Removal of an investment restriction: the ‘B’ share experience from China's stock markets

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Removal of an investment restriction: the ‘B’ shareexperience from China's stock marketsChien-Liang Chiu , Mingchih Lee & Chun-Da Chena Department of Banking and Finance, Tamkang University, Taiwanb Department of Banking and Finance, Tamkang University, Taiwan E-mail:Version of record first published: 21 Aug 2006.

To cite this article: Chien-Liang Chiu , Mingchih Lee & Chun-Da Chen (2005): Removal of an investment restriction: the ‘B’share experience from China's stock markets, Applied Financial Economics, 15:4, 273-285

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Removal of an investment

restriction: the ‘B’ share experience

from China’s stock markets

Chien-Liang Chiu, Mingchih Lee and Chun-Da Chen*

Department of Banking and Finance, Tamkang University, Taiwan

This paper investigates the impact of CSRC allowing domestic residents toinvest in the B-share stock market. An ARJI model is used to analyse thejump dynamics process during the pre- and post-event periods and impulseresponse functions are employed to demonstrate the volatility transmis-sions between the A- and B-share markets. Results indicate that the jumpintensity and the jump frequency of Shanghai and Shenzhen stock marketsincreases. Moreover, the volatility transmissions between A- and B-sharemarkets accelerates. It is therefore concluded that the CSRC, by permittingdomestic residents to invest in B shares, will impact the A- and B-sharestock markets.

I. Introduction

China’s financial markets have attracted investors’

attention because of that country’s fast development

and potential opportunities. China’s recent remark-

able aggregate economic growth rates, averaging

10% a year since 1992, have been accompanied by

similar growth in its equity markets. However,

China’s stock markets are unusual in comparison

with others in the world. There are two stock

exchanges, the Shanghai Stock Exchange (SHSE)

and the Shenzhen Stock Exchange (SZSE), which

began operations, respectively, 19 December 1990

and 3 July 1991.1 These two markets have expanded

rapidly and their market volatilities are very high.

China’s stock markets are also very interesting in

that there are two forms of common stocks in China:

‘A’ shares and ‘B’ shares. In the early stage of A

shares being traded on SHSE, China’s government

hoped to prevent foreign investors from influencing

A share prices and to solve the demand of foreign

exchange for domestic enterprises. Because of the

limitation that the RMB (Renminbi, local Chinese

currency) is not freely exchangeable, the government

established the B share market especially for foreign

investors (non-Chinese citizens or overseas Chinese)

in February 1992.2 Even so, the trading activity and

liquidity of the B share market is lower than the

A share market. One of the most important

reasons is that the market participators are foreign

investors only.

More than 70% of companies have issued B shares

and have also issued A shares on SHSE and SZSE.

China’s government is conscious of the unique fea-

ture that there are two stock markets, and that two

currencies trade the same company listed on both the

A and B share markets, which incurs different stock

prices. Until 20 February 2001, China’s government

allowed individual domestic residents to invest in for-

eign currency stocks listed on domestic stock markets

*Corresponding author. E-mail: [email protected] For a brief review of the history of China’s securities market, see Bailey (1994) and Poon et al. (1998).2 For the purpose of ‘B’ shares on the Shanghai and Shenzhen exchanges, overseas investors are described as: foreign legaland natural persons; legal and natural persons from Hong Kong, Macau, and Taiwan; and other investors approved bythe People’s Bank of China.

Applied Financial Economics ISSN 0960–3107 print/ISSN 1466–4305 online # 2005 Taylor & Francis Group Ltd 273

http://www.tandf.co.uk/journalsDOI: 10.1080/0960310042000314232

Applied Financial Economics, 2005, 15, 273–285

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(B shares). There are numerous arbitrage opportu-nities, because there exists a large price discount forB shares relative to A shares (Bailey, 1994). Hence, ahuge amount of domestic money will enter andimprove the activities of the B share market in thefuture.

This study tries to examine the impact of China’sstock markets during the pre-event (before allowingdomestic residents to invest in the B share market)and post-event (after allowing domestic residents toinvest in the B share market) periods, and pays specialattention to investigate whether the returns and thevolatility transmissions of prices between A and Bshares exist in different relationships. In other words,if the domestic investors’ restriction is lifted, then wewish to know which A and B share prices will movecloser to the new market prices. This relationship hasnot yet been examined. China’s government hasopened the B share market for three years, and stockmarkets have now had enough time to respond to thisand accompany it with structural changes.

It is worth noting that the OLS model, VAR model(Sims, 1980), nor the GARCH model (Bollerslev,1986) are employed to analyse the correlation ofthe variations. Also, no event study methods areapplied to demonstrate abnormal returns of timeseries surrounding the pre-event and post-eventperiods. Unlike previous studies, the AutoregressionConditional Jump Intensity (ARJI) model (Chan andMaheu, 2002) is adopted to explain different relation-ships between A andB shares during the pre- and post-event periods. The model extends the GARCH modelwith a time-varying jump intensity and jump size dis-tribution. The model also modifies the shortcomingthat returns of stock prices are a continuous series.Thus, the model can further capture the process ofreturn volatilities on stock prices. Eventually, thebivariate vector autoregression (VAR) model isemployed to analyse volatility transmissions betweenA and B share prices on Shanghai and Shenzhen stockmarkets during the pre- and post-event periods.

The article is organized as follows. Section II provi-des a brief description of the Chinese stock marketindex series. Section III discusses the characteristicsof the model. Empirical results are presented inSection IV. The paper ends with a brief conclusionin Section V.

II. Background on China’s Stock Markets,and Literature Review

Background on China’s stock market

China has two stock exchanges, the ShanghaiSecurities Exchange and the Shenzhen Securities

Exchange. They have grown rapidly and receivedmuch attention since their inception in the early1990s. In particular, market segmentation is amajor characteristic of China’s stock markets. Bothexchanges traded A shares limited to domestic inves-tors and B shares limited to foreign investors before20 February 2001.

In addition to the issuing of A shares (denominatedand settled in Yuan Renminbi/RMB) restricted toChinese citizens, since 1992 China’s equity marketshave opened to international investors with theissuing of B shares (settled in foreign currencies,namely, US dollars for Shanghai B shares andHong Kong Dollars for Shenzhen B shares). B sharescan be owned by foreign investors only and aretraded on the two stock exchanges. Generally,companies allowed to list shares have to fulfila greater number of requirements when issuingB shares than when issuing A shares. B shares carrythe same rights as A shares; for example, they receivethe same dividend, although in different currencies.The database of the analysis consists of the value-weighted daily closing price indices for Shanghai Ashares (SHAI), Shanghai B shares (SHBI), ShenzhenA shares (SZAI), and Shenzhen B shares (SZBI).

Although shares similar to A and B shares aretraded in other countries (Bailey and Jagtiani, 1994;Domowitz et al., 1997), pricing behaviours in China’sA and B share markets are quite unique. The priceof a B share is typically higher than the price of thecorresponding A share, meaning B shares generallytrade at a discount relative to A shares. Marketsegmentation and liquidity are possible explanationsfor the price differential in these markets. Given thesubstantial price differences between the two classesof shares, we address two issues. First, the ARJImodel is adopted to determine whether jumpdynamics can capture the stock market rally fre-quently observed during the pre- and post-event per-iods. Second, the bivariate VAR model is employedto examine the volatility transmissions of stock indexreturns and to analyse the impact of lifting theindividual domestic residents’ investing restrictionon volatility.

Literature review

Unlike other countries, there exists a large pricediscount for China’s B shares relative to the A shares.Numerous studies including Bailey (1994) andMa (1996) document that the A and B stock marketsare segmented despite the expectation that the twotypes of shares are likely to be impacted by thesame factors. One of the main reasons for the Bshare discount is the method by which information

274 C.-L. Chiu et al.

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is incorporated into their prices. One school ofthought on B share discounts argues that foreigninvestors have less information about Chinese firmsdue to the difficulty they face in accessing informationabout B shares. On the other hand, Chui and Kwok(1998) argue that China’s governmental control of itsdomestic media restricts information to A shareinvestors, who rely more on B share investors forinformation than vice versa. More recently, Funget al. (2000) provide supportive evidence for theargument for segmented A share and B sharemarkets. Chen et al. (2001) find that relatively illiquidB shares have a higher expected return and are pricedlower to compensate investors for increased tradingcosts and B share price movements are more closelyrelated to market fundamentals than A share prices.

Yang (2003) contradicts the arguments of no infor-mational asymmetry (Chen et al. 2001), better-informed domestic investors (Su and Fleisher,1999), and the informational leading role of theShenzhen market over the Shanghai market (Funget al., 2000). His finding for China’s stock marketssuggests that foreign investors are better informedthan domestic investors whereas, by contrast, foreigninvestors in developed markets can be less informed,as widely assumed in the literature. That is consistentwith more recent research, for example, Froot et al.(2001) and Pan et al. (2001).

Over the past few years a considerable number ofstudies have been made of the various approachesadopted to demonstrate the segmentation in China’sequity markets. Yu (1996) and Su and Fleisher (1998)discovered that ARCH/GARCH models could befitted to Chinese stock market returns. They alsoexplored the distributional assumptions underlyingthe ARCH/GARCH model plus the impact ofgovernment policy intervention on volatility. Chuiand Kwok (1998) used the iterated linear seeminglyuncorrelated regressions (ITSUR) to estimate thelead–lag relation between A and B shares in China.

Fung et al. (2000) employed the latent variableasset pricing framework to explore the degree towhich the two-tier markets of A and B shares inChina are segmented. Chen et al. (2001) use a paneldata model to analyse the cross-sectional and time-series determinants of the price differences betweenthe two classes of shares. Most studies, like Yang(2003), utilize multivariate VAR models to arguethat there is segmentation in China’s stock markets.Brooks and Ragunathan (2003) extended the multi-variate VAR model augmented with a GARCH (1,1)model to examine the spillover in volatility betweenA and B share prices.

We therefore conclude that segmentation inChina’s stock markets is a reasonable explanation

as to why there exists a price discount for B sharesrelatively to A shares. The aim of this paper is todescribe the differences in the process of price vola-tilities of A and B shares, and to illustrate the changesof volatility transmissions during pre- and post-eventperiods. It also determines whether this new policycan improve the B share price discount and acceleratemarket integration.

III. Data and Methodology

Data

In this study a comprehensive data set includingChina’s main stock market closing price indices isused. The four China stock market price indicesunder study are the Shanghai A share index(SHAI), Shanghai B share index (SHBI), ShenzhenA share index (SZAI), and Shenzhen B share index(SZBI). The sample period for this study covers tenyears, from 6 October 1992 to 31 July 2003, totalling2521 daily observations for each stock index priceseries. The selected period begins from when SZBIwas established, and the data are provided by theTaiwan Economic Journal (TEJ).

All the analysis is conducted on returns data.For the series of return rates we use the first differ-ence of log index-prices, i.e. the return rate,

Ri, t ¼ � lnPi, t � 100

where Pi,t represents the closing price index of i shareat time t.

Methodology

There are two components, that can explain the totalchanges in stock prices. One is the normal variationin stock prices due to changes in economic outlookor any other new information that causes marginalchanges in the stock’s value. This is approximated bya diffusion process. The second part is the uncommonfluctuation in stock prices caused by the arrival ofnew and important information about the stockthat has more than a marginal effect. This type ofnews usually is specific to the industry or the firm.One would expect active times when this news arrivesand quiet times when there is no news. Of course,these are random events and news arrives only atdiscrete points in time. The jump process modelsthis component as the sum of a discrete number(determined by a Poisson process) of normal variatesreflecting non-marginal effects of news.

The purpose of this paper is to investigate changesin the relationships between A and B shares afterChina’s government allowed individual domestic

Removal of an investment restriction: B shares in China 275

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residents to invest in the B share market. This policybelongs to the uncommon fluctuations in stockprices. Therefore, the jump dynamics in A and Bshare market returns are first studied by followingChan and Maheu (2002), who apply the ARJImodel, coupled with a GARCH specification, andfit index returns using the ARJI (1,1) model.Accordingly, Chan and Maheu (2002) find a signifi-cant time variation in the conditional jump intensityand jump size distribution in daily stock marketreturns. They also show that time series dynamicsin the jump size distribution are important in provid-ing a good characterization of the data.

After modelling the ARJI model, one can capturethe conditional variances in the model and apply thevector autoregression (VAR) model to examinethe difference of volatility information transmissionamong these indices during pre- and post-eventperiods. The models used are detailed below.

ARJI model. This part presents a discrete time jumpmodel with a time-varying conditional jump intensityand jump size distribution. Because of the vast litera-ture showing GARCH models to be a good firstapproximation to the conditional variance, thejump specification is combined with a GARCHparameterization of volatility. The information setat time t is defined to be the history of returns,�t¼ {Rt,. . . ,R1}. Consider the following ARJI (auto-regressive conditional jump intensity) model for stockindex returns:

Rt ¼ �þX2i¼1

�iRt�i þffiffiffiffiht

pzt þ

Xntk¼1

Yt, k

zt � NIDð0, 1Þ, Yt, k � Nð�t, �2t Þ ð1Þ

where Rt is the index return series for SHAI, SHBI,SZAI, and SZBI. The conditional jump size Yt,k,given �t� 1, is presumed to be independent andnormally distributed with mean �t and variance �2t .

Let nt denote the discrete counting process govern-ing the number of jumps that arrive between t� 1 andt, which is distributed as a Poisson random variablewith parameter �t>0 and density

Pðnt ¼ jj�t�1Þ ¼expð��tÞ�

jt

j !j ¼ 0, 1, 2, . . . ð2Þ

The mean and variance for the Poisson randomvariable are both �t,which is often called the (jump)intensity. To complete the conditional volatilitydynamics for returns, let ht be measurable withrespect to the information set �t� 1 and followa GARCH (1,1) process, that is,

ht ¼ !þ �"2t�1 þ �ht�1 ð3Þ

where "t ¼ Rt � ��P2

i¼1 �iRt�i. This specificationof "t contains the expected jump component andthus allows it to propagate and affect future volatilitythrough the GARCH variance factor.

Consider the following ARJI model, denotedARJI (1,1). Let �t � E½ntj�t�1� be the conditionalexpectation of the counting process that is assumedto follow

�t ¼ �0 þ ��t�1 þ t�1 ð4Þ

The conditional jump intensity at time t is relatedto one past lag of the conditional jump intensityplus one lag of t. Here, t� 1 represents the innova-tion to �t� 1 as measured ex post by the econometri-cian. This shock, of jump intensity residual, iscalculated as

t�1 � E nt�1j�t�1½ � � �t�1

¼X1j¼0

jP nt�1 ¼ jj�t�1ð Þ � �t�1 ð5Þ

The first term on the right side of Equation 5 isthe inference on the average number of jumps attime t� 1 based on time t� 1 information, whilethe second term is the expectation of the number ofjumps using information at time t� 2. Therefore,t� 1 represents the unpredictable component affect-ing the inference about the conditional mean of thecounting process nt� 1.

Let f ðRtjnt ¼ j,�t�1Þ denote the conditionaldensity of returns given that j jumps occur and theinformation set is �t� 1. Having observed Rt andusing the Bayes rule, one can infer ex post the prob-ability of the occurrence of j jumps at time t, with thefilter defined as

Pðnt ¼ jj�tÞ ¼f ðRtjnt ¼ j,�t�1Þ � Pðnt ¼ jj�t�1Þ

PðRtj�t�1Þ,

j ¼ 0, 1, 2, . . . ð6Þ

The filter (Equation 6) is an important component ofthe model of time-varying jump dynamics, because itenters Equation 5, but also can be constructed andused for inference purposes. The conditional densityof returns is

PðRtj�t�1Þ ¼X1j¼0

f ðRtjnt ¼ j,�t�1Þ � Pðnt ¼ jj�t�1Þ

ð7Þ

Equation 7 shows that this model is nothing morethan a discrete mixture of distributions wherethe mixing is driven by a time-varying Poisson distri-bution. The assumptions in Equation 1 imply thatthe distribution of returns conditional on the most


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recent information set and j jumps are normallydistributed as

f ðRtjnt ¼ j,�t�1Þ ¼1ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi

2�ðht þ j�2t Þp

� exp �

ðRt ��P2i¼1

�iRt�i � �tjÞ2

2ðht þ j�2t Þ

0BB@

1CCA

and

�t ¼ �0 þ �1Rt�1D Rt�1ð Þ þ �2Rt�1D 1� Rt�1ð Þ ð8Þ

�2t ¼ 0 þ 1R2t�1 þ 2ht ð9Þ

where D(Rt� 1)¼ 1 if Rt� 1>0 and 0 otherwise.The likelihood function is as follows:

L ¼XTt¼1

lnPðRtj�t�1Þ

¼XTt¼1

lnX2j¼0

f ðRtjnt ¼ j,�t�1ÞPðnt ¼ jj�t�1Þ

( )

Vector autoregression (VAR) model. The VARmodel is commonly used for analysing the dynamicimpact of random disturbances on a system ofvariables. Akaike information criteria (AIC) methodsare used to determine an appropriate lag length.According to the results, eight and six lags werechosen for pre- and post-event periods, respectively.The main objective in estimating a VAR in thisstudy is to identify any causal relationship amongA and B shares across sample stock exchanges.For example, we are interested to see how changesin the A share market affected the B share marketin SHSE and SZSE.

Only the results of the impulse response functionsare analysed. An impulse response function tracesthe effect of a one standard deviation shock to oneof the innovations on the current and future valuesof the endogenous variables. A shock to the ithvariable directly affects the ith variable and is alsotransmitted to all of the endogenous variablesthrough the dynamic structure of the VAR.

The bivariate vector regression model is formu-lated as follows:

hA, t ¼ aþ biX�i¼1

hA, t�� þ ciX�i¼1

hB, t�i þ "A, t ð10Þ

hB, t ¼ d þ eiX�i¼1

hB, t�1 þ fiX�i¼1

hB, t�i þ "B, t ð11Þ

Here, hA,t is the conditional variance of the indexreturn for A shares at time t; and hB,t is theconditional variance of the index return for B sharesat time t. Term "t is a vector of innovations that maybe contemporaneously correlated with each other,but are uncorrelated with their own lagged valuesand uncorrelated with all of the right-hand sidevariables. The lag-length �¼ 8 during the pre-eventperiod and �¼ 6 during the post-event period.

IV. Analysis of Empirical Results

Descriptive statistics

Many Chinese state-owned enterprises have beenauthorized to issue both A and B shares on theShanghai and Shenzhen stock exchanges in recentyears.3 As of February 2004, 777 companies hadissued A shares and 54 companies had issued B shareson the SHSE, with a total capitalization valueof about US$113 billion. At the same time, 492 com-panies have issued A shares and 57 companies haveissued B shares on the SZSE, with a market capitali-zation value of about US$70 billion. Table 1 reportsthese statistics.

Table 2 provides summary statistics for all stockreturn series in both the Shanghai and Shenzhenstock exchanges during the pre- and post-eventperiods. The statistics of Table 2, panel A showthat the average returns of the A share indices arehigher than the B share indices during the pre-eventperiod while the average returns on the SZBI arenegative. This result provides some preliminary

3 Typically, A shares include state shares (held by the central government), legal shares (held by domestic companies and non-banking institutions), employee shares (held by employees), and public individual shares. State and employees shares are nottraded in the market, although legal shares for some companies can be traded in the Stock Trading Automated QuotationSystem (STAQS) or National Exchange and Trading System (NETS) in Beijing.

Table 1. Market size, February 2004

Exchange Share

No. ofcompanieslisted Market capitalization

Shanghai A 777 RMB 893 billion(or US$107.87 billion)

B 54 US$5.52 billionShenzhen A 492 RMB 535 billion

(or US$64.63 billion)B 57 HK$50 billion

(or US$5.74 billion)

Notes: The total companies listed on SHSE and SZSE are787, and 508, respectively. There are 44 companies thathave issued both A and B shares on SHSE at the sametime and 41 companies on SZSE.


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evidence that A shares generally trade at a premiumrelative to B shares, a feature originally documentedby Bailey (1994). The A share index in bothexchanges has a higher standard deviation than theB share index. This is consistent with Su andFleisher’s (1999) finding of greater volatility in Ashares, which may indicate a higher risk of tradingA shares.

The JB statistic indicates that the distribution ofthe returns of all four indices during both periods,has a fat tail and sharper peak than the normaldistribution. The statistics also show that returnsare positively skewed although the skewness statisticsare not large. The positive skewness implies that thereturn distributions of the shares traded on theseexchanges have a higher probability of earning posi-tive returns. All the kurtosis that represent values inTable 2 are larger than 3. This shows that for allseries, the distribution of returns has a fat tailcompared with the normal distribution and which isconsistent with the presence of GARCH effects.

Some of the statistical results of Table 2, panel Bare different from panel A. It is seen that the averagereturns of the A share indices are negative while theaverage returns on the B share indices are positive.The A share indices in both exchanges hence havea lower standard deviation than the B share indices.These two results are not consistent with Bailey(1994) and Su and Fleisher (1999). It can be seenthat there are some shocks in A and B shares afterthe CRSC allowed individual domestic residents toinvest in foreign currency stocks listed in the domesticstock markets (B share market).

Table 3 reports the ordinary historical correlationmatrix of index return series between A and B shareindices in both exchanges during both periods.The upper and lower triangular matrices representthe pre- and post-event periods, respectively. It isobvious that the increase in correlation immediatelyduring the post-event period are higher than the

pre-event period. There is a plausible explanationfor this situation. Because there exists a large pricediscount for B shares relative to A shares (Bailey,1994, Wo, 1997, Bailey et al., 1999; Sun and Tong,2000) after the CRSC allowed individual domesticresidents to invest in B share markets, the returnvolatilities of A and B shares decreased and the Aand B share prices moved closer together.

Figure 1 illustrates the statistical properties of theindex return series. Volatility clustering is clear; highvolatility in one period tends to be followed by highvolatility in the next period. The GARCH variancestructure is appropriate for modelling this type ofbehaviour.

Volatility estimations of the ARJI model

According to Chan and Maheu (2002), the number ofjumps were set at 20. The results for the ARJI modelsfor the pre- and post-event periods are presented inTables 4 and 5. The first things to note are the strongGARCH effects and the persistence of the condi-tional variance, with parameters �þ �<1 for bothperiods and all index return series. The � parametersin the ARJI models provide a measure of the persis-tence in the conditional jump intensity. All of the �are significantly positive except for the � during thepre-event period of SHBI. This suggests that a high

Table 2. Descriptive statistics for the daily returns

Index Mean Std. Err. Maximum Minimun Skewness Kurtosis JB

Panel A: pre-event periodSHAI 0.0539 3.1747 30.8522 �18.4284 1.8074* 18.9427* 30 324.6780*SHBI 0.0141 2.3484 12.3760 �13.0888 0.5225* 4.5798* 1799.3606*SZAI 0.0414 2.8430 29.5811 �19.6324 1.0410* 14.3978* 17 256.8163*SZBI �0.0063 2.3640 18.1896 �16.7045 0.5034* 9.2849* 7112.3006*Panel B: post-event periodSHAI �0.0529 1.4440 9.3998 �6.5053 0.8602* 7.9729* 1557.8474*SHBI 0.0526 2.4125 9.4564 �10.2871 0.4946* 4.4181* 479.9884*SZAI �0.0733 1.5106 9.2403 �6.7459 0.6202* 6.8482* 1134.2240*SZBI 0.1047 2.5999 9.4716 �9.5888 0.3645* 3.3285* 271.8798*

*Significant at the 1% level. JB is the Jarque–Bera test for normality.

Table 3. Index return correlation matrix of the pre- and

post-event periods

SHAI SHBI SZAI SZBI

SHAI – 0.2628 0.7350 0.2395SHBI 0.6538 – 0.2655 0.6299SZAI 0.9830 0.6529 – 0.2977SZBI 0.6469 0.8853 0.6539 –

The upper and lower triangular matrices represent the pre-event period and the post-event period, respectively.


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probability of many (few) jumps today tends to befollowed by a high probability of many (few) jumpstomorrow for both stock markets. Comparing thetwo periods, it is found that the jump persistenceprocess has obvious clustering during the post-eventperiod.

The term measures the sensitivity of �t to the pastshock, t� 1. The results show that a unit increase int� 1 results in a dampened effect (only up to 0.2283)on the next period’s jump intensity during the post-event period. However, the pre-event period hasalmost none of the same circumstances. It followsfrom what has been said that the volatility persistenceof the index return shows a better performance andthe preceding volatilities increase in intensity duringthe period after domestic residents can invest in theB share market.

The estimates of these final models provideevidence that the jump direction is asymmetric andsensitive to the state of the stock market. The �2parameter during the post-event period is signifi-cantly negative, which implies that after a stock mar-ket downturn, the direction of a jump in the nextperiod is more likely to be positive than negative,but during the pre-event period, the converse istrue. This is because when B shares appear to havea price discount relative to A shares, the domesticresidents can arbitrage with the A shares until theprice discount disappears during the post-event per-iod. We then employ the conditional variance coeffi-cient (ht) from each ARJI model to estimate thecorrelation matrix.

Table 6 displays the conditional variance correla-tion coefficient matrices of index returns for the

-15.00

-10.00

-5.00

0.00

5.00

10.00

15.00

1998/9/10 1999/9/10 2000/9/10 2001/9/10 2002/9/10

SSEA SSEB

(A)

-15.00

-10.00

-5.0

0.00

5.00

10.00

15.00

1998/9/10 1999/9/10 2000/9/10 2001/9/10 2002/9/10

SZSA SZSB

(B)

2001/2/19

2001/2/19

Fig. 1. Index return volatilities (%) for Shanghai (A) and index return volatilities (%) for Shenzhen (B)


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pre-event and post-event periods. The results showthat conditional volatility between A and B sharemarkets increased significantly from the pre- topost-event period. This implies that when theA share (B share) market has index return volatilities,these information impacts will transmit to the B share(A share) market rapidly and the market will showvolatility. Figure 1 also reveals that the A share andB share markets are lead/lag markets with each otherand the volatility relationship became closer after19 February 2001.

Figures 2 to 9 display the jump frequency and jumpintensity of Shanghai and Shenzhen stock marketsfor pre-event and post-event periods. It is obviousthat during the post-event period, the jump frequency

of Shanghai and Shenzhen A shares increased andthe jump intensity grew. The most likely explanationis that between the A and B share markets there existsa large price discount and this spread will attract

Table 5. ARJI model of the post-event period

Independent variables SHAI SHBI SZAI SZBI

� 0.0344 �0.1290*** 0.0805* 0.6186***�1 0.0223 �0.0698* �0.0149 �0.0762***�2 �0.0137 �0.0389 �0.0209 �0.0821***! 0.1098*** 0.0001 0.1851*** 0.0001***� 0.0557*** 0.0225*** 0.1090*** 0.2373***� 0.6327*** 0.9200*** 0.4483*** 0.7666*** 0 �0.0720 1.7864** �0.1165 0.0795*** 1 0.1967 0.1993 0.0957 0.0013*** 2 3.4646*** 3.6831*** 2.5113*** 1.0740�0 �0.4783*** 0.0575 �0.7263*** �6.0668***�1 0.3964*** 0.2145* 0.6319*** 1.6752***�2 �0.1930* 0.1535 �0.2085** �1.4717***�0 0.0126*** 0.0537*** 0.0109*** 0.0040***� 0.9209*** 0.6485*** 0.8947*** 0.9309*** 0.0562*** 0.2283*** 0.0869*** �0.0427Function value �892.5656 �1090.5302 �905.6660 �1217.6341

Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively.

Table 4. ARJI model of the pre-event period

Independent variables SHAI SHBI SZAI SZBI

� 0.0392 �0.1679*** �0.0014 �0.0825***�1 �0.0390** 0.0342 �0.0210 0.0561**�2 0.0207 0.0189 0.0362 0.0515**! 0.0689*** 0.0429*** 0.1110 0.0497***� 0.1341*** 0.0742*** 0.2084*** 0.1539***� 0.7745*** 0.7842*** 0.7162*** 0.5966*** 0 4.6658*** 0.2715 9.5815 1.7429** 1 1.1448*** 0.1801** �0.0905 0.5673* 2 3.3174*** 3.2015*** 2.8826 2.8645***�0 �0.0980 0.3974*** 0.6391 �0.1889�1 0.9449*** 0.4394*** 0.3745 1.1092***�2 0.7150*** 0.4883*** 1.1863 0.3354**�0 0.0238*** 0.2551* 0.0100*** 0.1052� 0.8722*** 0.2113 0.9221* 0.7331** �0.0509 0.1853* �0.0378 �0.0690Function value �4247.4580 �3878.4797 �4235.2972 �3601.9419

Significance levels of 10%, 5%, and 1% are represented by *, **, and ***, respectively.

Table 6. Conditional volatility dynamics of index returns

of the pre- and post-event period

SHAI SHBI SZAI SZBI

SHAI – 0.0988 0.7576 0.0335SHBI 0.2987 – 0.0727 0.6114SZAI 0.9566 0.2551 – 0.1312SZBI 0.3421 0.6834 0.3411 –

The upper and lower triangular matrices represent the pre-event period and the post-event period, respectively.


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many domestic investors to invest in B shares. Morethan 80% (44/54) and 70% (41/57) of companieslisted on the Shanghai and Shenzhen B share stockmarkets, respectively, are also listed on the Shanghai

and Shenzhen A share stock markets. The A sharemarket is a more mature stock market than the Bshare market. Therefore, if the companies’B share prices are lower than the A share prices,

Panel 1: Ex-Post Jump Frequency

250 500 750 1000 1250 1500 17500.0

0.5

1.0

1.5

2.0

2.5

3.0

Panel 2: Conditional Time-Varying Jump Intensity

250 500 750 1000 1250 1500 17500.000

0.025

0.050

0.075

0.100

0.125

0.150

0.175

0.200

Fig. 2. Jump frequency (panel 1) and jump intensity (panel 2) of SHAI during the pre-event period


250 500 750 1000 1250 1500 17500.0

0.5

1.0

1.5

2.0

2.5


250 500 750 1000 1250 1500 17500.000

0.025

0.050

0.075

0.100

0.125

0.150

0.175

0.200

Fig. 4. Jump frequency (panel 1) and jump intensity (panel 2) of SZAI during the pre-event period


250 500 750 1000 1250 1500 17500.0

0.5

1.0

1.5

2.0

2.5

3.0


250 500 750 1000 1250 1500 17500.28

0.35

0.42

0.49

0.56

0.63

0.70

0.77

0.84

Fig. 3. Jump frequency (panel 1) and jump intensity (panel 2) of SHBI during the pre-event period


250 500 750 1000 1250 1500 17500.0

0.5

1.0

1.5

2.0

2.5


250 500 750 1000 1250 1500 17500.175

0.200

0.225

0.250

0.275

0.300

0.325

0.350

0.375

Fig. 5. Jump frequency (panel 1) and jump intensity (panel 2) of SZBI during the pre-event period


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then investors will speculate the B share pricesaccording to the rational A share prices. All investorshave better information to invest in the A sharemarket. Because the investment amount increased

rapidly, the jump frequency and jump intensity alsoincreased. Table 7 shows the trading volumes andtrading values in the pre- and post-event periods.It shows that trading volumes and values increased


50 100 150 200 250 300 350 400 450 500 5500.0

0.5

1.0

1.5

2.0

2.5


50 100 150 200 250 300 350 400 450 500 5500.16

0.32

0.48

0.64

0.80

0.96

1.12

Fig. 7. Jump frequency (panel 1) and jump intensity (panel 2) of SHBI during the post-event period


50 100 150 200 250 300 350 400 450 500 5500.0

0.5

1.0

1.5

2.0

2.5


50 100 150 200 250 300 350 400 450 500 5500.16

0.24

0.32

0.40

0.48

0.56

0.64

0.72

0.80

0.88

Fig. 6. Jump frequency (panel 1) and jump intensity (panel 2) of SHAI during the post-event period


50 100 150 200 250 300 350 400 450 500 5500.0

0.5

1.0

1.5

2.0

2.5


50 100 150 200 250 300 350 400 450 500 5500.16

0.32

0.48

0.64

0.80

0.96

Fig. 8. Jump frequency (panel 1) and jump intensity (panel 2) of SZAI during the post-event period


50 100 150 200 250 300 350 400 450 500 5500.00

0.16

0.32

0.48

0.64

0.80

0.96

1.12


50 100 150 200 250 300 350 400 450 500 550

0.35

0.40

0.45

0.50

0.55

0.60

0.65

0.70

0.75

Fig. 9. Jump frequency (panel 1) and jump intensity (panel 2) of SZBI during the post-event period


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significantly. Also, after allowing domestic investorsto invest in the B share market, investment in theA share market also increased in response to this.This issue (information volatility transmission) willbe examined in the next section.

Impulse responses of the VAR model

After modelling the ARJI model for the Shanghaiand Shenzhen stock markets, the conditional volati-lity dynamics are then employed and the bivariate

VAR model applied to examine the volatility

transmission effects between A and B share

markets during the pre- and post-event periods.

One needs to investigate here only the impact of the

A share market on the B share market. There are two

reasons for this: the first is that the size of the A share

market is larger than the B share market, and the B

share market volatility may not impact the A share

market. A second reason is that the investors should

conduct some arbitrage on A share stock prices.

For these two reasons, it is suggested that there

are no feedback effects between these two markets

and the B share market does not impact the A

share market.

Figures 10 A–D display impulse response results.

For each plot, the horizontal axis is the number of

days. These four graphs consider the impact of a one

standard error impulse in the B share index on the

A share index. The results show that the conditional

volatility of SHBI in response to SHAI continues

over almost 9 days (two weeks) and SZBI in response

to SZAI continues for about 5 days (one week)

during the pre-event period. During the post-event

period, however, the SHBI in response to SHAI

Plot of Responses to SHBI1

0 2 4 6 8 10 12 14 16 18 20 22-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

-0.2

0.0

0.2

0.4

0.6

0.8

1.0

Plot of Responses to SHBI2

0 2 4 6 8 10 12 14 16 18 20 22

(A) (B)Plot of Responses to SZBI1

0 2 4 6 8 10 12 14 16 18 20 22

Plot of Responses to SZBI2

0 2 4 6 8 10 12 14 16 18 20 22-0.16

0.00

0.16

0.32

0.48

0.64

0.80

0.96

(C) (D)

Figure 10. (A)–(D) Impulse response of A-share index conditional volatility to B-share index. (SHBI1 and SZBI1 represent

the pre-event period; SHBI2 and SZBI2 represent the post-event period; the upper lines and the lower lines represent the SZBIand SZAI, respectively)

Table 7. Trading volume and trading value of pre- and

post-event period

Trading volume(billion shares)

Trading value(billion RMB)

IndexPre-eventperiod

Post-eventperiod

Pre-eventperiod

Post-eventperiod

SHAI 420.48 764.83 4712.86 7925.84SHBI 19.23 85.08 58.37 588.77SZAI 394.50 496.83 4399.52 5011.23SZBI 11.77 70.40 42.56 475.37


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and SZBI in response to SZAI ‘die’ out quickly,

within 4 days and 3 days, respectively. It is clear

that after allowing domestic residents to invest

in the B share market, the volatility dynamic trans-

missions between A and B share markets obviously

accelerated. This also shortens the impact persistency

accordingly.

V. Conclusions

This article investigates the impact of CSRC allowing

domestic residents to invest in the B share market.

The ARJI model is applied to consider the jump

dynamic process on China’s stock markets and

impulse response functions are used to demonstrate

conditional volatility dynamic transmissions between

the A and B share markets.

According to the results of summary statistics

and the correlation matrix, there were some shocks

in the A and B shares after CRSC allowed individual

domestic residents to invest in the domestic stock

market (B share market). The ARJI model and

impulse response functions were further employed

to prove this argument. Most of the ARJI models

suggest that a high probability of many jumps

today tends to be followed by a high probability

of many jumps tomorrow, and vice versa. It is also

found that the jump persistence process has obvious

clustering during the post-event period and the

volatility persistence of the index return shows better

performance. The preceding volatilities will also

increase in intensity after the domestic residents can

invest in the B share market. The estimates of these

final models provide evidence that the jump direction

is asymmetric and sensitive to the state of the stock

market. Nevertheless, the jump frequency of the

Shanghai and Shenzhen A shares increase and the

jump intensity grew during the post-event period.

After modelling the ARJI model for the Shanghai

and Shenzhen stock markets, the conditional volati-

lity dynamics were employed and the bivariate VAR

model applied to examine the volatility transmission

effects between A and B share markets during the

pre- and post-event periods. The results show that

after allowing the domestic residents to invest in the

B share market, the volatility dynamic transmissions

and the impact persistency obviously accelerated

and are shortened. On these grounds one comes to

the conclusion that the CSRC allowing domestic

residents to invest in the B share market will definitely

impact the China A and B share markets.

References

Bailey, W. (1994) Risk and return on China’s new stockmarket: some preliminary evidence, Pacific-BasinFinance Journal, 2, 243–60.

Bailey, W. and Jagtiani, J. (1994) Foreign ownershiprestrictions and stock prices in the Thai capital market,Journal of Financial Economics, 36, 57–87.

Bailey, W., Chung, P. and Kang, J. (1999) Foreign owner-ship restrictions and equity price premiums: whatdrives the demand for cross-border investments?,Journal of Financial and Quantitative Analysis, 34,489–511.

Bollerslev, T. (1986) Generalized autoregressiveheteroskedasticity, Journal of Econometrics, 31, 307–27.

Brooks, R. D. and Ragunathan, V. (2003) Returns andvolatility on the Chinese stock markets, AppliedFinancial Economics, 13, 747–52.

Chan, W. H. and Maheu, J. M. (2002) Conditional jumpdynamics in stock market returns, Journal of Businessand Economics Statistics, 20, 377–89.

Chen, G. M., Lee, B. S. and Rui, O. M. (2001) Foreignownership restrictions and market segmentation inChina’s stock markets, Journal of Financial Research,24, 133–51.

Chui, A. and Kwok, C. (1998) Cross-autocorrela-tion between A shares and B shares in the Chinesestock market, Journal of Financial Research, 21,333–53.

Domowitz, I., Glen, J. and Madhavan, A. (1997)Market segmentation and stock prices: evidencefrom emerging markets, Journal of Finance, 52,1059–85.

Froot, K., O’Connell, P. and Seasholes, M. (2001) Theportfolio flows of international investors, Journal ofFinancial Economics, 59, 151–93.

Fung, H. G., Lee, W. and Leung, W. K. (2000)Segmentation of the A- and B-share Chineseequity markets, Journal of Financial Research, 23,179–95.

Ma, X. (1996) Capital controls, market segmen-tation and stock prices: evidence from theChinese stock market, Pacific-Basin Finance Journal,4, 219–39.

Pan, M., Chan, K. C. and Wright, D. J. (2001)Divergent expectations and the Asian financialcrisis of 1997, Journal of Financial Research, 24,219–38.

Poon, W. P. H., Firth, M. and Fung, H. G. (1998) Assetpricing in segmented capital markets: preliminaryevidence from China-domiciled companies, Pacific-Basin Finance Journal, 6, 307–19.

Sims, C. (1980) Macroeconomics and reality, Econometrica,48, 1–49.

Su, D. and Fleisher, B. (1998) Risk, return and regulationin Chinese stock market, Journal of Economics andBusiness, 50, 239–56.

Su, D. and Fleisher, B. (1999) Why does return volatilitydiffer in Chinese stock market?, Pacific-Basin FinanceJournal, 7, 557–86.

Sun, Q. and Tong, W. (2000) The effect ofmarket segmentation on stock prices: the Chinasyndrome, Journal of Banking and Finance, 24,1875–902.


Dow

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ded

by [

Geo

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Mas

on U

nive

rsity

] at

05:

20 2

7 A

pril

2013

Wo, C. S. (1997) Chinese dual-class equities: price differen-tials and information flows, Emerging MarketsQuarterly, 1, 49–62.

Yang, J. (2003) Market segmentation and informationasymmetry in Chinese stock market: a VAR analysis,Financial Review, 38, 591–609.

Yu, W. (1996) A conditional variance model fordaily stock returns in China’s emerging stockmarkets: empirical evidence on the Shanghaiand Shenzhen exchanges, Journal of Internat-ional Financial Markets, Institutions and Money, 6,1–19.


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Documents

Removal of an investment restriction: the ‘B’ share experience from China's stock markets