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Applied Economics Letters
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On international stock market co-movements and
macroeconomic risks
a
Peng Chen & Shu Wu
a
a
Department of Economics , University of Kansas , Lawrence , KS , 66045 , USA
Published online: 12 Mar 2013.
To cite this article: Peng Chen & Shu Wu (2013) On international stock market co-movements and macroeconomic risks,
Applied Economics Letters, 20:10, 978-982, DOI: 10.1080/13504851.2013.767973
To link to this article: http://dx.doi.org/10.1080/13504851.2013.767973
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Applied Economics Letters, 2013
Vol. 20, No. 10, 978–982, http://dx.doi.org/10.1080/13504851.2013.767973
On international stock market comovements and macroeconomic
risks
Peng Chen and Shu Wu*
Downloaded by [Shu Wu] at 08:21 16 October 2013
Department of Economics, University of Kansas, Lawrence, KS 66045, USA
We use Bayesian dynamic factor models to disentangle the global and
idiosyncratic country-specific factors of the stock market prices and other
macroeconomic variables from a large group of countries. We find that the
global factors account for significant portions of an individual country’s
stock market volatility and its macroeconomic fluctuations. The global
macroeconomic shocks have strong effects on the stock price movement
across countries in addition to domestic macroeconomic shocks. And a
country’s exposure to the global stock market risk to a large extent
reflects that country’s exposure to the global macroeconomic risks.
Keywords: stock market co-movement; macroeconomic risks; dynamic
factor model
JEL Classification: F15; F36; G15
I. Introduction
Are stock markets too volatile to be justified by the
fundamental economic risks? The seminal work of
Robert Shiller (1981) has stimulated a large empirical
literature on the sources of stock market volatility.1
This article revisits this issue in the context of a partially integrated global economy. Globalization has
accelerated in the past two decades. There are rapid
increases in both cross-country financial flows and
international trade in goods and services. Nowadays
almost every single country’s macroeconomic performance and financial market are affected by the developments in the other parts of the world. This
integration of national economies can fundamentally
alter the nature of risks faced by investors and, as a
result, stock market movements. For example, in a
completely isolated economy, only the domestic market risk of that country is a priced systematic risk. In a
perfectly integrated world economy, however, only
the exposure to the global stock market risk will be
priced. Similarly, the underlying macroeconomic risks
that drive much of the stock market movements will
also be different. In an isolated economy, the macroeconomic fluctuations of that country are perhaps the
most important driving force of its stock market
movement. In an integrated world economy, an individual country’s stock market will probably respond
more to the world business cycle shocks than to its
own macroeconomic fluctuations.
Using a dynamic factor model estimated on
monthly data on a group of 34 countries from 1995
to 2009 via Bayesian methods, we extract measures of
global stock returns and global macroeconomic risks
and seek to understand how they affect stock market
volatilities across countries. The model allows us to
decompose one economic variable into a common
‘global’ factor across countries and an independent
country-specific factor. We find that these global factors account for significant portions of a country’s
*Corresponding author. E-mail: [email protected]
Some classical examples include Campbell and Shiller (1988) and Cochrane (1992) among many others. See Campbell (1999)
and Cochrane (2011) for recent reviews of relevant literatures.
1
978
# 2013 Taylor & Francis
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International stock market co-movements and macroeconomic risks
stock market volatility and its macroeconomic fluctuations and that the global macroeconomic shocks
have strong effects on stock market movements in
addition to domestic macroeconomic shocks. A country’s exposure to the global stock market risk to a large
extent reflects that country’s exposure to the global
macroeconomic risks.
This article draws upon two strands of literature.
One is the empirical literature on international business cycle and macroeconomic co-movements, including Gregory et al. (1997), Kose et al. (2003, 2008),
Canova et al. (2007), Ciccarelli and Mojon (2010)
and Crucini et al. (2011) among many others. These
papers estimate versions of dynamic factor models in
order to understand the evolution and the driving
forces of international business cycle and macroeconomic co-movements. The other strand is the empirical literature on financial market integration,
including Hamao et al. (1990), Bekaert and Harvey
(1995), Forbes and Rigobon (2002), Brooks and Del
Negro (2005), Carrieri et al. (2007), Pukthuanthong
and Roll (2009) and Bekaert et al. (2009) among many
others. These studies focus on measuring and characterizing the extent and the evolution of financial integration over time using either international asset
pricing models or econometric factor models. The
main objective of this article, however, is to investigate
empirically the macroeconomic underpinnings of the
co-movements and the volatilities of stock markets
across different countries. We use a dynamic factor
model to disentangle the global and idiosyncratic
country-specific factors in the stock price movements
and other macroeconomic variables, respectively. This
allows us to examine the link between the stock market
volatility and the underlying macroeconomic risks in a
globally integrated economy.
ft ¼ f1 ft1 þ f2 ft2 þ þ fp ftp þ ut
ei;t ¼ c1 et1 þ c2 et2 þ þ cp etp þ ui;t ;
i ¼ 1; 2; ; N
979
ð2Þ
ð3Þ
where ut and ui;t are i.i.d. Gaussian shocks with zero
mean and var ðui;t Þ ¼ s2i . For identification,2 we
assume var ðut Þ ¼ 1.
The above model can be estimated by maximum
likelihood method via Kalman filter. But given the
large dimension of the model (in our case, N ¼ 34),
conducting the numerical maximization of the likelihood function can be challenging. Instead, we estimate the model using Bayesian method via Markov
Chain Monte Carlo (MCMC). The implementation of
MCMC is explained in details in, for example, Chib
and Greenberg (1996).
We apply the model separately to four (monthly)
variables: annualized log changes in stock market
indexes, nominal short-term interest rates, growth
rates of industry production and CPI inflation rates.
Our sample includes 34 countries (N ¼ 34) and covers
the period between 1995 and 2009 as many developing
countries do not have data for earlier periods. The
sample countries include most developed countries
and many developing countries (see Tables 1 and 3).
Stock market indexes are from Datastrem
International. Other macroeconomic variables are
either from World Bank or IMF International
Financial Statistics. All series are demeaned before
the estimation. Real stock market returns are obtained
by subtracting inflation rates from the nominal
returns. When implementing the Bayesian estimation,
we fix the lag length of in Equations 2 and 3 at 2. The
Gibbs sampling was iterated 25 000 times after the
initial 5000 draws.
II. Model and Estimation
III. Main Results
Let fyi;t gN
i¼1 be a collection of an economic variable
from N countries. We assume that yi;t has the following factor structure:
The dynamic factor model (1) allows us to decompose
the variance of an economic variable from an individual country into two parts,
yi;t ¼ li ft þ ei;t
ð1Þ
where ft represents a common global factor, li is the
factor loading for country i and ei;t is an independent
country-specific shock. Both ft and ei;t are assumed to
follow an autoregressive process of order p,
2
var ðyi;t Þ ¼ l2i varðft Þ þ var ðei;t Þ
ð4Þ
The first part is attributed to a global factor while the
second part is due to an idiosyncratic country-specific
factor. The variance share of the global factor for each
country can then be calculated as:
As in the standard latent factor models, the factor loading, li , and the variance of ut are not identified separately. We also
impose the sign restriction such as li >0 for all i. See Stock and Watson (1989) for more on the identification of factor models.
P. Chen and S. Wu
980
si ¼
l2i var ðft Þ
; i ¼ 1; ; N
var ðyi;t Þ
Table 2. Correlation of global factors
ð5Þ
This exercise is repeated for the stock market return,
industry output, inflation and interest rate, and the
results are reported in Table 1. We can see that global
factors account for a significant share of the volatility
Table 1. Variance share of global factors
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Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
Denmark
Finland
France
Germany
Greece
Hong Kong
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
New Zealand
Norway
Peru
Philippines
Portugal
Singapore
Spain
Sweden
Switzerland
Taiwan
Thailand
UK
USA
Average (1)
Average (2)
Output
17.610
25.890
75.904
79.168
36.569
68.831
29.171
35.584
86.260
86.949
86.251
44.991
25.023
0.519
19.846
88.028
76.253
21.934
38.480
26.868
46.883
35.794
6.168
25.349
N/A
28.664
17.729
84.622
83.688
58.212
34.884
19.441
86.974
79.677
47.825
56.118
Inflation
0.596
40.126
71.598
67.694
1.530
44.589
32.653
32.359
36.847
67.924
62.103
17.902
9.129
3.7016
49.608
44.279
11.783
4.366
15.613
0.656
11.129
42.326
10.024
4.104
6.759
41.428
44.133
81.100
48.963
76.189
35.243
17.987
42.746
70.017
33.741
43.416
Interest
0.012
39.521
53.967
57.116
27.789
47.996
39.245
60.991
58.201
76.426
50.296
55.301
28.839
3.785
66.896
79.174
20.241
29.735
21.037
44.500
31.658
40.244
12.986
27.204
21.149
52.421
12.351
81.089
76.354
37.353
N/A
20.581
42.855
37.215
41.046
47.503
Returns
33.951
70.044
63.711
71.675
34.515
74.757
32.382
72.355
51.182
87.016
83.678
45.587
45.408
37.901
69.896
75.956
49.428
29.046
18.821
46.137
90.815
46.564
75.590
33.008
32.694
66.954
45.040
76.497
76.051
73.289
32.345
20.575
81.240
81.114
56.624
66.434
Notes: Variance share denotes the fraction of the variance of
economic variables attributable to the global factor, i.e.
l2 varðf Þ
t
i
si ¼ varðy
; where ft represents the common global factor
i;t Þ
and yi;t is the economic variable in month t for country i,
including growth rate of industrial production, inflation,
short-term interest rate and stock market real returns. The
data on industrial production of Philippines and the interest
rate of Taiwan are not available. Average (1) and (2) denote,
respectively, the average of variance shares of all countries
and of developed countries only.
3
Output
Inflation
Interest
Returns
Output
Inflation
Interest
Returns
1
0.466
0.529
0.040
1
0.517
-0.311
1
-0.081
1
Note: This table presents pair-wise correlation coefficients
among the global factors of stock returns and other macroeconomic variables extracted from the dynamic factor
models.
of each of the four macroeconomic variables. On average, about 57% of the volatility in stock market returns
can be attributed to a global factor. The variance shares
of the global factor for output, inflation and interest
rate are also large, averaging around 48%, 34% and
41%, respectively. These variance shares are larger for
advanced countries, indicating a higher degree of integration among those countries than others. These
results confirm a strong presence of a common global
factor in the stock market returns as well as in each of
the macroeconomic variables.
Table 2 includes the pairwise correlations among the
different global factors, which are the proxies of the
global stock return and global output, inflation and
interest rate. We see a similar pattern of correlations
at the global level as that at national levels. For example, the real stock return is negatively correlated with
inflation, a phenomenon that is widely documented in
the United States and other countries. Moreover, inflation and output is positively correlated, suggesting a
global Phillips Curve relation.
To see how the global macroeconomic shocks affect
stock market prices, we estimate a VAR model for
each country that includes domestic stock market
return, industry output, inflation and interest rate.
We also include in each VAR model the global factors
of output, inflation and interest rate. The order of the
VAR is domestic macroeconomic variables, global
macroeconomic factors and domestic stock market
return. We report in Table 3 the Cholesky variance
decomposition (after 12 lags) for stock market returns.
We can clearly see that the global macroeconomic
shocks account for a significant portion of the stock
volatility for many countries in addition to domestic
macroeconomic shocks. For example, while domestic
macroeconomic shocks jointly account for 14%
of the variance of the US stock returns, the global
macroeconomic shocks account for an additional
20% of the variance of the US stock returns.3 The R2
reported in Table 3 imply that a standard F-test would
Note that our Cholesky ordering gives a conservative estimate of the effect of global macroeconomic shocks on stock market
returns relative to that of domestic macroeconomic shocks.
International stock market co-movements and macroeconomic risks
Table 3. Variance decomposition of stock returns
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Country
Argentina
Australia
Austria
Belgium
Brazil
Canada
Chile
Denmark
Finland
France
German
Greece
Hong Kong
Indonesia
Ireland
Italy
Japan
Korea
Malaysia
Mexico
Netherlands
Norway
Peru
Portugal
Singapore
Spain
Sweden
Switzerland
Thailand
UK
USA
Average (1)
Average (2)
Macro_C
7.535
10.94
9.216
7.726
4.547
9.766
5.891
11.41
6.206
10.726
7.599
12.839
10.133
13.357
16.861
5.091
8.815
20.811
4.817
4.535
17.87
14.122
2.726
5.172
15.033
8.269
5.928
10.786
19.000
4.392
13.907
9.872
10.128
Macro_G
9.360
27.366
10.630
8.614
3.597
10.734
6.919
16.854
7.569
5.256
4.696
20.080
18.425
11.095
22.770
12.721
18.138
7.398
3.454
12.210
13.665
5.743
12.833
15.276
9.033
12.341
8.277
16.065
4.613
9.434
19.066
11.749
13.307
R2r
R2u
0.221
0.292
0.240
0.190
0.170
0.206
0.225
0.245
0.228
0.218
0.189
0.341
0.240
0.425
0.302
0.222
0.111
0.355
0.212
0.178
0.228
0.229
0.208
0.211
0.342
0.215
0.190
0.246
0.294
0.081
0.280
0.236
0.229
0.304
0.501
0.330
0.292
0.208
0.289
0.219
0.391
0.298
0.276
0.263
0.520
0.411
0.496
0.546
0.322
0.318
0.379
0.242
0.302
0.432
0.300
0.298
0.328
0.406
0.337
0.245
0.398
0.354
0.191
0.440
0.343
0.356
Notes: This table reports the Cholesky variance decomposition for domestic stock market returns. Macro_C and
Macro_G denote, respectively, the fraction of the variance
a country’s stock market fluctuations attributable to the
domestic and global macro shocks. Column 3 represents
R2 of VAR that restrict the coefficients for global macro
variables to be 0, and Column 4 represents R2 of unrestricted
VAR. Average (1) and (2) denote, respectively, the average
of all countries and developed countries only.
reject the zero coefficients on the global macroeconomic factors for many countries in these regressions.
981
These results suggest that in an increasingly integrated
global economy, we have to look beyond national
borders in order to correctly identify and measure
the underlying macroeconomic risks in financial markets. We may miss important sources of macroeconomic risks if we only use domestic macroeconomic
variables in our empirical studies.
There are large variations in the variance shares
of the global factor of stock market returns across
countries as Table 1 indicates. For example, while
the global factor contributes to more than 80% of
the total stock market volatility in the United States
and United Kingdom, it only contributes to about
20% of the stock market volatility in Thailand and
Malaysia. Interestingly, these variations in the variance shares for stock market returns are positively
and highly correlated with variations in the variance
shares for output (58%), inflation (65%) and interest rate (60%) across countries. A simple regression
(Table 4) shows that the variance shares for the
macroeconomic variables jointly account for almost
half of the variations in the variance shares for
stock market returns. Countries that are more
(less) exposed to the global factor of the stock
market returns are also more (less) exposed to the
global factors of the macroeconomic variables. In
other words, a country’s exposure to the global
stock market risk to a large extent reflects that
country’s exposure to the global macroeconomic
risks.
IV. Conclusion
We conduct a simple empirical exercise to disentangle
the global and idiosyncratic country-specific factors of
the stock market prices and other macroeconomic
variables from a large group of countries. Our results
suggest that it is important to look beyond national
borders in order to understand the macroeconomic
underpinnings of financial market risks in an increasingly integrated global economy. Macroeconomic
risks, when properly identified, have strong impact
on stock market (co-)movements.
Table 4. Accounting for the variance shares of stock returns
Explanatory variables
Output
Inflation
Interest
Constant
R2
Coefficients
0.142 (0.333)
0.321(0.059)
0.199 (0.300)
31.890
0.483
Notes: This table reports the estimate of OLS regression of Si ¼ b0 þ b1 Si;1 þ b2 Si;2 þ b3 Si;3 þ ei ; where Si is the variance share
of global factor for stock returns, and the explanatory variables (Si;1 , Si;2 and Si;3 ) are the variance shares of global macroecnomic factors for industrial production, inflation and interest rate. p-Values are presented in parentheses.
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References
Bekaert, G. and Harvey, C. R. (1995) Time-varying world
integration, Journal of Finance, 51, 403–44.
Bekaert, G., Hodrick, R. J. and Zhang, X. (2009)
International stock returns comovements, Journal of
Finance, 64, 2591–626.
Brooks, R. and Del Negro, M. (2005) A latent factor model
with global, country, and industry shocks for international stock returns, IMF Working Papers No. 0552,
Washingto, DC.
Campbell, J. (1999) Asset prices, consumption, and the business cycle, in Handbook of Macroeconomics (Eds.) J. B.
Taylor and M. Woodford, Elsevier, Amsterdam, Vol. 1,
pp. 1231–303.
Campbell, J. and Shiller, R. (1988) Stock prices, earnings,
and expected dividends, Journal of Finance, American
Finance Association, 43, 661–76.
Canova, F., Ciccarelli, M. and Ortega, E. (2007) Similarities
and convergence in G-7 cycles, Journal of Monetary
Economics, 54, 850–78.
Carrieri, F., Errunza, V. and Hogan, K. (2007) Characterizing
world market integration through time, Journal of
Financial and Quantitative Analysis, 42, 915–40.
Chib, S. and Greenberg, E. (1996) Markov chain Monte
Carlo
simulation
methods
in
econometrics,
Econometric Theory, 12, 409–31.
Ciccarelli, M. and Mojon, B. (2010) Global inflation, The
Review of Economics and Statistics, 92, 524–35.
Cochrane, J. (1992) Explaining the variance of price dividend ratios, Review of Financial Studies, 5, 243–80.
Cochrane, J. (2011) Discount rates, Journal of Finance, 66,
1047–108.
P. Chen and S. Wu
Crucini, M., Kose, M. A. and Otrok, C. (2011) What are
driving forces of international business cycle?, Review of
Economic Dynamics, 14, 156–75.
Forbes, K. and Rigobon, R. (2002) No contagion, only
interdependence: measuring stock market co-movements, Journal of Finance, 57, 2223–61.
Gregory, A. W., Head, A. C. and Raynaud, J. (1997)
Measuring world business cycles, International
Economic Review, 38, 677–701.
Hamao, Y. R., Masulis, R. W. and Ng, V. K. (1990)
Correlations in price changes and volatility across international stock markets, Review of Financial Studies, 3,
281–307.
Kose, A. M., Otrok, C. and Whiteman, C. H. (2003)
International business cycles: world, region and country specific factors, American Economic Review, 93,
1216–39.
Kose, A. M., Otrok, C. and Whiteman, C. H. (2008)
Understanding the evolution of world business cycle,
Journal of International Economics, 75, 110–30.
Pukthuanthong, K. and Roll, R. W. (2009) Global
market integration: an alternative measure and its
application, Journal of Financial Economics, 94,
214–32.
Shiller, R. (1981) Do stock prices move too much to be
justified by subsequent changes in dividends?,
American Economic Review, 71, 421–36.
Stock, J. and Watson, M. (1989) New indexes of coincident
and leading economic indicators, in NBER
Macroeconomics Annual 1989 (Eds.) O. Jean
Blanchard and S. Fischer, The MIT Press, Cambridge,
pp. 351–94.