a = 0.010 and b= 0.982 (during the East Asian crisis period), respectively. The t-statistics are given belowin parentheses. Here *, **, and *** denote statistical significance at the 10%, 5% and 1%levels, respectively.

38

Table 4 (continued)


Mean equations Variance equationsγ0 γ1 γ2 ω α β

Asia-0.001 *(0.503)

-0.239***(-3.007)

0.443***(4.880)

3.57E-05(1.476)

0.218***(4.346)

0.754***(11.295)

Australasia-0.001(0.518)

-0.248***(-2.817)

0.503***(3.761)

4.15E-05(1.367)

0.224***(4.636)

0.769***(12.481)

Europe-0.001(0.234)

-0.297***(-2.794)

0.457***(3.488)

3.28E-05**(2.068)

0.276***(4.000)

0.724***(11.853)

Latin America-0.005*(1.739)

-0.200**(-1.991)

0.335**(2.155)

1.05E-04*(1.883)

0.322***(2.762)

0.667***(5.478)

U.S.-1.59E-04(-0.088)

-0.054(0.831)

2.61E-05**(2.245)

0.294***(4.278)

0.706***(8.710)

East Asian crisisMean equations Variance equations

γ0 γ1 γ2 ω α β

Asia-0.001(-0.537)

-0.008(-0.157)

0.108**(2.110)

1.02E-05(1.211)

0.060***(2.933)

0.929***(42.693)

Australasia-0.001(1.081)

-0.055(-0.976)

0.134***(2.971)

1.32E-04(1.111)

0.080*(1.839)

0.660**(2.404)

Europe-0.001(1.258)

-0.125*(-1.842)

0.027(0.456)

2.24E-05*(1.773)

0.249***(6.220)

0.745***(19.825)

Latin America-7.08E-05(0.042)

-0.001(0.021)

0.142**(2.015)

8.72E-05***(4.413)

0.138***(3.836)

0.793***(18.331)

U.S.-0.001(1.076)

-0.063(-1.293)

9.24E-05**(2.209)

0.120***(4.292)

0.767***(11.648)



Developed market-4.69E-04(0.242)

-0.282***(-2.785)

0.429***(3.833)

2.71E-05*(1.781)

0.251***(4.003)

0.741***(11.488)

Emerging market-0.003(1.171)

-0.213**(-2.333)

0.481***(3.709)

6.72E-05*(1.851)

0.260***(4.129)

0.714***(8.973)

U.S.-1.59E-04(-0.088)

-0.054(0.831)

2.61E-05**(2.245)

0.294***(4.278)

0.706***(8.710)

East Asian crisis


Developed market4.53E-04(0.417)

-0.087(-1.314)

0.048(0.925)

4.55E-05*(1.713)

0.201***(4.305)

0.722***(9.892)

Emerging market1.93E-04(0.154)

-0.136**(2.334)

0.053(0.990)

3.45E-05**(2.094)

0.093***(3.218)

0.857***(18.875)

U.S.0.001(1.076)

-0.063(-1.293)

9.24E-05**(2.210)

0.120***(4.292)

0.767***(11.647)

39

Table 5 Test of structural shifts in conditional correlations. This table presents the esti-mated coefficients of Eq. (10) using conditional correlations obtained in the DCC-GARCHmodel. The model specifications of the mean and variance equation of Eq. (10) are asfollows:

ρij,t = φ0 + φ1ρij,t−1 + dijDt + eij,t, hρij,t = ϖρ

ij + αρije

2ij,t−1 + βρ

ijhρij,t−1.

We estimate Eq. (10) over two different periods: one including the U.S. subprime crisis andanother including the East Asian crisis. The U.S. subprime crisis period covers from January1, 2005 to August 31, 2009. The East Asian crisis period covers from January 1, 1996 toDecember 31, 2003. Panel A uses region-level stock index returns. Panel B uses developedand emerging market stock returns. The t-statistics are given below in parentheses. Here *,**, and *** denote statistical significance at the 10%, 5% and 1% levels, respectively.

40

Table 5 (continued)


Mean equations Variance equationsφ0 φ1 d ω α β

Asia0.060***(3.192)

0.899***(28.986)

0.004**(2.505)

2.42E-05*(1.618)

0.085*(1.876)

0.694***(4.090)

Australasia0.050**(2.514)

0.910***(25.762)

0.005**(2.460)

- - -

Europe0.053**(2.291)

0.924***(27.635)

0.002(1.589)

- - -

Latin America0.060*(1.947)

0.918***(21.512)

0.002(1.472)

- - -

East Asian crisisMean equations Variance equations

φ0 φ1 d ω α β

Asia0.205***(7.912)

0.517***(8.467)

0.001(1.289)

7.97E-06***(9.247)

0.258***(7.176)

0.520***(13.448)

Australasia0.216***(8.407)

0.569***(11.061)

0.001(0.696)

1.71E-05***(3.626)

0.104***(3.405)

-

Europe0.295***(7.703)

0.547***(9.310)

0.001(1.442)

8.52E-06***(4.973)

0.114***(5.048)

-

Latin America0.264***(6.676)

0.527***(7.437)

3.71E-04(0.487)

1.22E-05***(7.939)

0.247***(8.020)

-



Developed market0.047***(2.786)

0.931***(37.616)

0.005*(1.928)

- - -

Emerging market0.050*(1.883)

0.924***(23.166)

0.004(1.524)

3.58E-05***(3.219)

0.135***(3.0450)

0.741***(10.945)

East Asian crisis


Developed market0.008**(2.178)

0.986***(168.396)

0.001*(1.731)

2.71E-05***(14.137)

0.249***(7.985)

-

Emerging market0.014***(2.986)

0.973***(117.937)

0.002***(2.700)

4.00E-06***(2.998)

0.076***(3.801)

0.841***(19.444)

41

Table 6 News spillover effects on conditional correlation. This table presents the estimatedcoefficients of Eq. (11) for cross-country spillover effects of news concerning sovereign creditratings on conditional correlations between U.S. and other international stock returns. Eventis the value of change in the CCR of the event country on the event day. Prior Event is thenet change in the CCR of a non-event country during the two weeks preceding the event.Matrix X contains full sets of year and country dummies and event and non-event countryCCRs. We estimate Eq. (11) over two different periods: one including the U.S. subprimecrisis and another including the East Asian crisis. The U.S. subprime crisis period coversfrom January 1, 2005 to February 28, 2009. The East Asian crisis period covers from January1, 1996 to December 31, 2003. Here *, **, and *** denote statistical significance at the 10%,5% and 1% levels, respectively. All t-statistics (t-stat) are heteroskedasticity-robust usingthe White correction.

U.S. subprime crisis

Upgrades DowngradesCoeff t-stat Coeff t-stat

Constant -0.802 -4.337*** -0.490 -2.254**Event -0.279 -3.623*** -0.240 -5.901***Prior Event -0.098 -6.107*** -0.008 -1.938*CCR (event country) -0.019 -2.569** -0.015 -1.601CCR (non-event country) -0.027 -3.024*** -0.003 -0.390Year dummies Yes YesEvent country dummies Yes YesNon-event country dummies Yes YesAdjusted R2 0.341 0.265Number of observations 756 756

East Asian crisis

Upgrades DowngradesCoeff t-stat Coeff t-stat

Constant -0.066 -2.147** -0.073 -1.879*Event -0.003 -0.996 -0.012 -3.313***Prior Event -0.002 -1.319 -0.006 -4.804***CCR (event country) -0.005 -3.775*** -0.003 -2.319**CCR (non-event country) -4.52E-04 -0.438 -4.14E-04 -0.309Year dummies Yes YesEvent country dummies Yes YesNon-event country dummies Yes YesAdjusted R2 0.114 0.156Number of observations 2808 2844

42

Jul07-0.1

0

0.1Asia

Jul07-0.1

0

0.1Australasia

Jul07-0.1

0

0.1Europe

Jul07-0.1

0

0.1Latin America

Jul07-0.1

0

0.1US

Figure 1 Region-level stock index returns (from January 1, 2005 to August 31, 2009).This figure shows region-level stock index returns over the period from January 1, 2005 toAugust 31, 2009. To adjust for the effects of time-zone differences between the locations ofexchanges in different countries, we calculate stock market returns as rolling-average, two-day [0, 1] returns. The vertical line in each panel indicates the start date of the turmoilperiod, August 1, 2007.

43

Graph A

Jan06 Jan07 Jan08 Jan090.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Asia

Australasia

Europe

Latin America

Jan06 Jan07 Jan08 Jan090.5

0.55

0.6

0.65

0.7

0.75

0.8

0.85

0.9

Dev.Mkts.Ex-NA

Emerging Markets

Graph B

Jan98 Jan00 Jan020.35

0.4

0.45

0.5

0.55

0.6

0.65

0.7

Asia

Australasia

Europe

Latin America

Jan98 Jan00 Jan020.45

0.5

0.55

0.6

0.65

0.7

0.75

Dev.Mkts.Ex-NA

Emerging Markets

Figure 2 Conditional correlations between U.S. and other international stock returns. Thisfigure shows conditional correlations between U.S. and other international stock returnsobtained from an estimation of the DCC-GARCH model. When the DCC-GARCH modelis estimated, weekly returns are employed to adjust for the effects of time-zone differences.Graph A shows conditional correlations during the U.S. subprime crisis period. The verticalline in Graph A indicates the start date of the turmoil period, August 1, 2007. GraphB shows conditional correlations during the East Asian crisis period. The vertical line inGraph B indicates the start date of the turmoil period, July 2, 1997. The first graphs inGraphs A and B present conditional correlations between U.S. and region-level stock returns.The second graphs in Graphs A and B present conditional correlations between U.S. anddeveloped/emerging market stock returns.

44

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