Are Global Systematic Risk and Country-Specific Idiosyncratic Risk Priced in the
Integrated World Markets?
Abstract
Empirical evidence showing significant effects of local factors on international equity returns
while failing to find significant effects from global systematic risk seems counter-intuitive in
today’s integrated world markets. This paper uses the conditional second moments estimated
from an asymmetric dynamic conditional correlation model to measure the time-varying world
beta and country-specific idiosyncratic risks, and tests the relationship between country-level
index returns and world beta risk conditioned on positive and negative world market returns.
The results show that the conditional dynamic world beta risks significantly predict the crosscountry variation in expected index returns, while country-specific risk is not significantly
priced.
JEL Classification: G11, G12, G15
Keywords:
World beta risk; Country-specific idiosyncratic risk; Dynamic conditional
correlation model.
i
1.
Introduction
Most would agree that financial markets have become increasingly global in the past decades.
The recent financial crisis provides evidence of the strong interlinkages among international
capital markets. Therefore, one would presume that globalization has led to national stock
markets moving more closely together and that internationally traded capital assets should be
globally priced.
Nonetheless, a large amount of academic research devotes itself to
recognizing whether or not the world market risk and country-specific idiosyncratic risk are
priced and continues to point to the significance of the effects of local factors while failing to
find significant effects from global systematic risk [e.g., Cumby and Glen (1990), Ferson and
Harvey (1994), Harvey and Zhou (1993), and Karolyi and Stulz (2003)].
The empirical evidence is summarized well in a recent study by Bali and Cakici (2010),
who provide a general cross-sectional test of global capital market integration in an
international capital asset pricing model (ICAPM). Using 37 country-level index data and a
global market risk factor, they show that there is a positive and significant relationship
between expected index returns and country-specific idiosyncratic risk, but the relationship
between a global-wide systematic risk and individual country's expected returns is flat. They
conclude that the "finding that the differences in countries' stock market returns can be
explained by the differences in country-specific risks is . . . consistent with the view that global
stock markets are not fully integrated."
This conclusion has important implication to
international investment because it shows that substantial risk-reduction can be created from
diversification.
1
The purpose of this paper is to challenge the above findings and provide more accurate
evidence on global capital market integration. The contribution of the paper is twofold. First,
on the observed weak global systematic effect, this paper argues that the literature on
conditional risk-return relationship stimulated by Pettengill et al. (1995) provides a possible
explanation.
It states that the relationship between market beta and realized returns is
conditional on the market return. In up markets, high-beta securities should be rewarded for
bearing risk with higher returns than low-beta securities, but in down markets high-beta
securities experience lower returns than low-beta securities. Thus, it is necessary to partition
the data into up market and down market periods based on the sign of the realized market
excess return. Empirical studies using data from various countries mostly confirm a significant
direct relationship between beta and returns in up markets and a significant inverse relationship
between beta and returns in down markets. Among them Fletcher (2000) and Tang and Shum
(2003) are of particular interest in that they use country-level index data and find a significant
conditional relationship between index returns and a world market beta. However, Fletcher
uses a CAPM assuming full integration and does not discuss the country-specific idiosyncratic
risk, while Tang and Shum only consider exchange rate risk as the country-specific risk. This
paper uses a partial integration ICAPM and considers both world beta risk and country-specific
idiosyncratic risk.
The second contribution of the paper is on the significance of the effects of countryspecific risk on international equity index returns, which is seldom challenged in the literature.
One possible challenge worth exploring is the validity of the measure of idiosyncratic risk.
This issue has been raised in the literature of firm-level idiosyncratic risk, where monthly
idiosyncratic volatility is often measured by realized volatility in the previous month using
2
daily data. 1 Spiegel and Wang (2005), Brockman et al. (2009), and Fu (2009) provide
empirical evidence supporting that, compared to the realized monthly idiosyncratic volatility
calculated from daily data, the conditional idiosyncratic volatility estimated from GARCH
models using monthly data is a more accurate proxy for expected future idiosyncratic
volatility.2 These studies, however, all focus on the measure of idiosyncratic volatilities and
do not apply time-varying models to measure the dynamics of the systematic market risk. Bali
et al. (2012), on the other hand, apply the dynamic conditional correlation (DCC) model of
Engle (2002) to construct dynamic conditional market betas and investigate the significance of
the conditional betas in predicting the cross-sectional variations in expected returns using firmlevel data. 3 But they do not discuss the idiosyncratic risk. This paper is the first in the
literature to consider both dynamic idiosyncratic risk and dynamic systematic risk, and the first
to apply the DCC model to country-level equity indices and to the world market integration
issue.
1
This literature particularly concerns the idiosyncratic volatility puzzle raised by Ang et al.
(2006). Measuring monthly idiosyncratic volatility by realized volatility in the previous month
using daily data, they find that stocks with high idiosyncratic volatilities in the previous month
have abysmally low average monthly returns.
2
Fu (2009) argues that the idiosyncratic volatility puzzle is caused by the use of the realized
idiosyncratic volatility, which is not a good predictor of the expected idiosyncratic volatility.
Spiegel and Wang (2005), using monthly data and EGARCH models, also find that stock
returns are increasing with the level of idiosyncratic volatility. Brockman et al. (2009) apply
Fu’s EGARCH model to another set of international index data and make the same conclusion.
On the other hand, Hueng and Yau (2013) show that realized idiosyncratic volatilities work as
well as conditional idiosyncratic volatilities in predicting international index returns.
3
You and Daigler (2010) apply the DCC model to estimate the conditional correlations
among international equity markets and show that the correlations change over time.
3
In sum, this paper revisits the relationship among world market risk, country-specific
risk, and expected returns in international stock markets by proposing the following two
improvements over the previous literature. First, this paper uses conditional second moments,
instead of lagged realized second moments, to measure the systematic and idiosyncratic risks.
The Asymmetric Dynamic Conditional Correlation Multivariate EGARCH (A-DCC-MVEGARCH) model introduced by Capiello et al. (2006) is used to model the time-varying
conditional world beta risk and to derive the conditional country-specific idiosyncratic risk.
Second, when running the Fama-MacBeth cross-sectional regressions, this paper takes into
account the conditional relationship between the global systematic risk and the index returns
by partitioning the data into up market and down market periods based on the sign of the
realized world market returns.
The next section discusses the regression models that are used to test the significance of
the systematic and idiosyncratic risks in international asset pricing.
The A-DCC-MV-
EGARCH model used to estimate the conditional second moments is also specified in this
section. Section 3 describes the data and examines the relevant sample statistics. The crosscountry risk-return analyses are shown in Section 4. Section 5 concludes the paper.
2.
The Model
If international markets are completely integrated, the expected market returns for country i
( Ri ,t ) depend on the covariance with the world market index returns [Dumas and Solnik
(1995)]:
Et −1 Ri ,t = λt −1Covt −1 ( Ri ,t , Rw,t ) ,
(1)
4
where λt is the expected world price of risk and Rw,t is the world return. For completely
segmented markets, only the variance of the index returns of a country affect the expected
returns of a country’s index returns:
Et −1 Ri ,t = λi ,t −1Vart −1 ( Ri ,t ) .
(2)
where λi ,t is the expected price of country i's idiosyncratic risk.
Bali and Cakici (2010) generalize (1) and (2) and consider the following partialintegration model:
Et −1 Ri ,t = λt −1Covt −1 ( Ri ,t , Rw,t ) + λi ,t −1Vart −1 ( Ri ,t ) .
(3)
To test the cross-sectional predictive power of world market risk and country-specific risk
under this partial integration model, they use the following Fama and MacBeth (1973)
regressions across countries in each month t:
Ri ,t = γ 0,t + γ 1,t Betai ,t −1 + γ 2,t IVOLi ,t −1 + ε i ,t ,
(4)
where γ 1,t is the price of world beta risk and γ 2,t is the price of country-specific risk. Country
i's monthly world market beta ( Betai ,t ) is obtained by regressing country i's daily market
portfolio index returns ( Ri ,d ,t ) on the daily world market portfolio returns ( Rw,d ,t ):
Ri ,d ,t = µi ,t + Betai ,t ⋅ Rw,d ,t + ri ,d ,t , for d = 1, 2, . . ., Dt ,
(5)
where Dt is the number of trading days in month t and ri ,d ,t is the daily idiosyncratic return.
The country-specific idiosyncratic volatility in month t is defined as the realized monthly
5
standard deviation of the daily idiosyncratic returns: IVOLi ,t =
Dt
∑ (r
d =1
i , d ,t
− ri , d ,t ) . That is, they
2
run a two-stage regression. In the first stage, the daily data are used in (5) to obtain the
estimates of the monthly observations for Betai ,t and IVOLi ,t . Then in the second stage, they
use lagged realized risk measures [ Betai ,t −1 and IVOLi ,t −1 ] to proxy for the expected risks
[ Et −1 ( Betai ,t ) and Et −1 ( IVOLi ,t ) ] and run regression (4).
Using country-level aggregate market index data from 37 countries and a world market
portfolio index, Bali and Cakici find that the time-series average of the estimated effect of
IVOLi ,t −1 on Ri ,t (i.e., γˆ2,t ) is positive and statistically significant, while the relationship
between average returns on countries' stock market indices and world market beta (i.e., γˆ1,t ) is
positive but insignificant.4 This indicates that the country-specific risk is priced and the price
of this risk is the same across countries, but the systematic world beta risk is not priced.
When analyzing the firm-level data in the U.S. stock market, Pettengill et al. (1995)
argue that the Fama-MacBeth methodology cannot directly test the expected risk-return
relationship implied by the CAPM. The CAPM shows that since the expected market return
must be greater than the risk-free rate (otherwise all investors would hold the risk-free assets),
the expected return of any risky asset must be a positive function of beta. However, the FamaMacBeth methodology utilizes the realized returns to proxy for the expected returns. Pettengill
et al. argue that there must be a non-zero probability that the realized market return is smaller
4
Bali and Cakici (2010) also replace lagged idiosyncratic volatility with lagged country-
specific total volatility and reach the same conclusions on integration/segmentation.
6
than the risk-free rate (otherwise no investor would hold the risk-free assets). To solve this
problem, they partition the market into an up market and a down market based on the sign of
the realized market excess return. A positive risk-return relationship should exist in the up
market and an inverse relationship should exist in the down market. In addition, to test for a
positive risk and return tradeoff, two necessary conditions need to hold, namely a positive
excess market return on average and a symmetric risk premium in up and down markets.
Fletcher (2000) and Tang and Shum (2003) adopt Pettengill et al.'s idea and use
country-level index data to test the conditional relationship between index returns and a world
market beta:
Ri ,t = γ 0,t + γ 1,+tδ t ⋅ Eˆ t −1 Betai ,t + γ 1,−t (1 − δ t ) ⋅ Eˆ t −1 Betai ,t + ε i ,t ,
(6)
where δ t = 1 if Rw,t > 0 (an up world market) and δ t = 0 if Rw,t <0 (a down world market).
Fletcher assumes that the world beta is constant over time ( Betai ,t = Betai ) and uses monthly
data for the whole sample period (1970-1998) to estimate Et −1 Betai ,t for each country. Tang
and Shum assume that the world beta is constant within a year and is estimated by using the
data over the past five years in rolling regressions. Both find evidence confirming Pettengill et
al.'s argument. But apparently they do not consider country-specific idiosyncratic risk.5
This paper generalizes (4) and (6) and considers the following model:
5
Tang and Shum (2003) use country indices denominated in domestic currencies and consider
exchange rates as an idiosyncratic risk factor. They include exchange rate in the first step of
the regressions when estimating the world beta to remove the country-specific exchange rate
effects. But when testing the risk-return relationship in the second stage, they do not consider
the idiosyncratic risk.
7
Ri ,t = γ 0,t + γ 1,+t δ t ⋅ Eˆ t −1 ( Betai ,t ) + γ 1,−t (1 − δ t ) ⋅ Eˆ t −1 ( Betai ,t ) + γ 2,t Eˆ t −1 ( IVOLi ,t ) + ε i ,t .
(7)
Unlike the aforementioned studies, this paper uses autoregressive conditional second moments
to estimate the expected world beta and country-specific risk. Spiegel and Wang (2005),
Brockman et al. (2009), and Fu (2009) all provide empirical evidence showing that the
conditional idiosyncratic volatility estimated from GARCH models using monthly data is a
more accurate proxy for expected future idiosyncratic volatility than the realized monthly
idiosyncratic volatility calculated from daily data.
The same argument can apply to the
covariance between individual country index returns and the world market returns [You and
Daigler (2010) and Bali et al. (2012)]. Therefore, I construct the measures of the expected
world market beta and idiosyncratic volatility by using the Asymmetric Dynamic Conditional
Correlation Multivariate EGARCH (A-DCC-MV-EGARCH) model suggested by Cappiello et
al. (2006).
Cappiello et al.'s asymmetric DCC model generalizes Engle's (2002) DCC model by
allowing conditional asymmetries in correlation dynamics. This modeling strategy involves
regressions in two stages. In the first stage a univariate GARCH model is estimated for each
asset. In the second stage, the transformed residuals resulting from the first stage are used to
estimate a conditional correlation estimator.
Specifically for the purpose of this paper, it is assumed that country i's index return
( Ri ,t ) and the world market index return ( Rw,t ) are conditionally bivariate normal with zero
 hi ,t
 hiw,t
expected value and covariance matrix H t ≡ 
hiw,t 
. In the first stage, I use the following
hw,t 
univariate EGARCH model to estimate the conditional variances hi ,t and hw,t :
8
ln h j ,t = κ j + α j ⋅ ln h j ,t −1 + β j ⋅
R j ,t −1
h j ,t −1
+γ j ⋅
R j ,t −1
h j ,t −1
j = i , w.
,
(8)
This EGARCH model, proposed by Nelson (1991), takes account for the importance of the
asymmetric responses of conditional volatilities to positive and negative news.
In the second stage, let ε j ,t =
R j ,t
h j ,t
be the standardized returns with h j ,t estimated from
the first stage and ε t = ε i ,t ε w,t  ' . Express the covariance matrix as H t = Ct ⋅ Pt ⋅ Ct , where
 hi ,t
Ct = 
 0

0 
 . Then Pt = Ct−1 ⋅ H t ⋅ Ct−1 is the correlation matrix with ones on the diagonal
hw,t 
and the off-diagonal element less than or equal to one in absolute value. Engle (2002) suggests
that the diagonal element of the correlation matrix can be constructed by estimating another
univariate GARCH process.
This computational advantage makes the DCC model more
attractive than other multivariate GARCH specifications such as the VEC model and the
BEKK representation [see Engle and Kroner (1995)].
 q11,t
Qt ≡ 
 q12,t
 q11,t
q12,t 
*
is
symmetric
and

Q
=
t
q22,t 
 0

Let Pt = Qt*−1 ⋅ Qt ⋅ Qt*−1 , where
0 
 , the matrix version of the DCC model
q22,t 
is given by Qt = ( P − a 2 P − b 2 P ) + a 2ε t −1ε t' −1 + b 2 ⋅ Qt −1 , where P = E (ε t ε t' ) and a and b are
parameters to be estimated.
To allow for conditional asymmetric responses in correlation dynamics to positive and
negative news, Cappiello et al. generalize the evolution of the correlation to:
Qt = ( P − a 2 P − b 2 P − g 2 N ) + a 2ε t −1ε t' −1 + g 2 nt −1nt' −1 + b 2 ⋅ Qt −1 ,
9
(9)
where nt = I [ε t < 0] ε t and N = E (nt nt' ) .
The indicator function I [⋅] equals one if the
argument is true and zero otherwise, and
is the Hadamard product. To estimate the model,
P and N are proxied by their sample analogues and Q0 is set to equal P .
A sufficient
condition for Qt to be positive definite is a positive semi-definite ( P − a 2 P − b 2 P − g 2 N ) . A
necessary and sufficient condition for this to hold is a 2 + b 2 + ξ ⋅ g 2 < 1, where ξ is the
maximum eigenvalue of P −1/2 ⋅ N ⋅ P −1/2 . Conditioning on the parameters estimated in the first
stage, a, b, and g can be estimated by maximizing the likelihood: −
1
 log ( Pt ) + ε t' Pt −1ε t  .
∑

2 t 
Once Pt is estimated in the second stage, combining Ct estimated from the first stage
yields the estimate of H t .
Then the time-varying conditional correlation is
hiw ,t
hi ,t hw ,t
, the
h
conditional world market beta is measured as Eˆ t −1 ( Betai ,t ) = iw,t , and the idiosyncratic return is
hw,t
constructed as ri ,t = Ri ,t − Eˆ t −1 ( Betai ,t ) ⋅ Rw,t . The conditional idiosyncratic volatility Eˆt −1 ( IVOLi ,t )
is the squared root of the conditional variance from another univariate EGARCH process (8)
for ri ,t .
Once Eˆ t −1 ( Betai ,t ) and Eˆt −1 ( IVOLi ,t ) are obtained from the A-DCC-MV-EGARCH
model in the first stage, they are plugged into the cross-sectional regression (7) in the second
stage.
3.
Data
I use the same data as those in Bali and Cakici (2010), whose data end in September 2006, but
update their data to May 2012. The data are obtained from Datastream Global indices and
10
include stock market indices for 37 countries plus the world market portfolio, all denominated
in U.S. dollars. There are 23 developed markets and 14 developing or emerging markets.6
Panel (A) of Table 1 shows the summary statistics of the monthly market index returns
for each country and the world market, including the means, standard deviations, and constant
correlations with the world market index returns. The starting month for each country is
shown in the second column. The sample ends in May 2012 for all countries. Compared to
those reported in Bali and Cakici, the updated data show slightly lower means and standard
deviations. More interestingly, the constant correlations with the world are higher in most of
the countries with the new data added, indicating that the global markets are getting more
integrated after their study.7 Comparing across countries, the summary statistics are in general
very similar to those in Bali and Cakici in that the emerging markets exhibit higher average
returns and higher standard deviations of returns compared to the developed markets.
In Panel (B) of Table 1, the first four columns show the means, standard deviations,
maximum values, and minimum values of the time-varying correlations with the world
estimated from the A-DCC-MV-EGARCH model. As can be seen for most countries, the
6
Ince and Porter (2006) point out several data problems in Datastream. Since most of the
problems identified in their paper are concentrated in the smaller size firms, to make sure that
the potential problems do not change my conclusions, I also do the analyses using an
alternative data source - the MSCI large- and mid-cap price returns, available from January
1980. This dataset also avoids the problem from using index returns. These results do not
change the conclusions in the paper and are available from the author upon request.
7
Even though here I follow Bali and Cakici (2010) and preliminarily use the average
correlations with the world market as an alternative measure of integration, note that it is wellknown in the literature that correlation is not a good measure of integration. See, for example,
Dumas et al. (2003) and Carrieri et al. (2007).
11
variations of the correlations are significant. When regressing the dynamic correlations on a
constant and a linear time trend, I find that the slope against the time trend (reported in the fifth
column) is positive and statistically significant for all countries except for Finland (an
insignificantly positive slope) and Japan (an insignificantly negative slope). This indicates that
the degree of integration with the world is increasing over time in most of the markets.
Panels (A) and (B) of Table 2 report the summary statistics for the realized world market
beta, Betai ,t −1 , and the conditional measure of world market beta, Eˆ t −1 ( Betai ,t ) , respectively.
The realized beta, used in Bali and Cakici, is estimated by using daily data within each month
to run time-series regression (5).
In general the realized beta in Panel (A) shows high
volatilities, with the standard deviation as high as the mean in most of the markets. The firstorder autocorrelation of the realized beta is mostly lower than 0.5, indicating that past realized
market beta is not a good predictor of future market beta. On the other hand, in Panel (B), the
conditional measure of beta estimated from the A-DCC-MV-EGARCH model is more stable,
with the standard deviation much lower than the mean in most of the markets. In addition, the
conditional measure of beta is very persistent, with the first-order autocorrelation coefficient
mostly higher than 0.9. Furthermore, the last column of Table 2 shows that the correlations
between the realized beta and the conditional beta are mostly lower than 0.5. Therefore, it is
expected that using the conditional beta would yield different results than those in the previous
studies that use the realized beta.
Panels (A) and (B) of Table 3 report the summary statistics of the realized idiosyncratic
volatility, IVOLi ,t −1 , and the conditional measure of idiosyncratic volatility, Eˆ t −1 ( IVOLi ,t ) ,
respectively.
The realized monthly idiosyncratic volatility is calculated from the daily
12
idiosyncratic returns estimated in equation (5). Compared to those reported in Bali and Cakici,
the realized country-specific volatilities are in general lower with the updated data added. But
the cross-country comparison is in general consistent with those reported in Bali and Cakici in
that the country-specific idiosyncratic volatility is much higher in the emerging markets than in
the developed markets. The first-order autocorrelations are around 0.5 for most countries,
which again indicates that past realized idiosyncratic volatility is not a very good predictor of
future idiosyncratic volatility. The conditional idiosyncratic volatility estimated from the ADCC-MV-EGARCH model, on the other hand, generally has a higher mean and is more stable
compared to the realized measure. The conditional measure is also very persistent. Finally,
the last column of Table 3 shows that the correlations between the realized measure and the
conditional measure are mostly lower than 0.5. Therefore, it is important to replace the
realized measures with the conditional measures in order to test the risk-return relationship.
4.
Empirical Results
The empirical work starts by revisiting Bali and Cakici's (2010) results in equation (4), where
the Fama and MacBeth (1973) regression is used for the extended sample period to examine
the cross-country relationship between expected returns and the realized measures of risks.
Following Bali and Cakici, I include two more control variables, the earnings-to-price ratio
and the dividends-to-price ratio, in the regressions. The first row of Panel (A) in Table 4
reports the time-series averages of the estimated coefficients, their p-values based on the
Newey and West (1987) heteroscedasticity- and autocorrelation-adjusted t-statistics, and the
time-series averages of the R-squared. The newly added data do not alter Bali and Cakici's
conclusions: the country-specific risk is priced and its effect on index returns is statistically
13
significant. The effect of the systematic risk on index returns, on the other hand, is highly
insignificant.
The second row of Panel (A) shows the results from using the conditional measures of
the world market beta and idiosyncratic risks. The conditional measures fit the model better by
raising the R-squared by 1.7%. More importantly, the effect of the idiosyncratic risk on the
index returns is lowered and only statistically significant at the 7.7% level. Recall that the
world markets are more integrated after Bali and Cakici's study. Therefore, one would expect
the significant effect of the country-specific risk on returns found in their paper to decline in
the new sample. Using the conditional measure, the results of this paper are consistent with
this expectation.8
Next I turn the attention to the systematic risk. Consistent with previous studies, the
results in Panel (A) of Table 4 show that there is no significant unconditional relationship
between expected returns and the world market beta. However, these results from the FamaMacBeth regressions are apparently subject to Pettengill et al.'s (1995) critics and cannot
directly test the expected risk-return relationship implied by the CAPM. To conduct a valid
test, the Fama-MacBeth regressions should be conditioned on positive and negative world
market returns. The results of the conditional regression (7) in the up and down world markets
are shown in Panel (B) of Table 4. Consistent with Fletcher (2000) and Tang and Shum
8
I also run a regression with conditional measures using only data prior to the recent financial
crisis (data up to 2007). The conclusions do not change. The estimated coefficient on the
idiosyncratic risk is higher (0.105) in the pre-crisis period than that (0.094) in the whole
sample period, and is again only marginally significant at the 6.7% level. This result supports
the argument that the world market is more integrated during the financial crisis.
14
(2003), when the sample is partitioned into up and down world markets, there is a significant
positive relationship between index returns and the world beta in up market months and a
significant and negative relationship in down market months. Therefore, high beta indices
outperform low beta indices when the realized world market return is positive, but incur higher
losses when the realized world market return is negative.
To test for a positive systematic risk and return tradeoff, the last column of Panel (B)
reports the test statistics of the two-sample t-test for a symmetric risk premium in up and down
market months.
The symmetric relationship cannot be rejected at the conventional
significance level. Combined with the positive mean world return, the evidence supports the
expectation of a positive reward for bearing systematic risk. Along with the results in Panel
(A), after adding the most recent data when the world markets are highly integrated, the world
market beta remains significant but the country-specific risk is only marginally significant.
Further support from a different sample
The empirical results so far provide more intuitive evidence that country-specific risk
plays a lesser role in determining international equity returns in a more integrated world capital
market. To further support this argument, I consider a more recent sample period when the
global market is increasingly more integrated. Studies have provided evidence supporting that
the degree of world financial market integration has increased since the 1990s. For example,
Carrieri et al. (2007) use an integration index to show that emerging markets have become
15
more integrated with other global markets after the early 1990s.9 Hardouvelis et al. (2006)
find that European markets became fully integrated in the second half of the 1990s due to the
formation of the European Union. You and Daigler (2010) use time-varying correlations to
show that the correlations of international index markets have increased since the late 1990s.
Therefore, I consider data starting from the 1990s to see whether the effect of country-specific
risk on international equity returns is insignificant in a more integrated world capital market.
A larger pool of international index data is available starting from June 1994. The
MSCI Investable Market Indices cover all investable large, mid, and small cap securities
across 24 developed markets, 21 emerging markets, and 26 frontier markets, a total of 71
markets plus the world market portfolio. In Appendix, Tables A1-A3 report the summary
statistics of the data for the developed markets, the emerging markets, and the frontier markets,
respectively. Since the data on the earnings-to-price ratio and the dividends-to-price ratio are
not available for half of the countries, these two variables are not included in the regression.
Since the results in Section (II) of Table 4 show that these two variables do not have
significant effects on returns when the conditional measures are used, it should not be too
unreasonable to assume that ignoring these two variables would not change the conclusions.10
Table 5 reports the estimation results. There is a significant and positive relationship
between index returns and the world beta in up market months and a significant and negative
9
Bekaert and Harvey (1995, 2000) imply that liberalization of the financial markets of
emerging countries causes further integration of international stock markets. Bekaert, Harvey,
and Lumsdaine (2002) show that significant break points exist for factors that indicate world
financial market integration for several emerging markets after the liberalization.
10
Fletcher (2000) and Tang and Shum (2003) do not include these two variables in their
analyses, either.
16
relationship in down market months. The relationships in the up and down markets are
symmetric. Therefore, the systematic risk is priced. On the other hand, I find no evidence of a
priced country-specific idiosyncratic risk.
The relationship between index returns and
idiosyncratic risk is highly insignificant. This result indicates that the global capital markets
are highly integrated so that the country-specific idiosyncratic risk is not important in pricing
international equity indices.
5.
Conclusions
Empirical evidence pointing to the significant effects of local factors on global equity returns
while failing to find significant effects from global systematic risk seems counter-intuitive in
today’s integrated world markets. This paper revisits this empirical issue by providing more
accurate evidence. Specifically, an asymmetric dynamic conditional correlation multivariate
EGARCH model is used to estimate the time-varying conditional world market beta risk and to
derive the conditional country-specific idiosyncratic risk. In addition, when using the Fama
and MacBeth (1973) methodology to test the significance of the relationship between world
beta risk and country-level index returns, this paper takes into account the conditional
relationship between the global systematic risk and index returns by partitioning the data into
up market and down market periods based on the sign of the realized world market returns.
Using 37 country-level index data and a global market risk factor from 1973 to 2012,
and a larger pool of data from 71 countries for the period 1994-2012, this paper shows that the
conditional world beta risks significantly affect country-level index returns, while countryspecific risk factors are not significantly priced. The results, therefore, support international
financial integration.
17
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21
Table 1: Summary Statistics of International Market Indices
The data are from Datastream Global indices. The sample ends in May 2012 for all countries. The
dynamic correlations with the world are estimated from the A-DCC-MV-GARCH model.
The
asterisk * indicates statistical significance at the 5% level, and ** at the 10% level.
Country
Data
start
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Greece
Hong Kong
India
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Singapore
South Africa
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
WORLD
Aug-93
Jan-73
Jan-73
Jan-73
Jul-94
Jan-73
Jul-89
Jul-93
Jan-73
Mar-88
Jan-73
Jan-73
Jan-90
Jan-73
Jan-90
Jan-73
Jan-73
Jan-73
Sep-87
Jan-86
May-89
Jan-73
Jan-88
Jan-80
Nov-88
Mar-94
Jan-90
Jan-73
Jan-73
Mar-87
Jan-82
Jan-73
May-88
Jan-87
Jun-89
Jan-73
Jan-73
Jan-73
(A) Market Index Returns
Const.
Std.
Mean
Corr. w/
Dev.
WORLD
0.552
9.257
0.521
1.103
7.242
0.659
0.965
6.789
0.527
0.990
5.886
0.684
1.525
10.760
0.689
0.945
5.550
0.766
1.572
6.740
0.484
1.541
11.121
0.429
1.115
5.944
0.624
0.987
8.673
0.680
1.101
6.780
0.731
0.939
6.066
0.721
0.673
10.312
0.494
1.399
9.893
0.533
1.294
10.647
0.367
1.074
7.262
0.677
0.860
7.636
0.581
0.757
6.180
0.701
1.143
11.043
0.567
1.282
8.627
0.446
1.605
8.608
0.609
1.057
5.607
0.829
0.900
6.407
0.629
1.188
8.034
0.677
1.236
9.020
0.480
0.831
10.805
0.623
0.490
6.165
0.671
1.033
8.422
0.641
1.320
8.262
0.568
0.834
6.619
0.776
1.326
7.357
0.754
1.024
5.172
0.728
0.905
10.780
0.452
1.535
10.665
0.529
2.291
16.557
0.383
1.076
6.513
0.741
0.928
4.484
0.823
0.868
4.527
1.000
22
(B) Dynamic Correlation with World
Trend
Mean
Std
Max
Min
Slope
x103
0.573
0.051
0.751
0.390 0.093**
0.660
0.181
0.956
0.144
0.862*
0.488
0.206
0.917
0.132
0.887*
0.652
0.146
0.924
0.271
0.493*
0.709
0.062
0.886
0.358
0.354*
0.752
0.089
0.892
0.491
0.342*
0.496
0.235
0.793 -0.171
2.387*
0.440
0.213
0.780 -0.051
2.051*
0.592
0.133
0.894
0.316
0.755*
0.682
0.000
0.682
0.682
0.000
0.736
0.137
0.964
0.364
0.752*
0.672
0.211
0.970 -0.021
1.014*
0.516
0.253
0.940 -0.094
2.431*
0.583
0.200
0.911 -0.330
0.596*
0.354
0.284
0.806 -0.297
3.260*
0.682
0.100
0.874
0.452
0.479*
0.572
0.213
0.960
0.133
1.144*
0.714
0.057
0.845
0.527
-0.021
0.565
0.230
0.906 -0.104
2.218*
0.526
0.126
0.803
0.213
0.573*
0.643
0.232
0.929
0.029
2.329*
0.806
0.072
0.951
0.624
0.349*
0.611
0.115
0.838
0.338
1.047*
0.666
0.140
0.908
0.348
0.897*
0.482
0.065
0.664
0.299
0.240*
0.626
0.149
0.934 -0.088
0.940*
0.621
0.108
0.854
0.387
1.024*
0.646
0.148
0.923
0.036
0.457*
0.553
0.137
0.885
0.348
0.739*
0.750
0.091
0.900
0.461
0.606*
0.723
0.174
0.958
0.170
1.214*
0.710
0.118
0.914
0.252
0.326*
0.494
0.048
0.759
0.361
0.110*
0.500
0.146
0.817
0.010
0.696*
0.406
0.227
0.748 -0.017
2.588*
0.787
0.112
0.975
0.489
0.658*
0.822
0.114
0.964
0.420
0.120*
---------------------
Table 2: Summary Statistics of the Realized and Conditional Market Betas
The realized beta is estimated by equation (5) using daily data. The conditional beta is estimated from
the A-DCC-MV-GARCH model using monthly data.
Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Greece
Hong Kong
India
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Singapore
South Africa
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
Mean
0.790
0.555
0.607
0.693
1.275
0.788
0.561
0.729
0.604
1.146
0.903
0.886
0.726
0.653
0.405
0.727
0.755
0.960
0.682
0.455
1.000
0.888
0.471
0.920
0.397
0.980
0.671
0.541
0.733
0.989
1.038
0.733
0.512
0.575
0.869
0.929
0.955
(A) Realized Beta
Std.
Auto corr.
0.735
0.160
0.560
0.352
0.578
0.572
0.464
0.445
0.701
0.356
0.396
0.452
0.472
0.312
0.711
0.323
0.569
0.394
0.662
0.360
0.507
0.479
0.508
0.529
0.729
0.123
0.676
0.266
0.676
0.358
0.571
0.454
0.706
0.363
0.636
0.658
0.732
0.141
0.605
0.412
0.716
0.179
0.451
0.509
0.484
0.151
0.621
0.464
0.673
0.162
0.762
0.365
0.497
0.509
0.546
0.247
0.742
0.480
0.483
0.544
0.610
0.487
0.433
0.342
0.738
0.194
0.913
0.263
1.274
0.357
0.453
0.357
0.359
0.663
(B) Conditional Beta
Corr. between Realized
Mean
Std.
Auto corr. and Conditional Betas
1.181
0.272
0.932
0.216
1.038
0.303
0.947
0.288
0.672
0.354
0.951
0.554
0.839
0.229
0.919
0.304
1.633
0.389
0.747
0.131
0.942
0.201
0.963
0.518
0.719
0.341
0.965
0.307
1.045
0.584
0.915
0.359
0.779
0.210
0.954
0.339
1.295
0.238
0.907
0.170
1.094
0.236
0.942
0.150
0.899
0.318
0.942
0.409
0.999
0.479
0.900
0.303
1.144
0.482
0.802
0.133
0.755
0.717
0.976
0.469
1.078
0.199
0.855
0.226
0.938
0.319
0.959
0.357
0.991
0.219
0.951
0.557
1.227
0.495
0.912
0.272
0.859
0.289
0.881
0.363
1.148
0.340
0.884
0.310
1.002
0.171
0.945
0.463
0.839
0.178
0.909
0.155
1.167
0.293
0.959
0.432
0.925
0.218
0.915
0.121
1.443
0.414
0.536
0.033
0.833
0.172
0.937
0.518
1.113
0.382
0.929
0.065
1.025
0.272
0.952
0.399
1.048
0.200
0.833
0.422
1.178
0.301
0.905
0.430
0.824
0.141
0.897
0.060
1.101
0.256
0.885
-0.035
1.115
0.372
0.907
0.345
1.371
0.738
0.981
0.348
1.063
0.218
0.926
0.165
0.824
0.162
0.940
0.574
23
Table 3: Summary Statistics of the Realized and Conditional Idiosyncratic Volatilities
The realized idiosyncratic volatility is calculated from equation (5) using daily data. The conditional
idiosyncratic volatility is estimated from the DCC-MV-GARCH model using monthly data.
Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
China
Denmark
Finland
France
Germany
Greece
Hong Kong
India
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
New Zealand
Norway
Philippines
Poland
Portugal
Singapore
South Africa
Spain
Sweden
Switzerland
Taiwan
Thailand
Turkey
UK
US
(A) Realized IVOL
Mean Std.
Auto corr.
6.225 3.898
0.526
4.848 2.210
0.508
3.709 1.772
0.608
3.690 1.578
0.460
6.267 3.266
0.608
2.896 1.314
0.523
4.178 1.853
0.510
7.261 3.676
0.622
4.205 1.993
0.407
5.707 2.987
0.686
4.067 1.788
0.531
3.597 1.543
0.426
6.691 3.318
0.605
6.309 3.857
0.587
6.595 3.524
0.428
4.424 1.988
0.427
4.981 2.393
0.606
4.003 2.048
0.635
8.086 5.025
0.719
5.078 4.327
0.671
5.211 3.253
0.631
3.420 1.581
0.576
4.651 2.105
0.462
5.322 2.336
0.532
6.064 3.157
0.408
6.887 3.698
0.581
3.857 1.543
0.500
4.941 2.877
0.607
5.781 2.693
0.455
4.008 1.839
0.480
4.874 2.204
0.524
3.480 1.418
0.476
7.432 3.484
0.678
7.171 3.756
0.621
11.153 5.810
0.579
3.792 1.838
0.656
2.471 1.496
0.599
(B) Conditional IVOL
Mean
Std.
Auto corr.
7.743
2.136
0.920
5.024
1.398
0.978
4.998
1.846
0.915
4.148
0.877
0.944
7.337
2.417
0.927
3.425
0.275
0.980
5.721
1.373
0.963
9.312
3.487
0.918
4.499
0.893
0.936
6.056
1.493
0.962
4.277
1.187
0.967
3.998
0.895
0.957
7.782
3.399
0.930
7.183
3.392
0.902
9.127
2.435
0.945
5.160
1.472
0.946
5.849
1.548
0.970
4.230
0.503
0.949
8.142
3.447
0.916
6.668
2.966
0.940
6.359
2.041
0.976
3.056
0.404
0.953
4.566
0.595
0.984
5.602
1.195
0.966
7.609
1.536
0.961
7.832
2.249
0.962
4.542
0.650
0.951
5.855
2.161
0.913
6.643
1.238
0.967
4.097
0.749
0.937
4.810
1.197
0.892
3.550
0.675
0.922
8.689
2.634
0.982
8.747
2.022
0.974
14.340 3.240
0.988
3.777
1.348
0.958
2.399
0.556
0.933
24
Corr. between Realized
and Conditional IVOLs
0.438
0.186
0.468
0.413
0.465
-0.014
0.487
0.405
0.259
0.540
0.399
0.355
0.564
0.596
0.395
0.333
0.490
0.004
0.565
0.594
0.526
0.359
0.327
0.358
0.373
0.483
0.367
0.537
0.324
0.421
0.320
0.313
0.565
0.501
0.520
0.519
0.279
Table 4: Cross-Sectional Regressions
In Panel (A), (I) reports the cross-sectional regression results from:
Ri ,t = γ 0,t + γ 1,t Betai ,t −1 + γ 2,t IVOLi ,t −1 + γ 3,t EPi ,t −1 + γ 4,t DYi ,t −1 + ε i ,t ,
where the monthly Betai ,t is obtained by regressing country i's daily market returns on the daily world market returns [Equation (5)]
and the idiosyncratic volatility IVOLi ,t is the realized monthly standard deviation of the daily idiosyncratic returns obtained from the
regression. The control variable EPi ,t is the natural logarithm of the earnings-to-price ratio, and DYi ,t is the natural logarithm of the
dividends-to-price ratio in month t. Rows (II) and (III) of Panel (A) reports the cross-sectional regression results from:
Ri ,t = γ 0,t + γ 1,t Eˆ t −1 ( Betai ,t ) + γ 2,t Eˆt −1 ( IVOLi ,t ) + γ 3,t EPi ,t −1 + γ 4,t DYi ,t −1 + ε i ,t ,
where the monthly observations of Eˆ t −1 ( Betai ,t ) and Eˆ t −1 ( IVOLi ,t ) are obtained from the regression results of the A-DCC-MVEGARCH model [Equations (8) and (9)].
In Panel (B), (I) reports the cross-sectional regression results from:
Ri ,t = γ 0,t + γ 1,+t Dt ⋅ Betai ,t −1 + γ 1,−t (1 − Dt ) ⋅ Betai ,t −1 + γ 2,t IVOLi ,t −1 + γ 3,t EPi ,t −1 + γ 4,t DYi ,t −1 + ε i ,t ,
and (II) and (III) report the cross-sectional regression results from:
Ri ,t = γ 0,t + γ 1,+t Dt ⋅ Eˆ t −1 ( Betai ,t ) + γ 1,−t (1 − Dt ) ⋅ Eˆ t −1 ( Betai ,t ) + γ 2,t Eˆ t −1 ( IVOLi ,t ) + γ 3,t EPi ,t −1 + γ 4,t DYi ,t −1 + ε i ,t .
The average intercepts, average slope coefficients, and average R2 are presented. The numbers in parentheses are P-values
calculated based on Newey and West (1987) t-statistics. A P-value of 0.000 indicates that the P-value is nonzero, but smaller than
0.0005.
(A)
Model
(I) Realized Measures
(II) Conditional Measures
γ0
γ1
γ2
(B)
γ3
γ4
2.525
0.096
0.118
0.677
-0.015
(0.001)
(0.549)
(0.036)
(0.001)
(0.940)
1.102
0.230
0.094
0.277
0.123
(0.186)
(0.511)
(0.077)
(0.246)
(0.509)
25
Average
R2
0.329
0.346
γ 1+
γ 1−
0.701
-0.881
(0.000)
(0.001)
1.926
-2.522
(0.000)
(0.000)
H 0 : γ 1+ = γ 1−
P-Value
0.594
0.343
Table 5: Cross-Sectional Regressions for 71 Markets
This table reports the cross-sectional regression results from:
Ri ,t = γ 0,t + γ 1,+t Dt ⋅ Eˆ t −1 ( Betai ,t ) + γ 1,−t (1 − Dt ) ⋅ Eˆ t −1 ( Betai ,t ) + γ 2,t Eˆ t −1 ( IVOLi ,t ) + ε i ,t .
The average intercepts, average slope coefficients, and average R2 are presented. The numbers
in parentheses are p-values calculated based on Newey and West (1987) t-statistics. A p-value
of 0.000 indicates that the p-value is nonzero, but smaller than 0.0005.
γ0
γ 1+
γ2
γ 1−
0.187
2.664
-3.049
0.024
(0.237)
(0.000)
(0.000)
(0.710)
26
Average R2
0.199
H 0 : γ 1+ = γ 1−
p-value
0.509
Appendix:
The data in Tables A1-A3 are from MSCI Investable Market Indices. The sample ends in May
2012 for all countries. The dynamic correlations with the world are estimated from the ADCC-MV-GARCH model. A value of 0.000 indicates that it is nonzero but smaller than
0.0005.
Table A1: Summary Statistics of International Market Indices (Developed Markets)
(A) Market Index Returns
Constant
Data start
Country
Mean
Std Correlation
with World
AUSTRALIA
May-94
0.675 6.224
0.844
AUSTRIA
May-94
0.431 6.930
0.736
BELGIUM
May-94
0.415 6.141
0.778
CANADA
May-94
0.875 6.069
0.861
DENMARK
May-94
0.866 5.880
0.795
FINLAND
May-94
0.810 9.033
0.748
FRANCE
May-94
0.456 6.066
0.892
GERMANY
May-94
0.492 6.743
0.881
GREECE
May-94 -0.024 10.02
0.606
HONG KONG
May-94
0.509 7.464
0.698
IRELAND
May-94
0.440 6.728
0.795
ISRAEL
May-94
0.643 7.003
0.637
ITALY
May-94
0.201 7.053
0.774
JAPAN
May-94 -0.088 5.494
0.653
NETHERLANDS
May-94
0.492 6.015
0.886
NEW ZEALAND
May-94
0.352 5.985
0.692
NORWAY
May-94
0.847 7.750
0.799
PORTUGAL
May-94
0.232 6.544
0.691
SINGAPORE
May-94
0.457 7.591
0.724
SPAIN
May-94
0.505 6.894
0.799
SWEDEN
May-94
0.917 7.458
0.849
SWITZERLAND
May-94
0.648 4.911
0.779
UNITED KINGDOM May-94
0.421 4.758
0.901
USA
May-94
0.626 4.680
0.955
WORLD
May-94
0.418 4.614
1.000
27
(B) Dynamic Correlation with World
Mean
Std
Max
Min
0.803
0.644
0.737
0.843
0.747
0.719
0.851
0.827
0.568
0.709
0.751
0.661
0.733
0.645
0.854
0.627
0.765
0.635
0.704
0.774
0.810
0.744
0.874
0.943
---
0.073
0.179
0.112
0.024
0.077
0.091
0.104
0.166
0.208
0.055
0.091
0.000
0.150
0.058
0.059
0.118
0.110
0.095
0.084
0.069
0.094
0.125
0.059
0.036
---
0.939
0.906
0.899
0.888
0.867
0.863
0.963
0.958
0.914
0.874
0.896
0.661
0.958
0.820
0.938
0.875
0.917
0.823
0.890
0.876
0.931
0.901
0.952
0.979
---
0.633
0.168
0.429
0.784
0.497
0.441
0.334
0.020
-0.015
0.469
0.373
0.661
0.276
0.522
0.633
0.304
0.420
0.404
0.554
0.537
0.501
0.256
0.675
0.834
---
Table A2: Summary Statistics of International Market Indices (Emerging Markets)
Country
BRAZIL
CHILE
CHINA
COLOMBIA
CZECH REPUBLIC
EGYPT
HUNGARY
INDIA
INDONESIA
KOREA
MALAYSIA
MEXICO
MOROCCO
PERU
PHILIPPINES
POLAND
RUSSIA
SOUTH AFRICA
TAIWAN
THAILAND
TURKEY
Data start
May-94
May-94
May-94
May-94
May-95
May-96
May-94
May-94
May-94
May-94
May-94
May-96
May-97
May-95
May-94
May-94
May-96
May-94
May-94
May-94
May-94
(A) Market Index Returns
(B) Dynamic Correlation with World
Constant
Mean
Std
Correlation Mean
Std
Max
Min
with World
1.388
11.260
0.665
0.665 0.132 0.854 0.316
0.770
6.679
0.600
0.602 0.052 0.706 0.468
0.347
10.469
0.518
0.556 0.189 0.809 0.123
1.049
8.313
0.415
0.388 0.151 0.674 0.052
1.033
8.244
0.575
0.507 0.259 0.846 -0.308
0.992
9.300
0.419
0.360 0.119 0.847 0.165
1.088
10.780
0.695
0.642 0.121 0.858 0.362
0.772
9.202
0.552
0.495 0.192 0.820 0.048
0.835
12.773
0.505
0.520 0.137 0.822 0.184
0.801
11.661
0.605
0.615 0.179 0.868 0.195
0.414
8.722
0.453
0.504 0.132 0.792 0.200
1.073
7.732
0.793
0.783 0.073 0.895 0.506
0.437
5.607
0.281
0.278 0.116 0.636 -0.199
1.112
8.305
0.524
0.518 0.000 0.518 0.518
0.241
9.409
0.466
0.448 0.085 0.619 0.219
0.640
11.288
0.629
0.601 0.185 0.897 0.050
2.188
14.589
0.572
0.589 0.096 0.817 0.403
0.848
7.872
0.709
0.682 0.116 0.867 0.354
0.190
8.558
0.561
0.513 0.118 0.752 0.317
0.373
10.980
0.540
0.515 0.098 0.798 0.171
1.949
15.451
0.521
0.520 0.130 0.722 0.234
28
Table A3: Summary Statistics of International Market Indices (Frontier Markets)
Country
ARGENTINA
BAHRAIN
BANGLADESH
HERZEGOVINA
BOTSWANA
BULGARIA
CROATIA
ESTONIA
GHANA
JAMAICA
JORDAN
KENYA
KUWAIT
LITHUANIA
MAURITIUS
NIGERIA
OMAN
PAKISTAN
QATAR
ROMANIA
SLOVENIA
SRI LANKA
TOBAGO
TUNISIA
UAE
ZIMBABWE
(A) Market Index Returns
(B) Dynamic Correlation with World
Constant
Data start
Mean
Std
Correlation Mean
Std
Max
Min
with World
Nov-10 -5.976 10.196
0.488
0.486 0.000
0.486
0.486
May-02 -0.122
6.596
0.378
0.298 0.057
0.483
0.234
Nov-10 -3.363 12.306
-0.222
-0.139 0.000 -0.139 -0.139
Nov-10 -1.534
7.109
0.470
0.493 0.000
0.493
0.493
Nov-10 -0.299
5.807
0.570
0.605 0.000
0.605
0.605
Nov-10 -3.361
9.550
0.681
0.687 0.000
0.687
0.687
Nov-10 -2.074
5.584
0.618
0.619 0.000
0.619
0.619
Nov-10 -0.977
8.102
0.822
0.816 0.000
0.816
0.816
Nov-10 -2.123
6.254
0.122
0.283 0.000
0.283
0.283
Nov-10
0.751
5.343
0.306
0.167 0.136
0.603
0.052
May-96
0.487
5.592
0.277
0.213 0.171
0.681
0.006
Nov-10 -1.081
7.223
0.639
0.571 0.000
0.571
0.571
May-02
0.594
6.629
0.499
0.394 0.123
0.596
0.216
Nov-10 -0.454
5.470
0.793
0.589 0.008
0.607
0.578
Nov-10 -0.401
3.332
0.383
0.421 0.000
0.421
0.421
Nov-10 -1.164
5.989
0.612
0.628 0.102
0.823
0.399
May-02
0.760
6.250
0.482
0.332 0.169
0.780
0.106
Nov-10 -0.200
5.854
0.381
0.396 0.000
0.396
0.396
May-02
1.626 10.809
0.345
0.268 0.223
0.740 -0.044
Nov-10 -0.630 11.447
0.873
0.868 0.000
0.868
0.868
Nov-10 -2.717
6.222
0.861
0.829 0.000
0.829
0.829
Nov-10 -2.892
4.034
0.109
0.149 0.000
0.149
0.149
Nov-10
1.374
3.500
0.159
0.108 0.000
0.108
0.108
Nov-10 -1.028
5.587
-0.107
-0.004 0.000 -0.004 -0.004
May-02
1.342 11.782
0.386
0.312 0.190
0.721
0.042
Nov-10 -0.082
4.965
0.000
0.005 0.000
0.005
0.005
29