Discussion Papers In Economics
And Business

Do International Investors Cause Stock Market Comovements?
Comparing Responses of Cross-Listed Stocks between Accessible andInaccessible Markets
Yusaku NISHIMURA
Yoshiro TSUTSUI
Kenjiro HIRAYAMA

Discussion Paper 17-01

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDo International Investors Cause Stock Market Comovements?
Comparing Responses of Cross-Listed Stocks between Accessible andInaccessible Markets
Yusaku NISHIMURA
Yoshiro TSUTSUI
Kenjiro HIRAYAMA

Discussion Paper 17-01

January 2017

Graduate School of Economics andOsaka School of International Public Policy (OSIPP)
Osaka University, Toyonaka, Osaka 560-0043, JAPANDo International Investors Cause Stock Market Comovements?
Comparing Responses of Cross-Listed Stocks between Accessible andInaccessible Markets
Yusaku NISHIMURA a
Yoshiro TSUTSUI b
Kenjiro HIRAYAMA c

Abstract
This study provides evidence that international stock investors’ transactions are a cause of stock
market comovements. We analyze return and volatility spillovers between eight major stock
markets and stocks cross-listed on an accessible market (H-shares in Hong Kong) and an
inaccessible market (A-shares in mainland China) by applying the spillover indexes proposed by
Diebold and Yilmaz (2012, 2014) to those markets. Results suggest that spillovers of both returnand volatility are greater in an accessible market than in an inaccessible one. We also find thatspillover effects intensify as openness of a stock market increases.
Keywords: investor-induced hypothesis; fundamentals-based hypothesis; cross-listed stocks;
spillover index; inaccessible market
JEL Classification: G15aInstitute of International Economy, University of International Business and Economics, 10 East
Huixin Street, Chaoyang District, Beijing, 100029, China, e-mail: yusaku.nishimura@gmail.combFaculty of Economics, Konan University, 1-8-9 Okamoto, Higashinada-ku, Kobe, 658-8501,
Phone & Fax: +81-78-435-2920, e-mail: tsutsui@center.konan-u.ac.jpcSchool of Economics, Kwansei Gakuin University, Uegahara, Nishinomiya-shi 662-8501 Japan,
Phone: +81-798-54-6438, Fax: +81-798-54-6438, e-mail: hiraken@kwansei.ac.jp1Introduction
Global integration of national economies, as well as financial deregulation in major countries sincethe 1980s, has strengthened relationships among international stock markets, which led to closer
comovements of stock prices across national borders. Global stock market selloffs caused byevents such as a calamitous stock price plunge in New York (Black Monday of October 19, 1987),
Shanghai (February 27, 2007), and again New York (September 29, 2008) have occurred as well.
These events represent cases in which an incident occurring in one market had an immediate andwidespread effect on global markets. As such, they aroused interest among economists to explore
empirical and theoretical questions posed by stronger stock price comovements.
One hypothesis to explain such comovements is based on fundamentals (fundamentalsbased hypothesis), and posits that, given a frictionless economy and rational investors, stock prices
are determined by fundamentals and, thus, stock price comovements are solely caused by
comovements in fundamentals (Barberis et al., 2005).
Another hypothesis is based on investor behavior (investor-induced hypothesis), andmaintains that stock market comovements are caused by the behavior of international investors,
such as adjustments in international portfolios. Theoretical models have been developed to explain
the phenomenon in which stock price changes in one country lead to changes in other markets
through portfolio holdings of international investors. As such, Kyle and Xiong (2001) assert that
big losses incurred in a market plunge will result in selloffs in other markets because investors
unwind positions to cover losses. Moreover, Kodres and Pritsker (2002) propose a theoreticalmodel that shows propagation of crisis situations through portfolio adjustments involving several
different stock markets. Hong and Stein (2003) show that new information that results in largescale portfolio reallocations will cause international price changes. Finally, Mondria and Quintana

2

- Domeque (2013) find that shocks in a given market will cause investors to allocate too muchattention to that market and that their portfolio reallocation helps spread the effects on othermarkets.
The aforementioned theoretical research hypothesizes that international investors’ behavior
causes stock market comovements, but there is little empirical research on this hypothesis. The
limited empirical research tends to focus on periods of contagion following a major financial crisis,and not on comovements during normal periods (e.g., Boyer et al., 2006, Petmezas and Santamaria,2014).1 Consequently, one of the purposes of this study is to determine the factors that explaininternational stock market comovements in normal periods and not during crisis periods.
The question is how to determine which of the two hypotheses is valid in explaining
comovements. As such, we consider two different stock markets: one that is open to international
investors (accessible market) and one that is not (inaccessible market). We subsequently focus onstocks cross-listed on these two markets. By analyzing how these cross-listed stocks react tochanges on a major foreign market such as the U.S. stock market, we can verify which hypothesis
is supported. If the fundamentals-based hypothesis is valid, changes in fundamentals affecting
American firms will have an impact on domestic firms, but the degree of response must be identical
for the two markets. Alternatively, if the investor-induced hypothesis is correct, while the prices in
the inaccessible market remain unaffected because of the absence of international investors, stock
prices on the accessible market are affected by portfolio adjustments. In this instance, international
stock market comovements are observed only on the accessible market. Examining whether

1

In this paper, comovement of stock prices is defined as a phenomenon in which a rise (fall) in return/volatility
on one market leads to a similar rise (fall) in return/volatility on other markets. Comovement is different fromcontagion which is defined by Forbes and Rigobon (2002) to signify “a significant increase in cross-market
linkages after a shock to one country (or group of countries).”3

comovements are observed equally in these cross-listed stocks enables us to determine which of
the two hypotheses is valid in explaining international stock price comovements. A newcontribution of this study is the method of focusing on differential responses of cross-listed stocks
on an accessible and an inaccessible market.
Another analytical framework is exploring the effects of opening an inaccessible market toforeign investors. If the fundamentals-based hypothesis is correct, the spillover effects on theinaccessible market will remain unchanged after the market opens for foreign investors, because
stock prices in such a market are determined with their fundamental values. However, if theinvestor-induced hypothesis holds true, the opening of an inaccessible market will increase thedegree of international price linkage on this market.
In order to execute the above tests, we use a unique feature of Chinese stock markets: theexistence of two independent stock markets with differing degrees of international openness. Aninternationally accessible market is provided by the H-share market in Hong Kong and theinaccessible market by the A-share market in mainland China (Shanghai and Shenzhen). 2 We
investigate the stocks of 86 companies that are cross-listed on these two markets. The stock of across-listed company is subject to the same fundamentals and external shocks (e.g., changes inregulation, shocks idiosyncratic to the industry, etc.), and the only difference between the twolistings is whether the stock is purchasable by international investors or not. Because A-sharescannot be bought/sold by foreign investors,3 if the H-share price more strongly comoves with a2

There is a B-share market in mainland China accessible to foreign investors. B-shares are specifically issued
for foreign investors and denominated in a foreign currency (US dollars in the case of the Shanghai Stock
Exchange and Hong Kong dollars in the case of the Shenzhen Stock Exchange). However, the market
capitalization of B-shares represented only 0.46% of the entire market capitalization (A + B shares) at the endof 2014. Issuance of the two types of shares at the same time was banned in 1998, and issuance of B-shares has
all but disappeared since then. Therefore, we ignore B-shares in our analysis.
3 A-shares can also be purchased by the so-called Qualified Foreign Institutional Investors (QFII) and RMB

4

major foreign stock market than the A-share price, it can be interpreted as reflecting the behavior
of international investors.
We attempt to analyze the average responses for the 86 stocks that are listed on both the Ashare and H-share markets. Therefore, we need to compile a stock price index composed of thesestocks. As such, we obtain tick data on them and extract prices at five-minute intervals. We
subsequently compute the capitalization-weighted average of those prices to produce the desired
price index for cross-listed stocks.
We estimate return and volatility spillovers between the two Chinese markets and major
foreign stock markets, respectively. Preceding works in the field used various methods to estimate
return and/or volatility spillovers. Our study adopts a new spillover index4 proposed by (Diebold
and Yilmaz (2012, 2014). This is a summary measure of forecast-error variance decompositions
using vector autoregressions (VAR), which captures, in a simple manner, spillovers between
markets as a whole and offers information on the magnitude and direction of spillovers.
The remainder of this paper is structured as follows: section 2 discusses related literature;
section 3 explains the hypotheses we consider and the methodologies used to test the hypotheses;
section 4 outlines the data used; section 5 discusses the estimation results; and section 6 concludes
our paper.

1. Literature Review
Studies on the investor-induced hypothesis include Boyer et al. (2006) and Petmezas andQualified Foreign Institutional Investors (RQFII). However, the actual amount of investment officially permitted
is miniscule. Specifically, the amount allocated to the QFII is 72.15 billion US dollars and that to RQFII is 53.7
billion US dollars, amounting to only 0.21% of the market capitalization. Consequently, we can ignore
international investors on the A-share market.
4 Note that Diebold and Yilmaz (2014) refer to this index as the connectedness index.

5Santamaria (2014). Boyer et al. (2006) examine whether transmission of the Asian financial crisisof 1997 to other markets occurred through stock holdings of international investors or throughchanges in fundamentals. As there are certain emerging markets not accessible to foreign investors,
they compare responses on an accessible market with those on an inaccessible market for plunging
stock prices in the crisis country (Thailand). The result is that the former is larger than the latter,
implying that stock market contagion is more likely caused by investor behavior than by thecommon effects of fundamentals. Therefore, investor-induced contagion is more plausible than thefundamentals-based contagion. Their focus on different responses on accessible and inaccessible
markets is interesting, but not without problems. They are not controlling for the effects offundamentals. In our study, however, we analyze cross-listed stocks in two markets with or without
accessibility to foreign investors. Since they are subject to the same fundamentals, we do not haveto control for different fundamentals. While Boyer et al. (2006) focus on the issue of contagion
during a major financial crisis, this study analyzes international stock market comovement periods
without major crises. Moreover, Petmezas and Santamaria (2014) emphasize the wealth andportfolio-rebalancing effects as the cause of investor-induced contagion, and compare these effects
during the global financial crisis, between 2007 and 2012, by analyzing correlations between stockand bond markets.5
This study also investigates spillover effects of return and volatility. Early research in this5

Additionally, there are studies which analyze the relationships between macroeconomic news and stock market
comovements. King et al. (1994) conclude that observable economic variables (e.g., interest rates, industrial
production, inflation, etc.) can explain only a small part of international stock market comovements. Connolly
and Wang (2003) show that the bulk of the observed comovement in intraday and overnight returns on the U.S.,
the U.K., and Japanese stock markets cannot be attributed to public information about economic fundamentals.
Albuquerque and Vega (2009) find that the U.S. macroeconomic news does not affect stock market comovement
between the U.S. and Portugal. Consequently, we can conclude that we have not found clear evidence thatmacroeconomic news cause stock market comovements.

6

field focused on returns only, such as studies by Eun and Shim (1989), Jeon and Von Furstenberg(1990), Cheung and Mak (1992), Janakiramanan and Lamba, (1998), Leong and Felmingham(2003). More recent studies analyze volatility spillovers in addition to return spillovers, including
Hamao et al. (1990), Ng (2000), Bae et al. (2003), Baur and Jung (2006); Diebold and Yilmaz(2009), Mukherjee and Mishra (2010). Simultaneous analysis of return and volatility spillovers
has become the mainstream research approach in this field. Although precise methods, markets foranalysis, and sample periods vary greatly among these studies, the following consensus exists
among researchers. (1) Significant return and/or volatility spillovers across national borders areobserved among stock markets. (2) Spillovers from more advanced economies, especially the U.S.,
to other smaller markets are clearly observed. (3) Return and volatility spillovers intensify
significantly during global financial crises, such as the Mexican Peso crisis of 1994, the Asianfinancial crisis of 1997, and the global crisis triggered by the demise of the Lehman Brothers in
2008. Hitherto, a plethora of studies exists on spillover effects, but few have attempted to focus on
the causes of these effects. Consequently, this study is an endeavor to fill this gap.
Regarding studies that examine both return and volatility spillover effects, we notice that
estimation results vary widely, namely results on return spillovers differ from those on volatility
spillovers. For example, Hamao et al. (1990) analyze return and volatility spillovers among theTokyo, London, and New York stock markets. They report significant volatility spillovers fromNew York to Tokyo, London to Tokyo, and New York to London, but return spillovers were lesssignificant. On the other hand, Baur and Jung (2006) analyze spillovers between daytime returns
in New York and overnight returns in Frankfurt and report significant return spillovers, but notvolatility spillovers (Table 3, p.606). Spillover effects between stock markets arise through twochannels: one, which affects returns and the other, which affects volatility. Moreover, we have no7

prior information as to which channel should be more significant, and, as such, we pay close
attention to the relative importance of these channels.
This study belongs to a line of research on stock price comovements of cross-listed stocks.
Most studies in this field focus on price discovery (e.g., Eun and Sabherwal, 2003; Su and Chong,2007), but a few analyze international stock price spillovers. Gagnon and Karolyi (2009) analyze
spillovers between stock prices of American firms and those on 36 other markets. They attempt tomeasure the effects of macroeconomic news on different stocks, but when using closing prices
observed in other markets, time lags between them and the closing hour in New York prevent us
from isolating the effects of news releases, because new information arises in the intervening hours.
However, American depositary receipts (ADRs) are traded in New York, and, thus, enable us to
avoid this timing problem. In this study, we focus on the H-share and A-share markets in China
with or without accessibility to foreign investors, and compare the effects of the New York stock
prices on these markets. This comparison is expected to provide evidence on the causes of returnand volatility spillovers. Therefore, the reason for analyzing cross-listed stocks in this study isdifferent from that of Gagnon and Karolyi (2009).
There are studies that analyze stocks cross-listed in A-share and H-share markets. However,there was a large price differential between the two markets (H-shares were much lower in pricethan A-shares), which was called a price disparity puzzle. Arquette et al. (2008) conclude that thisdisparity was caused by differences in expectations of exchange rate changes and in market
sentiments. Additionally, Chung et al. (2013) conclude that this disparity arises due toinformational asymmetry and market segmentation.
Subsequently, we need to determine whether the two prices are unrelated Su et al. (2007),
analyze 29 stocks cross-listed in the A-share and H-share markets, and conduct cointegration tests.

8

It is notable that the extent of cointegration between A-shares and H-shares has intensified in manycases after the Closer Economic Partnership Arrangement between Hong Kong and mainland
China. Cai et al. (2011) utilize a Markov switching error correction model to analyze the long-term
discount on H-shares, short-term comovements between A-shares and H-shares, and the intensityof error correction. They find that comovements between A-shares and H-shares have strengthenedin the presence of higher economic integration between Hong Kong and mainland China. Sun etal. (2013) report a similar result because of a rising presence of mainland Chinese firms in HongKong.6
While the above studies analyze comovements between A-share and H-share price indexes,
this study analyzes spillover effects from foreign markets to shares that are cross-listed in mainland
China and in Hong Kong to investigate the cause of international stock price comovements.

2. Method
3.1 Hypotheses
First, we compute and compare the magnitudes of the spillover indexes between foreign stock
markets and either an accessible or an inaccessible market in China. If price comovements arecaused by common changes in fundamentals (fundamentals-based hypothesis), the spillover mustbe identical across the two Chinese markets. However, if the comovements are caused by investor
behavior (investor-induced hypothesis), the spillover effect will become stronger in an accessible
market than in an inaccessible one. Therefore, if the spillover index measuring spillovers fromforeign markets to an accessible market is greater than that from foreign markets to an inaccessible

6

Instead of cross-listed stocks, Qiao et al. (2008) analyze comovements between the stock price indexes ofHong Kong and mainland China. They report a one-way volatility spillover from mainland China to Hong Kong.

9

market, the investor-based hypothesis is supported. Our working hypothesis is that stock market
spillovers are caused by international investors. Consequently, we put forward the following
testable hypothesis:
Hypothesis 1: The spillover index from foreign markets to an accessible market ( S A ) is greaterthan that from foreign markets to an inaccessible market ( SIA ).
As previously mentioned, if this hypothesis is accepted, it implies that spillovers are caused byinternational investors.
Second, we analyze the effects of opening an inaccessible market. If the fundamentalsbased hypothesis is valid, changes in the number of international investors would not, ceterisparibus, affect spillover effects. Moreover, if spillovers are caused by investor behavior, an
increase in international investors in the hitherto inaccessible market would raise the extent of
spillovers into this market. During the sample period analyzed in this study, there was an importantderegulatory change on November 17, 2014, called the Shanghai-Hong Kong Stock Connect
Program. Under this Program, international investors can purchase some of the stocks (component
stocks in the SSE 380 Index and those cross-listed in Shanghai and Hong Kong) listed at the
Shanghai Stock Exchange through the Hong Kong Stock Exchange. We analyze changes in thespillover index following this substantial deregulation of the inaccessible market to obtainevidence on the cause of spillover effects.
Hypothesis 2A: The spillover index of the inaccessible market after opening to international
investors ( S IA,after ) is higher than that before SIA,before ).
If this hypothesis is accepted, it implies that stock market spillovers are caused by international
investors, ceteris paribus. We subsequently relax this assumption and attempt to confirm whether10we obtain the same result with changes in underlying conditions. Hypothesis 2A focuses only on
the inaccessible market. As such, we infer that the accessible market should see no or little change
in spillovers after deregulation. We then compare the spillover indexes of the inaccessible and theaccessible market before and after the Shanghai-Hong Kong Stock Connect Program. The
following hypothesis must hold even if other things change, as far as those changes arise equallyin the two markets.
Hypothesis 2B: The relative change in the spillover index of the inaccessible market( SIA,after / SIA,before ) is greater than the relative change in the spillover index of the accessible market( S A,after / S A,before ).
If this hypothesis is accepted, it implies that stock market spillovers are caused by international
investors.
Even under the Shanghai-Hong Kong Stock Connect Program, not all stocks listed in
Shanghai were available for trading by foreign investors. If transactions by international investorscause spillovers, liberalization of the hitherto restricted A-shares market should cause spillover
effects in stocks that became available to international investors more than in stocks inaccessible
to foreigners. We thus compare the relative change in spillover indexes of the permitted andunpermitted stocks before and after the Shanghai-Hong Kong Stock Connect Program.
Hypothesis 2C: The relative change in the spillover index for newly permitted stocks( SP,after / SP,before )is greater than that for stocks that remain unpermitted to foreigners( SUP,after / SUP,before ).
If this hypothesis is accepted, it implies that stock market spillovers are caused by international
investors.

11

2.2 Spillover Index
To measure the stock price comovement mechanism, we employ the spillover index proposed by
Diebold and Yilmaz (2009, 2012, 2014). The estimated spillover index is used to test our
hypotheses on the cause of comovements. In preceding studies on international stock price
comovements, researchers have applied long-run analyses using level data, as well as short-run
analyses utilizing first differences of the price data. Namely, long-run analyses were conducted incointegration analysis proposed by Engle and Granger (1987) or in multivariate system
cointegration analysis proposed by Johansen (1988) and Johansen and Juselius (1990). The lag
augmented-VAR (LA-VAR) model proposed by Toda and Yamamoto (1995) utilizes levels oforiginal price data, and we can examine long-run relationships among these prices. Other methods
have also been developed to analyze first differences of stock prices. Early research employed
Granger’s causality tests (Granger, 1969) in order to investigate return spillovers. Research onvolatility spillovers followed, and the main-stream model was the autoregressive conditional
heteroscedasticity model (ARCH) proposed by Engle (1982) and the extended ARCH-typemodels.7 The cross correlation function (CCF) approach developed by Cheung and Ng (1996) cantest causality in mean and variance among variables simultaneously. However, in recent years,
economists have turned to the spillover indexes proposed by Diebold and Yilmaz (2009, 2012,2014) to examine various issues regarding spillover effects (e.g., Bubák et al., 2011; Zhou et al.,2012; Cronin, 2014; Lucey et al., 2014; Grobys, 2015). This study also applies this method to

7

In addition to univariate ARCH-type models above, multivariate ARCH-type models are also widely used in
the literature. For instance, there is the BEKK model by Engle and Kroner (1995). A Dynamic Conditional
Correlation model is developed by Engle (2002). For these univariate and multivariate ARCH-type models, thereader is referred to Bollerslev et al. (1994) and Xekalaki and Degiannakis (2010).

12

analyze return and volatility of spillover effects.
The spillover index was first proposed by Diebold and Yilmaz (2009), and it utilizes forecast
errors as computed by a VAR model. Variance decomposition of forecast errors gives us the shareof variance which is explained by the shock to a certain variable, and “it can be used to measure
the spillovers in returns or return volatilities across individual assets, asset portfolios, asset markets,
etc., both within and across countries” (Diebold and Yilmaz, 2012, pp. 57-58). However, thisprocedure has certain issues. Shocks that drive the VAR system must be orthogonal in order to beable to compute variance decomposition. If they are correlated, we cannot reach a clear-cut
decomposition. To achieve this goal, Diebold and Yilmaz (2009) apply a Cholesky decompositionof shocks, which makes results dependent upon the ordering of variables. Consequently, their
method allows us only to analyze total spillover effects (i.e., spillovers from one market to all othermarkets or from all other markets to one market). However, by utilizing the generalized VAR
model of Koop et al. (1996) and Pesaran and Shin (1998), Diebold and Yilmaz (2012, 2014)
propose a new spillover index, which can capture directional spillovers (from/to a particularmarket) independent of the causal ordering of variables (see Appendix A for details). This study
uses this new spillover index to investigate spillovers, their magnitudes, and directions.

3. Data
We use two types of stock price indexes of stocks cross-listed in both the A-share market (mainland
China) and the H-share market (Hong Kong) between January 2, 2012 and February 27, 2015. We
also use eight representative stock price indexes from major economies. Since the stock price index
of the Chinese cross-listed shares is not available, we compiled two types of indexes (see Appendix
B for specific compilation detail). The cross-listed stocks are quoted in Renminbi (RMB) in13mainland China, thus the index measured in RMB is denominated as the AHA index. The index
measured in Hong Kong dollars is called the AHH index. Since international investors holdwidely-diversified portfolios across different economies, as a reference index of global stock
markets, we employ U.S.’s S&P500 Index (USA), U.K.’s FTSE100 (GBR), Japan’s Nikkei225(JPN), Germany’s DAX (DEU), France’s CAC40 (FRA), Taiwan’s TWSE (TWN), India’s
SENSEX (IND), and Australia’s AS51 (AUS).8
We follow Diebold and Yilmaz (2009) in using weekly data, and define a weekly returnutilizing closing prices of Fridays.9 One of the reasons for using weekly data is due to different
closing hours of national stock exchanges. On a calendar day, t, Chinese stock exchanges open
and close prior to the opening of the New York Stock Exchange. Therefore, there cannot be
spillovers from the U.S. to China on day t. There is also the day-of-the-week effect of daily data.
Lo and MacKinlay (1988) write: “Weekly sampling is the ideal compromise, yielding a large
number of observations while minimizing the biases inherent in daily data” (p.27).
Descriptive statistics of weekly returns are displayed in Table 1. Every mean takes a positive
value in these markets, indicating a rising trend during the sample period. Standard deviations are
the highest for AHA and AHH returns.
[Insert Table 1 about here]
Since volatility of stock returns is not directly observable in the market, it has to be
estimated. We employ realized volatility (RV) as a measure of this volatility. This concept was
proposed by Andersen and Bollerslev (1998), and has a higher precision than other estimates.10
8

Data on foreign markets were all obtained through Bloomberg.
If price data on a Friday is missing due to a national holiday or other reasons, we copied the previous day’svalue to Friday, following Jeon and Von Furstenberg (1990).
10 Andersen et al. (2003), Koopman et al. (2005), and Angelidis and Degiannakis (2008) have compared various
volatility estimates and concluded that the RV is a better predictor of volatility relative to the ARCH-type and
914The RV on day t is an accumulated sum of intraday returns:n

RVt   rt 2(i ) .

(1)

i 1

If the number of intraday observations, n, is sufficiently large, RVt is a consistent estimator of
the true volatility given certain conditions (e.g., Andersen et al., 2001; Barndorff-Nielsen andShephard, 2002; Andersen et al., 2003).
However, there are questions to be tackled before obtaining RV using intraday data. The
first is that intraday prices are subject to microstructure noise.11 The higher the frequency of daily
observations, n, the larger the effect of microstructure noise on estimates of volatility, creating adeviation from true volatility. As such, we need to search for the optimal frequency of intraday
data, minimizing the effects of microstructure noise. Many researchers, such as Andersen et al.
(2001) and Koopman et al. (2005) have applied the five-min frequency to solve this problem andwe also follow this procedure.
Another problem with intraday data is how to treat the overnight return from the previous
day’s closing price to the current day’s opening. We cannot compute high-frequency returns during
the overnight period when there is no trading. Consequently, the close-to-open return is based ona long period of time that would add noise to RV. However, if we simply exclude it fromcomputation, that would underestimate RV. Hansen and Lunde (2005) offer a solution by using theequation below:

stochastic volatility (SV) models.
11 A well-known example of microstructure noise is the bid-ask bounce. Since transactions occur either at ask
or bid prices, the transaction price tends to fluctuate between them, resulting in alternatively positive andnegative returns. Hence, returns data has an apparent negative first-order autocorrelation. For more details see
Campbell et al. (1997), Chapter 3.

15 ( R  R) RV

 RV
T

RVt

( HL )

t 1
T

t 1

2

t

t ( intra ),(2)t ( intra )where Rt is the daily return, R is the mean of daily returns, and RVt (intra ) is RV without
overnight returns. This study utilizes weekly data to measure spillover effects and, thus, we
compute the weekly average of daily RV series given by equation (2). Numerous studies reportthat the logarithm of RV approaches a normal distribution, and it is now a common practice to use
log RV as a measure of volatility in model estimations (e.g., Andersen et al., 2001; Andersen et al.,
2003; Koopman et. al., 2005), which we will also follow in this study.
Table 2 shows the descriptive statistics on weekly volatility measures. The means of AHA
and AHH are the highest, which signify that mainland Chinese and Hong Kong stock markets werethe most volatile in this group of markets. This is consistent with high standard deviations ofreturns in these markets, as per Table 1. Volatilities in European and American markets are
generally lower.
[Insert Table 2 about here]

4. Estimation Results
This section presents the estimation results of the four hypotheses put forward in section 3. Section
5.1 discusses spillovers between major global markets and accessible and inaccessible markets in
China, while section 5.2 discusses the effects of market liberalization, which opens the hitherto
closed market to foreign investors.

5.1 Test Results of Hypothesis 1
Tables 3 and 4 (spillover tables) exhibit the spillover indexes proposed by Diebold and Yilmaz(2009, 2012). The 10x10 matrix in the upper-left hand corner is a variance decomposition matrix,

16

D H  [dijH ] , and each (i, j) element shows the spillover effect from market j (column) to market i(row). For instance, the first row, second column element is 25.09, which means the spillover effectin returns from AHH (price index of cross-listed stocks in Hong Kong) to AHA (price index ofcross-listed stocks in mainland China) amounts to 25.09%. The greater this number, the greater
the spillover effect. Numbers given in the right-most column, labeled “Directional FROM others(off-diagonal row sums),” display the Spillover Index, SiH , which measures the spillover effectfrom all other markets to market i (equation (A.4) in Appendix A). For example, the element in
the first row, last column is 43.78, which indicates the effect of all other markets on the return in
AHA is 43.78%. The antepenultimate row, labeled “Directional TO others (off-diagonal column
Hsums),” gives the spillover index from market i to all other markets, S
i (equation (A.5) inH
HAppendix A). The penultimate row, labeled “TO-FROM,” is defined as SiH  S
i  Si  , which

gives the net spillover effect of market i on other markets. The last row, labeled “TO+FROM,”H
gives the sum of SiH and S
i , which measures the degree of interrelationships between

market i and other markets.
[Insert Table 3 about here][Insert Table 4 about here]
The spillover indexes in Tables 3 and 4 are computed with all data points in the sample.12
The indexes of AHA for the inaccessible market (mainland China) and that of AHH for theaccessible market (Hong Kong) are composed of the cross-listed firms. Therefore, they are subject
12

The lag order p of the VAR(p) model is determined so as to minimize the Akaike information criterion (AIC)
by varying p from 1 to 10. The lag order is 1 in both return and volatility models. All spillover indexes in thispaper are based on a forecasting horizon of two periods. This horizon was varied from 1 to 10 periods, and thecomputed spillover indexes similar to the ones we display in this paper.

17

to identical fundamentals, which means these indexes will adjust the same way to changes infundamentals such as policy changes and downgrading of profit perspectives. Obviously, these
two indexes are closely linked and, to capture the effects from other major markets, it would bedesirable to remove this part of cross effects within China. The results of this removal are given
between parentheses in the cells involving either AHA or AHH. After removal, the spillover index
H
AHA, S AHA(shown in the last column, first row of the matrix) is 18.68% in the case of returns
(Table 3) and 7.53% in the case of volatility (Table 4). The same spillover index of AHH is 37.46%
in the case of returns (Table 3) and 33.57% in the case of volatility (Table 4). As these numbers
clearly indicate, spillovers to AHH are much higher than those to AHA, indicating that Hypothesis
1 is supported. Namely, stocks in the accessible market are more closely linked to major foreign
markets than those in the inaccessible market. Moreover, the result is consistent with the investorinduced hypothesis rather than the fundamentals-based hypothesis.
In numerous preceding studies, the result varies substantially regarding the channel of
spillovers through either return or volatility (see section 2). As such, we can determine whichchannel is more important by comparing relevant spillover indexes (Diebold and Yilmaz, 2009).
The previously cited values concerning AHA indicate that the spillover index of return (18.68%)
is much higher than that of volatility (7.53%). In other words, spillover effects in mainland China
are more strongly observed in the case of returns than in the case of volatility. 13 Regarding
spillover indexes measuring effects from all other markets (the last column in both Tables 3 and4), when we compare values between return and volatility, they are rather similar in magnitude.

13

In the case of Hong Kong (AHH), the Spillover Index measuring effects from other markets net of crosseffects is 37.46% for return and 33.57% for volatility. Since the difference is very small, we cannot judge whichchannel was more important.

18

Stock markets in advanced economies exhibit higher spillover effects both in return and volatility,
but the stock market in mainland China is affected by other markets more strongly in return thanin volatility.
However, spillover indexes measuring effects from other markets to mainland China
H( S AHA) exhibit the smallest values compared with other markets. The finding that mainland

China is not responsive to stock price changes in other major economies is consistent with results
obtained in previous studies (i.e., Liu and Chen, 2008; Nishimura and Men, 2010). We can accountfor this finding by the fact that access to stocks in mainland China was limited for foreign investors(see section 1). Another reason may relate to the tendency of Chinese investors not to holdsufficiently diversified international portfolio. Additionally, the Chinese government has actively
implemented numerous measures to promote Chinese investment abroad, such as defining
Qualified Domestic Institutional Investors (QDII), as well as measures to open domestic market
to foreigners, such as granting permission to Qualified Foreign Institutional Investors (QFII) andRMB Qualified Foreign Insititutional Investors (RQFII). 14 However, the result obtained heresuggests that these measures are still short of a fully open and internationalized stock market, andthat further liberalizing measures are necessary to attain such a market.
H
H
The net spillover effect given by SiH  S
i  Si  is shown in the row labeled “TO-FROM” in both Tables 3 and 4. Positive values are observed only for USA, GBR, DEU, and FRA.

14

Investment abroad by domestic Chinese investors started in 2006, when QDII were allowed to do so. As of
July 2014, the amount of approved investment for QDII is 80.793 billion U.S. dollars and the total market
capitalization of the Hong Kong Stock Exchange (HKSE) stands at 3248.459 billion HK dollars (source: The
State Administration of Foreign Exchange of the People's Republic of China). Even if the entire amount of theapproved investment by QDII is placed on the HKSE, it is a mere 2.49% of the total capitalization of the HKSE
(using the exchange rate of 7.85 USD/HKD). Furthermore, since there is a restriction on risky foreign investment
by QDII, the actual investment in stocks abroad is much more limited.

19

Only these countries affect others more than the reverse direction, and the sum of TO and FROM
indexes, TO+FROM, is over 150% for these four countries. These advanced economies exert great
influence on other stock markets, which is consistent with preceding research (e.g., Cheung andMak, 1992; Diebold and Yilmaz, 2009).
The sum labeled TO+FROM is also adjusted for cross-effects between AHA and AHH,
and the results are given between parentheses. The sum for AHH is 63.58% in the case of returnand 35.67% in the case of volatility, and 26.64% and 8.1% for AHA, respectively. These low values
indicate, relative to other major economies, how small spillovers are in mainland China.
The analysis in this section shows that the H-shares, which can be traded by international
investors, are more sensitive to price changes in other markets than the A-shares, which areseverely restricted for foreign investors. Although they are issued by the same firms, the prices
react differently according to the openness of the market. This fact strongly implies that the stock
price comovements are caused by investor behavior.

5.2 Testing Hypotheses 2: Effects of Market Liberalization
In this section, we report test results of Hypotheses 2A–C, which predict greater spillovers between
global and Chinese markets after opening the latter to global investors, based on the premise thatspillovers are mainly caused by investor behavior. Hypothesis 2A, which analyzes the effects of aliberalizing measure is tested by focusing on two three-month periods prior to and following thestart of the Shanghai-Hong Kong Stock Connect Program on November 17, 2014. Specifically, weHcompare the values of the spillover index, S AHA , which gives the spillover effects from all othermarkets to mainland China (the last column of both Tables 3 and 4, labeled “Directional FROM
others”). The reason for adopting a three-month window is that if the window is too long, othermajor events may obfuscate estimation results. However, with a weekly frequency, there are only20

15 observations in a three-month period. As such, we have a rolling window by using a one-year(52-week) span. One year is long enough to avoid the problems of seasonal anomalies reported by
Rozeff and Kinney (1976), Ariel (1987) and others. The spillover indexes estimated by the rolling
method are graphed in Figure 1, a spillover plot (Diebold and Yilmaz, 2009, 2012).
Figure 1 plots spillover indexes for return and volatility separately. The vertical, dotted line
indicates the time (third week of November 2014) when the Shanghai-Hong Kong Stock Connect
Program started. Return spillover effects to mainland China have rapidly risen after thisliberalization (Figure 1 (a)). The average spillover index before the policy change is 13.27%, butrises to 29.16%, which is over 2.2 times the previous value. This result implies that international
investors flocked to mainland China, and that this change caused a higher linkage between thehitherto closed market and other global markets. Hypothesis 2A seems to be supported.
As previously mentioned, the mainland Chinese stock market has been found to be lesser
influenced by other global markets in the past. However, now that this market is partly open toforeign investors, the influence has substantially increased, as shown in this section. Moreover, as
the Chinese government further liberalizes its stock market, the comovements with global markets
will grow more in the future.
Volatility spillovers, however, are not affected significantly by the opening of mainland
China market (Figure 1 (b)). Return spillovers exhibit a rising trend after liberalization, but
volatility spillovers do not show any trend, but clear bursts. The average spillover index is 18.37%
before liberalization and 19.90% afterwards, showing no significant change. This feature isconsistent with the finding in the previous section that spillovers into Chinese market are observed
only for return, but not for volatility, similar to the work of Diebold and Yilmaz (2009). They
studied 19 major markets between January 1992 and December 2007 and found that return21

spillovers display an increasing trend and no bursts, but that volatility spillovers display no trends
and clear bursts.
[Insert Figure 1 about here]
We now conduct tests for Hypothesis 2B, and compare the spillover indexes from all othermarkets to AHA and AHH price indexes before and after the Shanghai-Hong Kong Stock Connect
Program. We focus on the six months surrounding liberalization, but one-year rolling windows areused to estimate indexes. Table 5 shows mean return and volatility spillover indexes from othermarkets to mainland China (AHA) and Hong Kong (AHH) for the sub-period before liberalization(first week of August to second week of November 2014) and after (third week of November 2014
to fourth week of February 2015).
Return spillovers to AHA from all other markets net of cross effects between mainland
China and Hong Kong (bottom row of Table 5) increased from 13.27% before liberalization to29.16% afterwards, and for AHH to 30.91% and 29.91%, respectively, showing minimal change.
Volatility spillovers to AHA are 18.37% before liberalization and 19.90% after liberalization, andfor AHH the numbers are 39.24% and 37.35%, respectively. The inaccessible market experienced
a large increase in return spillovers after liberalization, while the accessible market saw littlechange in return spillovers. Consequently, Hypothesis 2B is accepted. We reconfirm the conclusionthat the volatility channel is not important in the case of spillovers to mainland China.
[Insert Table 5 about here]
We finally test for Hypothesis 2C. We compare the spillover effects on stocks that became
accessible thanks to the Shanghai-Hong Kong Stock Connect Program and on those that remain
inaccessible after liberalization. The stocks cross-listed both in Shanghai and Hong Kong becamepurchasable to foreigners. The AHA index of these stocks in Shanghai represents new stocks that22

can now be traded by foreigners. However, compiling a price index of stocks that remain
inaccessible is too complicated and demanding because of the large number of firms involved. As
a proxy for this index, we utilize the Shanghai Composite Index (SHCOMP) which covers all
stocks listed at the Shanghai Stock Exchange (1,040 firms as of March 2015) and includes manystocks that remain inaccessible even after the Connect Program.15 We utilize the SHCOMP as arough proxy for the price index of stocks not accessible to foreigners. Additionally, we computethe spillover indexes for AHA and SHCOMP in order to check how spillover effects change after
liberalization. If the premise of Hypothesis 2C is correct, the result would be strengthened if weused the true price index of inaccessible stocks.
Table 6 shows mean return and volatility spillover indexes for the three-month periods
before (first week of August to second week of November 2014) and after the liberalization (third
week of November 2014 to fourth week of February 2015). We focus on spillovers from all othermarkets to AHA and SHCOMP net of cross effects between the two markets. The values are shownin the bottom row of Table 5 or 6. We compare the results of AHA in Table 5 to those of SHCOMP
in Table 6. The return spillover for SHCOMP is 14.58% before liberalization ( SUP,before ) and19.71% after liberalization ( SUP,after ). The proportion of SUP,after / SUP,before is merely 1.35,
indicating a small increase. On the other hand, the return spillover for AHA is 13.27% before
liberalization ( SP,before ) and 29.16% afterwards ( SP,after ). The proportion of SP,after / SP,before
indicates a 2.2-fold increase in the index. Therefore, the rise in the return spillover index for stocks
that became accessible because of the liberalization is much greater than the rise for stocks thatremained inaccessible. SHCOMP includes all the stocks included in the AHA index. If we could
15

Stocks that became accessible to foreigners number 577 firms and account for about 55.5% of the total.

23

use a price index purely composed of inaccessible stocks, the rise in the spillover index would
have been much smaller than the proportion of 1.35 observed with SHCOMP. In any case,
Hypothesis 2C, with a proposition that SUP,after / SUP,before  SP,after / SP,before , is supported. However,the change in volatility spillovers was not clear-cut, and Hypothesis 2C regarding volatility is notsupported as in the case of Hypotheses 2A and 2B.
In summary, the return spillover effects from other markets to mainland China increased
after the market liberalization to foreign investors, vindicating Hypotheses 2A, 2B, and 2C. These
results imply that transactions by international investors are the cause of stock price comovements.

5. Conclusion
In this study, we have endeavored to answer whether international stock price comovements are
influenced by investor behavior by focusing on how cross-listed stocks in mainland China andHong Kong markets are impacted by foreign markets. Stocks listed at the Hong Kong Stock
Exchange are tradable by foreign investors, but stocks listed in mainland China are not. If stock
prices are driven only by fundamentals, shocks arising in other stock markets should affect Chinese
stock prices by exactly the same amount, because the underlying fundamentals are identical,
whether they adjust to foreign shocks or not. If, on the other hand, stock prices are driven bytransactions of investors, stock prices in Hong Kong must be more responsive to foreign shocks
than those in mainland China, because foreign investors will adjust their international portfolios
in the presence of new shocks elsewhere. We tested these two hypotheses by applying the spillover
index proposed by Diebold and Yilmaz (2012, 2014). An innovation of this study lies in the use ofcross-listed stocks on both an accessible and an inaccessible market, enabling us to examine thepossible cause of stock market spillover effects. The results generally indicate that spillovers are24

stronger in Hong Kong than in mainland China, implying that the investor-induced hypothesis isvalid. Our estimation also reveals that return spillovers between mainland China and major foreign
markets are stronger than volatility spillovers.
During the sample period we have adopted for this study, the Chinese government
implemented the Shanghai-Hong Kong Stock Connect Program, which partly opened the mainland
stock market to foreign investors. Therefore, this policy change provides another opportunity totest the appropriateness of either the fundamentals-based or the investor-induced hypothesis on
price comovements by comparing spillover effects before and after the change. The result indicatesthat the return spillovers became stronger in the mainland Chinese market. However, volatility
spillovers remained constant, which is understandable because spillovers were mainly observed inreturns rather than in volatilities. Consequently, our results tend to support the investor-induced
hypothesis.
Nonetheless, not all stocks traded in mainland China became accessible to foreign investors
after liberalization, but only part of them, including stocks cross-listed in Shanghai and Hong Kong.
Therefore, if the investor-induced hypothesis is correct, the stocks cross-listed in Shanghai andHong Kong are likely to generally exhibit stronger spillovers after liberalization than the stocks inShanghai. This has been verified by our empirical analysis.
The above results support the investor-induced hypothesis, which implies that international
stock market comovements are caused by international investor behavior. As increases ininternational portfolio investments are expected to continue, we expect to see stronger stock
market comovements.
Our paper has limitations as well. First, the factual evidence we provided was obtained byfocusing on two specific markets during a limited time period. We need to confirm these results

25with other markets and extended sample periods. Furthermore, the results need to be verified byother methods as follows. Although we utilized weekly data and computed spillover indexes, it isdesirable to use other frequencies, such as monthly, daily, or even intraday high-frequency data.
Other estimation methods regarding stock market comovements are also necessary, such as crosscorrelation functions. Second, one of the contributions of this paper is that fundamentals arecontrolled for by analyzing stocks cross-listed in accessible and inaccessible markets. This featureenabled us to focus on investor behavior. However, it also means we cannot observe the effects ofchanges in fundamentals. As such, devising a methodology whereby we can compare the effect ofinternational investors and that of changing fundamentals would be useful. Third, our analysis was
based on the premise that there are only two possible causes of stock market comovements, namely
investor behavior and fundamentals. However, news on stock price changes in other countries may
affect investor sentiment significantly, resulting in comovement. This possibility needs to be
examined. Even if this were the case, the main conclusion of this paper would hold valid, if theeffects of news are the same across accessible and inaccessible markets. Addressing these
problems provides scope for future research.

26

Appendix A. Spillover Index of Diebold and Yilmaz (2012, 2014)
The spillover index, proposed by Diebold and Yilmaz (2012, 2014), is based on the variancedecompositions of a generalized VAR system. The forecast error matrix with an H-period horizon,

D H  [dijH ] , is defined by: jj1  h 0 (eih  e j ) 2
H 1

d

H
ij



 h0 (eih  h ei )
H 1

,

(A.1)

where  is a variance-covariance matrix of the error term  ,  jj is the j-th term on the
diagonal of  , ei ( e j ) is a selection vector which has an i-th (j-th) element equal to unit andzero otherwise. The error terms are not orthogonal to each other, thus making the row sums ofd ijH not necessarily equal to 1 (  Nj 1 dijH  1 ). In order to compare magnitudes of the spillovers
between different markets, Diebold and Yilmaz (2012, 2014) normalize the elements of DH as
follows:

dijH 

dijH

 j 1 dijH
N

.

(A.2)

This normalization produces properties such as  j 1 dijH  1 and  i , j 1 dijH  N , which provide
NN

comparability between markets. Note that dijH measures spillovers from the j-th to the i-th market.
H
The total spillover index, Stotal, which measures return (volatility) spillovers in the entire

global markets is defined as:
H
StotalNi , j 1
i j
N

dijH

100 
Hdi , j 1 ij

1NH
i , j 1 d ij  100 .

i

j
N

(A.3)

The total spillover index defined above can measure spillovers in global markets. We are further

27

interested in measuring return (volatility) spillovers between cross-listed Chinese stocks and other
non-Chinese stocks. Such spillover indexes are defined as follows:

SiH   j 1 dijH 100 ,

(A.4)

H
H
S
i   j 1 d ji  100 ,

(A.5)

N

j i

N

j i

H
where SiH measures spillovers from all other markets to the i-th market, and S
i measures

spillovers from the i-th market to all other markets.

28

Appendix B. Compilation of A-share and H-share Cross-Listed Stock Index
We retrieve prices at five-min frequency for 86 stocks cross-listed in mainland China (A-shares)and Hong Kong (H-shares) and compute capitalization-weighted average of those prices. This
index on day t and at time i is computed as
86

Indextm(i )    tl((im) ) ptl((im) ) ,
l 1

where

 l ( m) 

Vt l ( m) ptl((im) )l 1Vt l (m) ptl((im) )86.

In the expressions above, ptl(i ) is the price of stock l in market m (m = China, Hong Kong) at timeli on day t, Vt is the number of shares of stock l in market m on day t. Prices of A-shares wereretrieved from FoxTrader and those of H-shares from Stock Exchange of Hong Kong, Ltd. The
number of shares was retrieved from the Wind database. Five-min data are available for mainland
China, but only tick data are available for H-shares. As such, we retrieved five-min data from thesetick data.
[Insert Table A about here]

29

Figures and Tables
Table 1. Descriptive Statistics, Global Stock Market Weekly Returns
Sample period: January 2, 2012 to February 27, 2015 (T = 165)
AHA

AHH

USA

GBR

JPN

Mean

0.320

0.227

0.312

0.134

0.484

Std. dev

3.530

3.432

1.539

1.656

2.785

Skewness

0.647

0.456

-0.262-0.636-0.340

Kurtosis

4.122

3.427

3.288

4.641

3.132

DEU

FRA

TWN

IND

AUS

Mean

0.398

0.272

0.177

0.380

0.231

Std. dev

2.261

2.178

1.636

1.934

1.532

Skewness-0.279-0.434-0.1530.161-0.597

Kurtosis

2.794

3.405

4.031

2.458

4.202

30

Table 2. Descriptive Statistics, Global Stock Market Weekly Log Volatility
Sample period: January 2, 2012 to February 27, 2015 (T = 165)
AHA

AHH

USA

GBR

JPN

Mean

0.481

0.761

-0.880-0.7050.202

Std. dev

0.730

0.417

0.623

0.557

0.657

Kurtosis

1.254

0.170

-0.104

0.220

0.538

Skewness

5.399

3.387

3.221

2.541

4.081

DEU

FRA

TWN

IND

AUS

Mean

-0.181-0.087-0.709-0.321-0.852

Std. dev

0.625

0.604

0.460

0.470

0.464

Kurtosis-0.112

0.200

0.168

0.388

0.152

Skewness

2.685

2.491

2.523

2.969

2.811

31

Table 3. Full-Sample Return Spillover Table
Sample period: January 2012 to February 2015
Directional
FROM
others
43.78(18.68)56.76(37.46)

AHA

AHH

USA

GBR

JPN

DEU

FRA

TWN

IND

AUS

AHA

56.22

25.09

1.83

3.34

0.73

3.38

2.68

3.87

1.54

1.32

AHH

19.30

43.24

3.81

4.68

3.30

5.36

4.78

5.16

6.50

3.88

USA

0.59

2.01

26.61

16.24

7.06

14.78

16.14

4.56

5.11

6.91

73.39

GBR

1.29

2.44

14.35

23.59

6.65

15.01

16.71

5.00

6.56

8.40

76.41

JPN

0.54

3.12

10.35

10.55

37.59

7.94

10.04

6.10

6.05

7.71

62.41

DEU

0.56

2.85

14.23

16.27

5.11

25.58

20.95

3.88

5.52

5.05

74.42

FRA

0.78

2.50

14.60

16.94

6.18

19.54

23.91

4.27

5.79

5.49

76.09

TWN

2.38

4.14

9.08

10.08

6.47

7.85

9.23

35.03

9.77

5.94

64.97

IND

0.97

5.63

7.84

11.00

5.12

8.60

9.64

8.34

38.91

3.97

61.09

AUS

0.85

3.43

10.02

13.49

7.61

7.69

8.97

6.07

4.46

37.40

62.60

Directional
TO others

27.26
51.22
86.10
102.59
48.23
90.16
99.14
47.24
51.29
48.67
(7.96)(26.12)
Total-16.52-5.54
spillover
TO-FROM
12.72
26.18-14.1715.73
23.05-17.73-9.79-13.92(-10.72)(-11.34)71.03
107.97
TO+FROM
159.49178.99110.64164.58175.23112.20112.38111.2765.19(26.64)(63.58)
Note: The underlying variance decomposition is based upon a VAR of order 1, and the predictive horizon is 2. The ij-th entry of the upper-left 10 10market submatrix gives the ij-th pairwise directional spillover index (i.e., spillover from j market to i market). The rightmost (FROM) column gives thetotal directional spillover index (FROM); that is, row sums (from all others to i). The bottom (TO) row gives the total directional spillover Index (TO); thatis, column sums (to all others from j). Values in parentheses are the directional spillover indexes net of cross effects between mainland China and Hong
Kong.

32

Table 4. Full-Sample Volatility Spillover Table
Sample period: January 2012 to February 2015
Directional
FROM
others
17.35(7.53)
46.98(33.57)

AHA

AHH

USA

GBR

JPN

DEU

FRA

TWN

IND

AUS

AHA

82.65

9.83

0.12

0.50

0.81

0.05

0.17

0.19

4.39

1.30

AHH

13.41

53.02

4.20

8.32

3.21

5.03

4.44

2.06

0.46

5.85

USA

0.05

1.99

28.74

19.31

6.25

15.55

16.69

3.87

1.04

6.49

71.26

GBR

0.24

2.61

15.26

25.58

4.30

17.90

19.15

4.39

1.34

9.23

74.42

JPN

0.69

1.33

10.01

9.31

61.72

4.38

4.96

2.58

0.04

4.97

38.28

DEU

0.02

1.82

14.15

21.11

2.35

27.56

23.37

4.00

0.59

5.04

72.44

FRA

0.03

1.90

14.64

21.48

2.56

22.65

25.79

4.33

0.68

5.95

74.21

TWN

0.47

0.64

6.71

11.91

2.55

10.27

11.62

52.98

1.41

1.46

47.02

IND

1.23

1.22

3.20

4.73

0.37

2.28

2.13

3.50

79.03

2.31

20.97

AUS

1.86

5.32

10.14

12.66

5.84

7.35

8.69

2.30

0.78

45.07

54.93

Directional
TO others

18.00
26.66
78.41
109.32
28.24
85.46
91.20
27.24
10.73
42.60
(0.57)(2.10)
Total
0.65-20.33spillover
TO-FROM
7.16
34.90-10.03
13.03
16.98-19.79-10.24-12.33(-6.96)(-31.47)
35.35
73.64
TO+FROM
149.67183.7466.52
157.90165.41
74.26
31.70
97.54
51.79
(8.1)
(35.67)
Note: The underlying variance decomposition is based upon a VAR of order 1 and the predictive horizon is 2. The ij-th entry of the upper-left 10 10market submatrix gives the ij-th pairwise directional spillover index (i.e., spillover from j market to i market). The rightmost (FROM) column gives thetotal directional spillover index (FROM); that is, row sums (from all others to i). The bottom (TO) row gives total directional spillover index (TO); that is,column sums (to all others from j). Values in parentheses are the directional spillover indexes net of cross effects between mainland China and Hong Kong.

33

Table 5. Return and Volatility Spillover Index of AHA and AHH before and after the
Shanghai-Hong Kong Stock Connect Program
Return Spillover Index
AHA

Volatility Spillover Index

AHH

AHA

AHH

beforeafter

beforeafter

beforeafter

before

after

AHA

53.27

45.64

26.50

23.32

62.88

65.95

11.58

18.69

AHH

33.46

25.20

42.59

46.77

18.75

14.15

49.19

43.96

USA

1.31

2.26

4.90

3.78

0.71

3.83

3.22

3.73

GBR

1.53

5.20

3.05

2.34

3.39

2.65

6.82

8.66

JPN

0.62

0.68

4.77

2.10

7.36

4.01

13.30

2.55

DEU

2.58

6.19

5.68

6.95

0.98

0.43

1.58

3.18

FRA

3.11

5.88

4.25

4.10

0.78

0.38

4.23

3.66

TWN

1.66

4.38

3.35

3.45

1.29

0.67

0.83

1.08

IND

1.72

1.61

3.06

3.94

1.21

5.22

2.24

4.71

AUS

0.74

2.96

1.85

3.25

2.65

2.71

7.00

9.78

FROM exclude
AHA & AHH

13.27

29.16

30.91

29.91

18.37

19.90

39.24

37.35

Notes: The rolling estimation window width is 52 weeks, and the predictive horizon for the underlying
variance decomposition is 2. The “before” column presents mean values between the first week ofAugust 2014 and the third week of November 2014. The “after” column presents mean values between
the third week of November 2014 and the fourth week of February 2015.

34

Table 6. Return and Volatility Spillover Index of SHCOMP before and after the
Shanghai-Hong Kong Stock Connect Program
Return Spillover Index

Volatility Spillover Index

beforeafter

beforeafter

SHCOMP

60.36

55.71

68.92

53.09

AHH

25.06

24.58

9.87

18.37

USA

2.92

1.96

1.05

1.71

GBR

1.96

2.87

5.42

6.42

JPN

0.89

0.62

4.00

4.66

DEU

2.64

4.45

1.18

1.43

FRA

2.96

4.22

1.54

1.18

TWN

0.72

2.41

0.73

0.51

IND

1.23

1.30

3.00

6.20

AUS

1.24

1.88

4.29

6.42

FROM exclude
SHCOMP & AHH

14.58

19.71

21.21

28.53

Notes: The rolling estimation window width is 52 weeks, and the predictive horizon for the underlying
variance decomposition is 2. The “before” column presents mean values between the first week ofAugust 2014 and the third week of November 2014. The “after” column presents mean values between
the third week of November 2014 and the fourth week of February 2015.

35

Table A. List of Companies Included in the AH Cross-Listed Index.
A-shareCode

H-shareCodeCompany Name

A-shareCode

H-shareCode

China Vanke Co., Ltd.
China International Marine Containers (Group) Co., Ltd.

000002.SZ

2202.HK

000039.SZ

2039.HK

Tianjin Capital Environmental Protection Group Co., Ltd.
Dongfang Electric Corporation Limited

600874.SH
600875.SH

1065.HK
1072.HKZTE Corporation
Zoomlion Heavy Industry Science and Technology Co.,Ltd.
Weichai Power Co., Ltd.

000063.SZ

0763.HK

Luoyang Glass Co., Ltd.

600876.SH

1108.HK

000157.SZ

1157.HK

Chongqing Iron and Steel Company Limited

601005.SH

1053.HK

000338.SZ

2338.HKFirst Tractor Company Limited

601038.SH

0038.HK

Shandong Chenming Paper Holdings Limited
Livzon Pharmaceutical Group Inc.

000488.SZ
000513.SZ

1812.HK
1513.HK

China Shenhua Energy Company Limited
Sichuan Expressway Company Limited

601088.SH
601107.SH

1088.HK
0107.HK

Northeast Electric Development Company Limited
Jingwei Textile Machinery Co., Ltd.

000585.SZ
000666.SZ

0042.HK
0350.HK

Air China Limited
China Railway Construction Corporation Limited

601111.SH
601186.SH

0753.HK
1186.HK

Shandong Xinhua Pharmaceutical Company Limited

000756.SZ

0719.HK

Guangzhou Automobile Group Co., Ltd.

601238.SH

2238.HK

Angang Steel Company Limited
Hisense Kelon Electrical Holdings Co., Ltd.

000898.SZ
000921.SZ

0347.HK
0921.HK

Agricultural Bank of China Limited
China CNR Corporation Limited

601288.SH
601299.SH

1288.HK
6199.HK

Xinjiang Goldwind Science and Technology Co., Ltd.
Shandong Molong Petroleum Machinery Co., Ltd.

002202.SZ
002490.SZ

2208.HK
0568.HK

Ping An Insurance (Group) Company of China, Ltd.
Bank of Communications Co., Ltd.

601318.SH
601328.SH

2318.HK3328.HKBYD Company Limited
Dongjiang Environmental Company Limited

002594.SZ
002672.SZ

1211.HK
0895.HK

Guangshen Railway Company Limited
New China Life Insurance Company Ltd.

601333.SH
601336.SH

0525.HK
1336.HK

Company Name

Zhejiang Shibao Company Limited

002703.SZ

1057.HK

China Railway Group Limited

601390.SH

0390.HK

Huaneng Power International Co., Ltd.
Anhui Expressway Co., Ltd.

600011.SH
600012.SH

0902.HK0995.HK

Industrial and Commercial Bank of China Limited
Beijing North Star Company Limited

601398.SH
601588.SH

1398.HK0588.HK

China Minsheng Banking Corp., Ltd.
China Shipping Development Company Limited

600016.SH
600026.SH

1988.HK
1138.HK

Aluminum Corporation of China Limited
China Pacific Insurance (Group) Co., Ltd.

601600.SH
601601.SH

2600.HK
2601.HK

Huadian Power International Corporation Limited

600027.SH

1071.HK

Shanghai Pharmaceuticals Holding Co., Ltd.

601607.SH

2607.HK

China Petroleum and Chemical Corporation
China Southern Airlines Co., Ltd.

600028.SH
600029.SH

0386.HK
1055.HK

Metallurgical Corporation of China Ltd.
China Life Insurance Company Limited

601618.SH
601628.SH

1618.HK
2628.HK

CITIC Securities Company Limited
China Merchants Bank Co., Ltd.

600030.SH
600036.SH

6030.HK
3968.HK

Great Wall Motor Company Limited
Zhengzhou Coal Mining Machinery Group Co., Ltd.

601633.SH
601717.SH

2333.HK
0564.HK

China Eastern Airlines Corporation Limited

600115.SH

0670.HK

Shanghai Electric Group Company Limited

601727.SH

2727.HK

Yanzhou Coal Mining Company Limited
Shanghai Fosun Pharmaceutical (Group) Co., Ltd.
Guangzhou Baiyunshan Pharmaceutical Holdings Company
LimitedJiangxi Copper Co., Ltd.

600188.SH
600196.SH

1171.HK
2196.HKCSR Corporation Limited
China Communications Construction Company Limited

601766.SH
601800.SH

1766.HK
1800.HK

600332.SH

0874.HK

China Oilfield Services Limited

601808.SH

2883.HK

600362.SH

0358.HK

China Everbright Bank Company Limited

601818.SH

6818.HK

Jiangsu Expressway Co., Ltd.

600377.SH

0177.HK

Petrochina Company Limited

601857.SH

0857.HK

Shenzhen Expressway Co., Ltd.
Anhui Conch Cement Company Limited

600548.SH
600585.SH

0548.HK
0914.HK

China Shipping Container Lines Company Limited
Dalian Port (PDA) Company Limited

601866.SH
601880.SH

2866.HK
2880.HK

Tsingtao Brewery Co., Ltd.
Guangzhou Shipyard International Co., Ltd.

600600.SH
600685.SH

0168.HK
0317.HK

China Coal Energy Company Limited
Zijin Mining Group Co., Ltd.

601898.SH
601899.SH

1898.HK
2899.HK

Sinopec Shanghai Petrochemical Co., Ltd.
Nanjing Panda Electronics Co., Ltd.

600688.SH
600775.SH

0338.HK0553.HK

China COSCO Holdings Company Limited
China Construction Bank Corporation

601919.SH
601939.SH

1919.HK0939.HK

Shenji Group Kunming Machine Tool Co., Ltd.

600806.SH

0300.HK

Datang International Power Generation Co., Ltd.

601991.SH

0991.HK

Maanshan Iron and Steel Co., Ltd.
Haitong Securities Company Ltd.

600808.SH
600837.SH

0323.HK
6837.HK

China CITIC Bank Corporation Limited
BBMG Corporation

601998.SH
601992.SH

0998.HK
2009.HK

Beijing Jingcheng Machinery Electric Company Limited
Sinopec Yizheng Chemical Fibre Co., Ltd.

600860.SH
600871.SH

0187.HK
1033.HKBank of China Limited
China Molybdenum Co., Ltd.

601988.SH
603993.SH

3988.HK3993.HK

36

40

30

(a)return spillover(b)volatility spillover

35
25
30
20

25
20

15

15
10
10
5

5
14M08

14M10

14M12

15M02

14M08

14M10

14M12

15M02

Figure 1. Directional Spillover Plots, A-share market FROM all other markets
Notes: The rolling estimation window width is 52 weeks, and the predictive horizon for the underlying
variance decomposition is 2. Panel (a) depicts return spillovers and (b) volatility spillovers. The solidline indicates spillover levels, and the vertical dotted line shows the third week of November 2014, when
the Shanghai-Hong Kong Stock Connect Program started.

37

References
Albuquerque, R. and Vega, C. (2009) 'Economic news and international stock market comovement', Review of Finance, 13(3), pp. 401-465.
Andersen, T. G. and Bollerslev, T. (1998) 'Answering the skeptics: Yes, standard volatility
models do provide accurate forecasts', International Economic Review, 39(4), pp.
885-905.
Andersen, T. G., Bollerslev, T., Diebold, F. X. and Labys, P. (2001) 'The distribution ofrealized exchange rate volatility', Journal of the American Statistical Association,
96(453), pp. 42-55.
Andersen, T. G., Bollerslev, T., Diebold, F. X. and Labys, P. (2003) 'Modeling andforecasting realized volatility', Econometrica, 71(2), pp. 579-625.
Angelidis, T. and Degiannakis, S. (2008) 'Volatility forecasting: Intra-day versus inter-day
models', Journal of International Financial Markets, Institutions and Money, 18(5),pp. 449-465.
Ariel, R. A. (1987) 'A monthly effect in stock returns', Journal of Financial Economics,
18(1), pp. 161-174.
Aït-Sahalia, Y., Mykland, P. A. and Zhang, L. (2005) 'How often to sample a continuoustime process in the presence of market microstructure noise', Review of Financial
Studies, 18(2), pp. 351-416.
Bae, K.-H., Karolyi, G. A. and Stulz, R. M. (2003) 'A new approach to measuring financial
contagion', Review of Financial Studies, 16(3), pp. 717-763.
Bandi, F. M. and Russell, J. R. (2006) 'Separating microstructure noise from volatility',
Journal of Financial Economics, 79(3), pp. 655-692.
38

Barberis, N., Shleifer, A. and Wurgler, J. (2005) 'Comovement', Journal of Financial
Economics, 75(2), pp. 283-317.
Barndorff-Nielsen, O. E. and Shephard, N. (2002) 'Estimating quadratic variation using
realized variance', Journal of Applied Econometrics, 17(5), pp. 457-477.
Baur, D. and Jung, R. C. (2006) 'Return and volatility linkages between the US and theGerman stock market', Journal of International Money and Finance, 25(4), pp.
598-613.
Bollerslev, T., Engle, R. F. and Nelson, D. B. (1994) 'ARCH models', Handbook of
Econometrics, 4, pp. 2959-3038.
Boyer, B. H., Kumagai, T. and Yuan, K. (2006) 'How do crises spread? Evidence fromaccessible and inaccessible stock indices', The Journal of Finance, 61(2), pp. 9571003.
Bubák, V., Kočenda, E. and Žikeš, F. (2011) 'Volatility transmission in emerging European
foreign exchange markets', Journal of Banking & Finance, 35(11), pp. 2829-2841.
Cai, C. X., McGuinness, P. B. and Zhang, Q. (2011) 'The pricing dynamics of cross-listed
securities: the case of Chinese A-and H-shares', Journal of Banking & Finance,
35(8), pp. 2123-2136.
Campbell, J. Y., Lo, A. W.-C. and MacKinlay, A. C. (1997) The econometrics of financial
markets. Princeton University Press, Princeton, NJ.
Cheung, Y.-L. and Mak, S.-C. (1992) 'The international transmission of stock market
fluctuation between the developed markets and the Asian-Pacific markets', Applied
Financial Economics, 2(1), pp. 43-47.
Cheung, Y.-W. and Ng, L. K. (1996) 'A causality-in-variance test and its application to39

financial market prices', Journal of Econometrics, 72(1), pp. 33-48.
Connolly, R. A. and Wang, F. A. (2003) 'International equity market comovements:
Economic fundamentals or contagion?', Pacific-Basin Finance Journal, 11(1), pp.
23-43.
Cronin, D. (2014) 'The interaction between money and asset markets: A spillover index
approach', Journal of Macroeconomics, 39, pp. 185-202.
Diebold, F. X. and Yilmaz, K. (2009) 'Measuring financial asset return and volatility
spillovers, with application to global equity markets', Economic Journal, 119(534),
pp. 158-171.
Diebold, F. X. and Yilmaz, K. (2012) 'Better to give than to receive: Predictive directional
measurement of volatility spillovers', International Journal of Forecasting, 28(1),pp. 57-66.
Diebold, F. X. and Yilmaz, K. (2014) 'On the network topology of variancedecompositions: Measuring the connectedness of financial firms', Journal of
Econometrics, 182(1), pp. 119-134.
Engle, R. (2002) 'Dynamic conditional correlation: A simple class of multivariate
generalized autoregressive conditional heteroskedasticity models', Journal ofBusiness & Economic Statistics, 20(3), pp. 339-350.
Engle, R. F. (1982) 'Autoregressive conditional heteroscedasticity with estimates of thevariance of United Kingdom inflation', Econometrica, 50(4), pp. 987-1007.
Engle, R. F. and Granger, C. W. J. (1987) 'Co-integration and error correction:
representation, estimation, and testing', Econometrica, 55(2), pp. 251-276.
Engle, R. F. and Kroner, K. F. (1995) 'Multivariate simultaneous generalized ARCH',
40

Econometric theory, 11(1), pp. 122-150.
Eun, C. S. and Sabherwal, S. (2003) 'Cross-border listings and rice discovery: Evidence
from US-listed Canadian stocks', The Journal of Finance, 58(2), pp. 549-576.
Eun, C. S. and Shim, S. (1989) 'International transmission of stock market movements',
Journal of Financial and Quantitative Analysis, 24(02), pp. 241-256.
Forbes, K. J. and Rigobon, R. (2002) 'No contagion, only interdependence: measuring
stock market comovements', The Journal of Finance, 57(5), pp. 2223-2261.
Gagnon, L. and Karolyi, G. A. (2009) 'Information, trading volume, and international stock
return comovements: Evidence from cross-listed stocks', Journal of Financial andQuantitative Analysis, 44(04), pp. 953-986.
Granger, C. W. J. (1969) 'Investigating causal relations by econometric models and crossspectral methods', Econometrica, 37(3), pp. 424-438.
Grobys, K. (2015) 'Are volatility spillovers between currency and equity market driven by
economic states? Evidence from the US economy', Economics Letters, 127, pp. 7275.
Hamao, Y., Masulis, R. W. and Ng, V. (1990) 'Correlations in price changes and volatility
across international stock markets', Review of Financial Studies, 3(2), pp. 281-307.
Hansen, P. R. and Lunde, A. (2005) 'A forecast comparison of volatility models: does
anything beat a GARCH (1, 1)?', Journal of Applied Econometrics, 20(7), pp. 873889.
Hansen, P. R. and Lunde, A. (2006) 'Realized variance and market microstructure noise',
Journal of Business & Economic Statistics, 24(2), pp. 127-161.
Hong, H. and Stein, J. C. (2003) 'Differences of opinion, short-sales constraints, and market
41

crashes', Review of Financial Studies, 16(2), pp. 487-525.
Janakiramanan, S. and Lamba, A. S. (1998) 'An empirical examination of linkages between
Pacific-Basin stock markets', Journal of International Financial Markets,
Institutions and Money, 8(2), pp. 155-173.
Jeon, B. N. and Von Furstenberg, G. M. (1990) 'Growing international co-movement instock-price index', Quarterly Review of Economics and Business, 30(3), pp. 15-30.
Johansen, S. (1988) 'Statistical analysis of cointegration vectors', Journal of Economic
Dynamics and Control, 12(2), pp. 231-254.
Johansen, S. and Juselius, K. (1990) 'Maximum likelihood estimation and inference on
cointegration—with applications to the demand for money', Oxford Bulletin of
Economics and Statistics, 52(2), pp. 169-210.
Karolyi, G. A. and Stulz, R. M. (1996) 'Why do markets move together? An investigationof US-Japan stock return comovements', The Journal of Finance, 51(3), pp. 951986.
King, M., Sentana, E. and Wadhwani, S. (1994) 'Volatiltiy and links between national stock
markets', Econometrica, 62(4), pp. 901-33.
Kodres, L. E. and Pritsker, M. (2002) 'A rational expectations model of financial contagion',
The Journal of Finance, 57(2), pp. 769-799.
Koop, G., Pesaran, M. H. and Potter, S. M. (1996) 'Impulse response analysis in nonlinear
multivariate models', Journal of Econometrics, 74(1), pp. 119-147.
Koopman, S. J., Jungbacker, B. and Hol, E. (2005) 'Forecasting daily variability of the S&P
100 stock index using historical, realised and implied volatility measurements',
Journal of Empirical Finance, 12(3), pp. 445-475.
42

Kyle, A. S. and Xiong, W. (2001) 'Contagion as a wealth effect', The Journal of Finance,
56(4), pp. 1401-1440.
Leong, S. C. and Felmingham, B. (2003) 'The interdependence of share markets in thedeveloped economies of East Asia', Pacific-Basin Finance Journal, 11(2), pp. 219237.
Liu, C. and Chen, S. (2008) 'The volatility spillover between Chinese and majorinternational stock markets: New evidence from a causality-in-variance approach',World Economic Papers, 5, pp. 30-44.
Lucey, B. M., Larkin, C. and O’Connor, F. (2014) 'Gold markets around the world–who
spills over what, to whom, when?', Applied Economics Letters, 21(13), pp. 887-892.
Lo, A. W. and MacKinlay, A. C. (1988) 'Stock market prices do not follow random walks:
Evidence from a simple specification test', Review of financial studies, 1(1), pp. 4166.
Mondria, J. and Quintana-Domeque, C. (2013) 'Financial contagion and attention
allocation', The Economic Journal, 123(568), pp. 429-454.
Mukherjee, K. N. and Mishra, R. K. (2010) 'Stock market integration and volatility
spillover: India and its major Asian counterparts', Research in International
Business and Finance, 24(2), pp. 235-251.
Ng, A. (2000) 'Volatility spillover effects from Japan and the US to the Pacific–Basin',
Journal of International Money and Finance, 19(2), pp. 207-233.
Nishimura, Y. and Men, M. (2010) 'The paradox of China's international stock market comovement: Evidence from volatility spillover effects between China and G5 stock
markets', Journal of Chinese Economic and Foreign Trade Studies, 3(3), pp. 23543

253.
Pesaran, H. H. and Shin, Y. (1998) 'Generalized impulse response analysis in linear
multivariate models', Economics Letters, 58(1), pp. 17-29.
Petmezas, D. and Santamaria, D. (2014) 'Investor induced contagion during the banking
and European sovereign debt crisis of 2007–2012: Wealth effect or portfolio
rebalancing?', Journal of International Money and Finance, 49, pp. 401-424.
Rozeff, M. S. and Kinney, W. R. (1976) 'Capital market seasonality: The case of stock
returns', Journal of Financial Economics, 3(4), pp. 379-402.
Su, Q. and Chong, T. T.-L. (2007) 'Determining the contributions to price discovery for
Chinese cross-listed stocks', Pacific-Basin Finance Journal, 15(2), pp. 140-153.
Su, Q., Chong, T. T.-L. and Yan, I. K.-M. (2007) 'On the convergence of the Chinese andHong Kong stock markets: a cointegration analysis of the A and H shares', Applied
Financial Economics, 17(16), pp. 1349-1357.
Sun, Q., Tong, W. H. S. and Zhang, X. (2013) 'How cross-listings from an emerging
economy affect the host market?', Journal of Banking & Finance, 37(7), pp. 22292245.
Toda, H. Y. and Yamamoto, T. (1995) 'Statistical inference in vector autoregressions withpossibly integrated processes', Journal of Econometrics, 66(1), pp. 225-250.
Xekalaki, E. and Degiannakis, S. (2010) ARCH models for financial applications. John
Wiley & Sons.
Zhang, L., Mykland, P. A. and Aït-Sahalia, Y. (2005) 'A tale of two time scales:
Determining integrated volatility with noisy high-frequency data', Journal of theAmerican Statistical Association, 100(472), pp. 1394-1411.
44

Zhou, X., Zhang, W. and Zhang, J. (2012) 'Volatility spillovers between the Chinese andworld equity markets', Pacific-Basin Finance Journal, 20(2), pp. 247-270.

45