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Journal of Multinational Financial Management 10 (2000) 315 – 343

Red chips or H shares: which China-backed

securities process information the fastest?

Winnie P.H. Poon

_{*, Hung-Gay Fung}

a_{Department of Accounting and Finance}_,_{Lingnan Uni}

6ersity,8 Castle Peak Road,Tuen Mun, N.T., Hong Kong

b_{School of Business Administration}_,_Uni

6ersity of Missouri-St. Louis,8001 Natural Bridge Road, St. Louis,MI 63121-4499, USA

Received 15 July 1999; accepted 21 February 2000

Abstract

This study examines the information flow between China-backed securities, namely H shares, red chips, Shanghai and Shenzhen listed common shares. We document several findings. We find that an exponential generalized autoregressive conditional heteroscedastic-ity in mean (EGARCH-M) model appears to describe adequately the return process of the China-backed securities. Our empirical findings show that both H shares and red chips (which are listed in Hong Kong) are more sensitive to ‘good’ news than ‘bad’ news, while stocks listed in the China market are more sensitive to ‘bad’ news than ‘good’ news. Using a multivariate EGARCH-M model, we have found significant return and volatility spillover effects among the China-backed securities. Our study indicates that the red chips appear to spread information to other China-backed markets ‘directly’ or ‘indirectly’. The results imply that the red chip market processes information faster than the other markets. © 2000 Elsevier Science B.V. All rights reserved.

JEL classification:G14; G15; F30

Keywords:EGARCH; China-backed securities; Information and market efficiency

www.elsevier.com/locate/econbase

* Corresponding author. Tel./fax: +852-26168179.

E-mail address:[email protected] (W.P.H. Poon).

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1. Introduction

Ten years after ‘Black Monday’ — the stock market crash of October 1987 — the world market slumped again following a new record fall in Hong Kong’s Hang Seng Index of 1438.31 index points (i.e. −13.7%) on October 28, 1997. A new record increase of 1705.41 index points (i.e. 18.82%) was set in Hong Kong on the following day and it was believed that this led the recovery of the stock markets around the globe. This recent share price turmoil in Hong Kong broke both the records for 1-day point-loss and 1-day point-gain of the Hang Seng Index. The huge falls in Hong Kong led to big declines in London and other European markets, leading to a collapse of the New York Stock Exchange, which in turn reverberated around the world (The Economist, 1997).

Some investors might have entered into the volatile equity arena in early 1997, by investing in some China-backed stocks like H shares and red chips in Hong Kong. The basic question they need to know the answer to is whether the returns on these China related stocks behave similarly because they both: (1) have China related business; and (2) are listed on the Stock Exchange of Hong Kong or whether other factors are involved. This question has implications for investors who wish to diversify their portfolios by investing in these stocks.

Defining ‘red-chip’ stock is difficult, as there are currently so many Chinese companies listed on the Stock Exchange of Hong Kong (SEHK). The term ‘red chip’ is financial jargon used in the securities market rather than legitimate terminology used in the official books of the SEHK. Red chips are Chinese companies incorporated in Hong Kong and listed on the SEHK. Therefore, they have to follow the same set of rules governing the listing of securities and to comply with the same set of disclosure requirements (SEHK, 1997) as other locally listed companies. There are no ‘explicit’ additional listing and disclosure requirements for red chips. The SEHK does have a separate chapter on listing rules1 _{for issuers}

incorporated in the People’s Republic of China (PRC) (i.e. H-share issuers) (SEHK, 1997) and it has published a guidebook2 _{for listing Chinese companies in Hong}

Kong (SEHK, 1996).

For the purposes of the SEHK Listing Rules, ‘H shares’ are broadly defined as overseas listed foreign shares which are listed on the SEHK and subscribed for and traded in Hong Kong dollars. In summary, the PRC issuers are subject to the additional requirements (SEHK, 1997) as follows:

1. ‘‘PRC issuers are expected to present their annual accounts in accordance with Hong Kong or international accounting standards;

2. The articles of association of PRC issuers must contain provisions which will reflect the different nature of domestic shares and overseas listed foreign shares (including H shares) and the different rights of their respective holders; and

1_{That is, requirements for new listings and rules which must be continuously complied with by the} listed companies.

2_{A guide, in layman terms, to the regulatory framework and listing requirements for potential} investors in, and prospective issuers of, securities in China incorporated companies.

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W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 317 3. Disputes involving holders of H shares and arising from a PRC issuer’s articles of association, or from any rights or obligations conferred or imposed by the Company Law and any other relevant laws and regulations concerning the affairs of the PRC issuer, are to be settled by arbitration in either Hong Kong or the PRC at the election of the claimant.’’

Both red chips and H shares are China-backed securities, although the issuers of H shares have to comply with ‘additional’ listing/disclosure requirements, while no such additional requirement is imposed on the issuers of red chip. Some H shares have turned up as backdoor listings using locally listed companies as a vehicle, while others are listed as partners of Hong Kong companies (China concepts). That is why many people are confused about what a ‘red chip’ really is and how it is different from other China stocks like H shares. However, the recent market reactions to these two types of shares appear to be different. In fact, a cooling in enthusiasm for China-backed shares started before the recent crash, which prompted a fall of over 30% in the red chip index in 1997. Some investors find it difficult to forecast the performance of these red chips, while some financial analysts cannot explain the return behavior and volatility of the red chips.

To illustrate, China Everbrights (one of the red chips) price-earnings (P/E) ratio, on average, was over 1000 during the year and occasionally exceeded even 2000 over the last few months. The pioneering issue in July 1993 of Tsingtao Brewery (H-share) was 111 times oversubscribed, whereas Beijing Enterprises (red chip) was 1368 times oversubscribed in May 1997. The P/E ratio of the Hang Seng China-affiliated Corporations Index (HSCCI) (this is often called the ‘red chip index’) on October 27, 1997 was 27.14, which contrasts to 16.96 for the Hang Seng China Enterprises Index (HSCEI) (this is frequently called the ‘H-share index’) and 12.67 for the Hang Seng Index (HSI).

The major objective of this study is to examine whether the red chip or H-share market processes information faster than the other China-backed securities because stocks trading in Hong Kong appear to be subject to less manipulation and have easier access to information. In addition, most red chips are typically managed by executives from the West (who are headquartered in Hong Kong) (Marriott, 1996). It has been argued that these red chips are better managed than those of the H shares. We examine the hypothesis that the red chips have the ability to process information faster than the H shares and other China-backed securities. To this end, we first examine the return behavior and the volatility of H shares and red chips listed in Hong Kong and also the Shanghai and Shenzhen common equities listed in China.

Recently, there has been a considerable increase in literature on the relationship of conditional variance across financial markets and this relationship’s implications concerning the information transmission mechanism. Ross (1989) uses a no-arbi-trage model to show information transmission is primarily related to the volatility of price changes. Engle et al. (1990) provide an alternative interpretation that relates information processing time to variance movements. These developments

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suggest price volatility has significant implications concerning information linkages between markets. In light of the literature, we analyze the information flow in the four markets using a multivariate version of the exponential generalized autoregres-sive conditional heteroscedasticity in mean (EGARCH-M) model along with the generalized error distribution (GED).

This study is important to the finance literature for the following reasons:

It is a timely topic because of the rapid growth of the red chips and H shares in

recent years. The Chinese government is continuing its effort to privatize its state-owned enterprises in order to raise capital to revitalize the operation of these enterprises.

It is important to study the return and return volatility of China-backed

securities because almost one in every six companies listed on the SEHK recently is controlled by a PRC interest. A new China security is listed almost every week on the SEHK. The growth of China-affiliated corporations is reflected in a 109.6% increase in 1996 of the Credit Lyonnais Securities Asia Red Chip Index. The combined value of red chips and H shares on the SEHK was approaching HK$232 billion (i.e. US$30 billion) in June 1997 (Leung and Surry, 1997).

Past studies in China equities have focused on the return behavior of A and B

shares listed on the Shanghai and Shenzhen Stock Exchanges. We believe that insufficient research effort has been devoted to the return behavior and the relationship among H shares, red chips, Shanghai and Shenzhen equities. Our study shows that stock returns of these Chinese stocks have fatter tails relative to the normal distribution. We have carried out our analysis using the EGARCH-M model along with GED, which allows for variable kurtosis in the data.

Both types of shares (H shares and red chips) might be influenced by some

common factors like political risk and government influence. Information might have been transmitted between the issuers of red chips and the issuers of H shares. The return behavior of these two types of shares and the return volatility between the two markets might be related. We, therefore examine the spillover effects among the H share, red-chip, Shanghai and Shenzhen security markets. An examination of the linkages across the four markets may shed light on how investors perceive the information flow across markets.

The paper is organized as follows. The next section presents the background to our research. The third section describes the research design and methodology. Empirical results of this study are discussed in the fourth section. The final section gives some concluding remarks.

2. Background to the research

A brief history of China’s securities markets is described and selected literature on EGARCH models is reviewed in this section.

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2.1. History of China’s securities markets

There are two major stock exchanges in China, namely, the Shanghai Stock Exchange (SHSE) and the Shenzhen Stock Exchange (SZSE). Both offer Class A shares (A shares) and Class B shares (B shares) of common stocks issued by Chinese domiciled companies. B share and H share listings all carry the same rights as the A share listings. For example, they receive the same dividends (although in different currencies). For all intents, B and H shares are identical to A shares except for who can buy them; A shares are restricted to PRC citizens while B and H shares are restricted to non-PRC citizens.

The SHSE (formerly known as the Shanghai Securities Exchange) was founded on November 26, 1990 and began to operate in December of the same year. Four major classes of securities are listed on the SHSE as follows: equities (A and B shares), debts (government, corporate and financial debts), funds (including other trust beneficiary receipts), and other financial instruments. The SZSE was estab-lished on December 1, 1990. Five major types of securities are traded on the SZSE as follows: stocks (A and B shares), bonds (corporate, convertible and treasury bonds), funds, warrants/rights and treasury bond repurchases. As of December 1998, there are 425 A shares and 52 B shares listed on the SHSE while there are 400 A shares and 54 B shares listed on the SZSE. At the same time, 41 China-domiciled companies have H shares listings in Hong Kong and there are 47 red chips listed on the SEHK (Hang Seng Index Service Limited (HSI web): www.hsiservices.com).

Bailey (1994) and Johnson et al. (1994) provide preliminary empirical evidence on the financial characteristics of China’s equity markets. Bailey (1994) studied the early evolutionary stage of both the Shanghai and Shenzhen stock markets and found that B-share returns displayed little or no correlation with international equity index returns. The results of Bailey’s study imply that B shares can be considered good diversification investments for foreign investors and confirmed the effectiveness of market segmentation in the A and B share markets. Poon et al. (1998) have also found that Chinese capital markets appear to be segmented. Similarly, Johnson et al. (1994) examine the risks and returns on the SZSE over the period September 1, 1991 to September 5, 1993 and found that all equities listed on the SZSE have extreme volatility.

Song et al. (1998) have recently investigated the relationship between return and volatility on the SHSE and SZSE in China during the period May 21, 1992 to February 2, 1996 using GARCH models. The results of their study document significant volatility transmission between the two stock exchanges and the Chinese stock. Su and Fleisher (1998) have also studied the return and risk behavior in Chinese stock markets in terms of local and global information variables that could predict the excess returns of the Chinese stock markets. Their study indicates that the volatility of Chinese stock markets is time-varying and mildly persistent, also that the market intervention policies of the Chinese government have influenced stock market volatility.

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2.2. Selected literature on EGARCH models

The generalized autoregressive conditional heteroscedasticity (GARCH) models have been widely applied in different time series studies (Cheung and Ng, 1992a; Antoniou and Holmes, 1995; Chan and Wu, 1995; Tse and Booth, 1996; Liu et al., 1996; Song et al., 1998). The GARCH models incorporate time-varying returns and time-varying volatility which can deal with the problem of autocorrelation and heteroscedasticity in the time series data.

The GARCH model does not, however, address the issue of asymmetric volatility effects on stock returns.3_{It imposes a non-negativity constraint on the parameters}

of past conditional variance (d) and past volatility shock (g) in the volatility equation such that the sum (d+g) must be B1 for the volatility process to be

covariance stationary.4

The Nelson (1991) EGARCH model relaxes the restrictions of the GARCH model and incorporates the asymmetric volatility effect in the volatility equation. There are numerous papers using the EGARCH model to examine the behavior of stock returns of national stock markets, such as Cheung and Ng (1992b), Koutmos et al. (1993), Episcopos (1996) and Booth et al. (1997). They all find that the EGARCH model can adequately capture the stochastic behavior of return and volatility in stock markets.

3. Research design and methodology

3.1. Sample data and study period

The data include the four daily indices: H shares (HSCEI), red chips (HSCCI)5

, Shanghai Composite Index (SHI), and Shenzhen Composite Index (SZI) for the period August 4, 1994 to June 27, 1997.6_{Data on the HSCEI and the HSCCI were}

obtained from EXTEL Equity Research Database (EXTEL, 1998), whereas data on the SHI and the SZI was supplied by the Hong Kong branch of the Taiwan Economic Journal (TEJ, 1998).

The official definition of H shares from the SEHK is used in the study. That is, H shares refer to ‘overseas listed foreign shares which are listed on the Exchange and subscribed for and traded in Hong Kong dollars’. Specifically, they are ‘foreign shares issued by a PRC issuer under PRC law, the par value of which is denominated in Reminbi and which are subscribed for in a currency other than Reminbi (Hong Kong dollars for H shares)’. The H-share issuers have to comply 3_{The asymmetry effect in several stock markets has been found by Booth et al. (1997) for Denmark,} Norway, Sweden and Finland, Koutmos et al. (1993) for Greece, Cheung and Ng (1992b) for the US, Koutmos (1992) for Canada, France and Japan and Poon and Taylor (1992) for the UK.

4_{See Section 3.3 for details of the models.}

5_{See Table 1 and Table 2 for the list of H shares and red chips included in the HSCEI and HSCCI,} respectively.

6_{The sample period ended right before the Hong Kong hand-over on July 1, 1997 and the Asia} financial crisis.

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W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 321 with additional requirements set by the SEHK (SEHK, 1997). H shares are companies incorporated in China and are listed in Hong Kong. They denote the issued shares of China enterprises (mainly state-owned China enterprises) listed in Hong Kong. The H-share companies are typically concentrated in heavy industries (i.e. steel or petrochemicals) like Maanshan Iron and Shanghai Petrochemical.

Since there is no official definition of red chips announced by the SEHK, the selection criteria for the constituent companies to be included in the HSCCI by Hang Seng Index Services Ltd., are used in this study (HSI, 1997b). That is,

The company should have at least 35% shareholding directly held by either: (a) China entities which are defined to include state-owned organizations, provincial or municipal authorities in China; OR

(b) Listed or privately owned Hong Kong companies (Hong Kong or overseas incorporated) which are controlled by a. above;...

Tables 1 and 2 display the composition of the Red Chip and H-Share Indices. The data for the two tables are based on the information provided by HSI (1997a, 1998), SEHK (1999) and HSI web (Hang Seng Index Service Ltd. (HSI web): www.hsiservices.com). As expected, Tables 1 and 2 indicate that companies included in the H-Share Index are primarily industrial firms while red chips are more diverse and include industrial, consolidated enterprises and financial companies. In addition, red chips appear to be bigger in size compared to the H shares.

Table 3 displays the distribution of the four indices (Red Chip, H-Share, Shanghai Composite and Shenzhen Composite Indices) among different industries over the years 1993 – 1998. The data are collected from annual reports on SHSE (1994, 1996, 1997, 1998), TEJ (1998), Shenzhen Securities Market (1997) and SZSE (1997a,b, 1998). Most of the companies in each of the respective indices, during the period 1993 – 1998, are industrial firms. So, we would expect that these four indices would be closely related and would show common characteristics of information flow over time.

3.2. Descripti6e statistics

Table 4 shows descriptive statistics for the return of the four indices: HSCEI, HSCCI, SHI and SZI. The HSCCI, HSCEI, SHI and SZI returns are positively skewed. The excess kurtosis measures indicate that all index returns are highly leptokurtic and do not follow the normal distribution. The Bera – Jarque test statistics rejects normality for all index returns.7_{Ljung – Box [LB}_(Q)_{] statistics for ten lags on}

the HSCEI and HSCCI returns indicate the presence of serial correlation.8_{Also, LB}_(Q)

statistics on all squared index returns show high autocorrelation.

7_{The Bera – Jarque statistic is given by (}_n₋_k₎₍_S2_/_6-_K2_/_{24), where}_n_{is the number of observations,}_k is zero for an ordinary series and equal to the number of regressors when working with the residuals of an equation,Sis skewness, andKis excess kurtosis. Bera – Jarque statistic for normality is distributed as ax2_{. See Jarque and Bera (1980).}

8_{The Ljung – Box [LB}₍_Q₎_{] statistics is used to test whether a series is uncorrelated. This is calculated} using the formula LB(N)=T(T+2)kN=1r2k/(T−k), whererk, fork=1, . . ,Nis lagksample autocor-relation of the series, andTis the sample size. LB(N) is asymptotically distributed as x2 _with_N_dof.

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H shares (China enterprises) included in the Hang Seng China-enterprises index (HSCEI) Market

Company name Industry

Security code % Total market

No. Listing date Total assets

(US$)* classification capitalization capitalization

(US$)

60 887 888 0.018 07-24-97 740 684 535 Angang New Steel Co. Ltd.

1 0347 Industrial

10-21-97 293 207 137 0.012

2 0914 Anhui Conch Cement Co. Ltd. Industrial 40 074 741 0.017

0995 Anhui Expressway Co. Ltd. Industrial 57 911 151 11-13-96 349 589 822 3

429 365 548

Beijing Datang Power Generation 0.125 03-21-97 1 598 265 774

0991 Utilities

Co. Ltd.

05-14-97 830 710 286 113 166,647

Properties

0588 Beijing North Star Co. Ltd. 0.033

91 441 556

Beijing Yanhua Petrochemical Co. 0.027 06-25-97 918 023 536

0325 Industrial

Ltd.

0.002 08-06-93 133 466 423 7 0187 Beiren Printing Machinery Holdings Industrial 5 485 977

Ltd.

09-29-97 18 463 408 0.004

14 525 577 0161

8 Catic Shenzhen Holdings Ltd. Industrial

10 946 137

Chengdu Telecommunications Cable 0.003 12-13-94 182 187 911

1202 Industrial

Co. Ltd.

10 0670 China Eastern Airlines Corp. Ltd. Consolidated 105 177 666 0.031 02-05-97 3 243 390 399 enterprises

0.033 11-11-94 1 117 444 369 Others

11 1138 China Shipping Development Co. 112 084 324 Ltd.

China Southern Airlines Co. Ltd. Consolidated 18 008 904

12 1055 122 767 672 0.036 07-31-97

enterprises

10-17-97 447 134 076 13 1053 Chongquing Iron and Steel Co. Ltd. Industrial 20 304 404 0.006

0.003 06-06-94 268 548 424 10 094 198

14 1072 Dongfang Electrical Machinery Co. Industrial Ltd.

0038 First Tractor Co. Ltd. Industrial 0.022 06-23-97 380 437 159

15 76 539 061

Guangdong Kelon Electrical 409 340 341 0.119 07-23-96 606 068 902 0921

16 Industrial

Holdings Co. Ltd.

05-14-96 1 298 056 487 166 279 316

Utilities

Guangshen Railway Co. Ltd. 0.048

17 0525

0874 Guangzhou Pharmaceutical Co. Ltd. Industrial 0.007 10-30-97 374 700 249

18 23 275 774

17 955 678

Guangzhou Shipyard International 0.005 08-06-93 371 193 179

0317 Industrial

Co. Ltd.

12-16-94 1 041 348 989 0.010

34 518 551 20 1133 Harbin Power Equipment Co. Ltd. Industrial

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Table 1 (Continued)

Market

Company name Industry

Security code % Total market

No. Listing date Total assets

(US$)* classification capitalization capitalization

(US$)

513 100 230 0.149 01-21-98 4 286 472 572 Huaneng Power International Co.

21 0902 Utilities

06-27-97 1 487 284 848 0.078

22 0177 Jiangsu Expressway Co. Ltd. Industrial 266 577 190 0.019

0358 Jiangxi Copper Co. Ltd. Industrial 65 683 550 06-12-97 568 818 429**

51 059 471 0.015 05-23-95 1 867 100 225 Jilin Chemical Industrial Co. Ltd.

24 0368 Industrial

02-02-96 135 997 973 0.002

Industrial

Jingwei Textile Machinery Co. Ltd. 7 468 157 25 0350

0.001

0300 Kunming Machine Tool Co. Ltd. Industrial 2 559 047 12-07-93 78 130 554 26

8 003 072 0.002 07-08-94 415 920 347 27 1108 Luoyang Glass Co. Ltd. Industrial

78 291 411 0.023 11-03-93 2 161 103 191 28 0323 Maanshan Iron and Steel Co. Ltd. Industrial

05-02-96 449 776 602 0.003

10 933 229 29 0553 Nanjing Panda Electronics Co. Ltd. Industrial

30 0042 Northest Electrical Transmission and Industrial 16 981 293 0.005 07-06-95 407 027 561** Transformation Machinery

Manufacturing Co.

08-17-94 1 052 150 274 Industrial

31 1122 Qingling Motors Co. Ltd. 172 040 244 0.050

Shandong Xinhua Pharmaceutical Co. 15 489 818 0.005 12-31-96 150 897 494 0719

32 Industrial

Ltd.

07-26-93 2 526 800 609 33 0338 Shanghai Petrochemical Co. Ltd. Industrial 210 532 435 0.061

03-12-97 395 050 268** 0.051

34 0548 Shenzhen Expressway Co. Ltd. Industrial 173 679 576 0.023

0107 Sichuan Expressway Co. Ltd. Industrial 78 587 281 10-07-97 650 505 020 35

0.004 05-17-94 833 696 640 36 1065 Tianjin Bohai Chemical Industry Industrial 14 702 419

Group Co. Ltd.

07-15-93 474 523 728 36 713 062

Tsingtao Brewery Co. Ltd.

0168 Industrial 0.011

1171 Yanzhou Coal Mining Co. Industrial 0.042 04-01-98 566 968 123

38 144 829 795

128 307 319 0.037 03-29-94 1 565 858 323 39 1033 Yizheng Chemical Fibre Co. Ltd. Industrial

290 582 738 0.085 05-15-97 1 384 996 912 0576

40 Zhejiang Expressway Co. Ltd. Industrial

0.033 12-02-94 1 130 083 833 111 174 018

41 1128 Zhenhai Refining and Chemical Co. Industrial Ltd.

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Table 1 (Continued)

Market

Company name Industry

Security code % Total market

No. Listing date Total assets

capitalization (US$)*

classification capitalization (US$)

36 820 093 495 1.257

4 319 437 561 Total

0.031 898 051 061

Mean 105 352 136

65 683 550 0.019 568 818 429

Median

4 286 472 572 0.149

513 100 230 Maximum

0.001 18 008 904

Minimum 2 559 047

343 578 708 964 1.000 Total market capitalization (US$)

* The total assets are as at December 31, 1997.

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Table 2

Red chips (China-affiliated corporations) included in the Hang Seng China-affiliated corporations index (HSCCI) Market

Industry

Company name % Total market

Security

No. Listing date Change date† _{Total assets}

code _{classification} _{capitalization} _{capitalization} (US$)‡

(US$)

0.258

1 0154 Beijing Development Hong Industrial 12 730 379 Aug. 1970* 05-29-98 43 574 113 Kong

88 795 410 0.004 05-29-97 08-31-98 1 239 569 663 2 0392 Beijing Enterprises Holdings Consolidated

enterprises Ltd.

0.032 08-11-97 11-30-98

Industrial 91 508 426

3 1185 CASIL Telecommunications 111 526 688 Holdings Ltd.

China Aerospace 172 246 091 0.050 08-25-81 06-25-93 959 016 201

0031

4 Industrial

International Holdings Ltd. China Everbright

0257 191 364 306 0.056 Feb. 1973* 05-07-93 492 708 088

5 Consolidated

International Ltd. enterprises

0.155 02-26-73 12-31-97

Consolidated 532 524 710 993 609 142

China Everbright Ltd. 0165

enterprises

Industrial 107 432 674 0.031 12-10-91 12-31-97 66 987 747 0256 China Everbright

Technology Ltd.

0.045 07-18-90 07-23-93 221 909 755 154 287 816

8 0506 China Foods Holdings Ltd. Consolidated enterprises

0.348 07-15-92 01-04-93 1 188 508 632 9 0144 China Merchants Holdings Industrial 1 194 235 805

International Co. Ltd.

0.200 08-20-92 01-04-93 2 411 702 195 Properties 686 241 206

10 0688 China Overseas Land and Investment Ltd.

118 723 688 0.035 06-21-94 06-22-94 163 053 041 11 1093 China Pharmaceutical Industrial

Enterprises and Investment Corp. Ltd.

0.109 11-08-96 03-31-98

Properties 812 592 091

China Resources Beijing

1109 372 809 787

Land Ltd.

0.706 Jan. 1973* 01-04-93 2 704 374 104 13 0291 China Resources Enterprise Properties 2 425 687 477

Ltd.

419 659 826 0.122 11-11-92 01-04-93 1 288 025 092 Consolidated

14 0308 China Travel International

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Table 2 (Continued)

No. Security Company name Industry Market % Total market Listing date Change date† _{Total assets} classification _{capitalization} _{capitalization} (US$)‡ code

(US$)

Others 74 544 746 0.022 05-23-97 08-31-98 105 434 560 0560 Chu Kong Shipping

Development Co. Ltd.

07-17-80 01-04-93 4 458 600 567 768 115 397 0.224

Finance CITIC Ka Wah Bank Ltd.

0183 16

0.165

0135 CNPC (HK) Ltd. Others 566 460 570 03-13-73 07-12-93 129 075 473

0.027 08-30-73 02-18-93 435 786 223** 92 608 921

18 0119 Continental Mariner Others Investment Co. Ltd.

0.030 02-11-92 05-29-98 290 261 739 19 0517 Cosco International Holdings Industrial 104 369 923

Ltd.

0.249 12-19-94 12-20-94

Consolidated 854 152 479 1 426 899 000

Cosco Pacific Ltd. 1199

enterprises

02-22-93 02-23-93 109 232 739 0.021

72 433 786 21 0203 Denway Investment Ltd. Industrial

177 588 581 0.052 12-21-95 12-22-95 175 879 879 0418

22 Founder Hong Kong Ltd. Industrial

0.010 03-26-97 05-29-98 105 022 063 33 161 915

Consolidated 23 0340 GITIC Enterprises Ltd.

enterprises

0.044 08-08-97 11-30-98 243 995 283 24 0124 Guangdong Brewery HoldingsIndustrial 151 417 417

Ltd.

0.145 Jan. 1973* 01-04-93

Consolidated 497 854 031 3,044 582 506

Guangdong Investment Ltd. 0270

enterprises

12-16-96 03-31-98 126 433 862 0.005

17 592 617 26 1058 Guangdong Tannery Ltd. Industrial

180 942 432

Guangnan Holdings Ltd. Consolidated 0.053 12-09-94 12-31-94 898 845 764 1203

enterprises

0.105 12-15-92 01-04-93 1 848 459 956 Properties

0123 Guangzhou Investment Co. 359 701 320 28

Ltd.

1052 GZI Transport Ltd. Industrial 0.059 01-30-97 03-31-98 487 899 961

29 203 562 683

Industrial 29 283 688 0.009 12-20-93 12-21-93 199 685 868 0382 GZITIC Hualing Holdings

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Table 2 (Continued)

No. _Security _{Company name} Industry Market % Total market Listing date Change date† _{Total assets} (US$)‡ capitalization

capitalization

code classification

(US$)

02-14-94 02-15-94 684 099 085** 0.178

31 0992 Legend Holdings Ltd. Industrial 612 287 934 0.013

0222 Min Xin Holdings Ltd. Finance 46 140 869 06-28-82 12-31-97 190 713 658 32

0318 920 863 519 0.268 10-25-95 10-26-95 359 811 721

33 Ng Fung Hong Ltd. Consolidated enterprises

12-20-91 06-15-93 359 628 188 27 908 895

Properties 0.008

34 0230 ONFEM Holdings Ltd.

23 144 351 0.007 12-15-94 12-16-94 397 273 625 35 1208 Oriental Metals Holdings Co. Consolidated

enterprises Ltd.

0.010 04-06-88 09-01-93

Properties 36 045 668 135 760 903**

Poly Investment Holdings 0263

Ltd.

Industrial 1 699 576 061 0.495 05-30-96 05-31-96 1 727 465 364 Shanghai Industrial Holdings

0363 37

Ltd.

Feb 1973* 08-04-93 218 774 769 26 985 928 0.008

Finance Shenyin Wanguo HK Ltd.

0218 38

70 434 938 0.021 09-25-72 12-31-97 152 169 271 39 0152 Shenzhen International Consolidated

enterprises Holdings Ltd.

17 585 618 0.005 04-09-92 12-02-93

Consolidated 188 061 464

40 0103 Shougang Concord Century

enterprises Holdings Ltd.

0.010 08-08-91 07-20-93 86 159 877 41 0730 Shougang Concord Grand Properties 34 408 268

Group Ltd.

42 0697 Shougang Concord Consolidated 73 542 821 0.021 04-30-91 01-04-93 979 675 412 enterprises

International Enterprises Co. Ltd.

0.009 12-23-88 05-31-93

43 0521 Shougang Concord Industrial 29 320 409 110 065 091

Technology Holdings Ltd.

03-07-97 05-29-98 580 998 314 250 434 855

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Fung

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Fin

Manag

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315

–

343

328

Table 2 (Continued)

Market Company name

No. _{Security code} _Industry % Total market Listing date Change date† _{Total assets} capitalization

classification capitalization (US$)‡

(US$)

0.021

45 0409 Stone Electronic Industrial 73 322 502 08-11-93 08-17-93 210 439 974

Technology Ltd.

272 796 029 0.079 02-28-73 11-26-93

Properties 993 676 065

Top Glory 0268

International Holdings Ltd.

Union Bank of Hong 240 258 020 0.070 03-14-73 01-04-93 2 766 665 880 0349

47 Finance

Kong Ltd.

36 904 672 394

Total 16 026 223 053 4.664

785 205 796 0.099

Mean 340 983 469

0.045 359 811 721

Median 154 287 816

2 425 687 477 0.706 4 458 600 567

Maximum

12 730 379 0.004 43 574 113

Minimum

1.000 343 578 708 964 Total Market

Capitalization (US$)

†_{This is the date the company becomes the Hang Seng China-affiliated corporations index (HSCCI) constituent stocks.} ‡_{The total assets are as at December 31, 1997.}

* The exact listing date in 1970’s cannot be found.

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315

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343

329

Table 3

The distribution of H shares, Red chips, Shanghai A- and B-shares and Shenzhen A- and B-shares by industry and yeara Panel A

Year Red chips H-shares Total

Fin Total Con Ind Oth Pro Uti Total Con Ind Oth Pro Uti

2 24 0 6* 0 0

7* 0

1993 6 7* 2 0 6 30

9 10* 2 0 2 29 0 14* 1 0 0 15 44

1994 7

2 31 0 16* 1 0 0 17

1995 10* 10* 2 7 0 48

2 32 0 21* 1 0 1

0 23

2 7 55

1996 11* 10

12* 11 2 7 4 36 2 33* 1 1 2 39 75

1997

4 47 2 34* 1 1 3

1998 14 17* 3 9 0 41 88

Panel B

Shanghai B-shares Shanghai composite

Shanghai A-shares Year

Total Com

Com Ind Mis Pro Uti Ind Mis Pro Uti Total Total

8 8 101 1 16* 1 3 1 22 123

1993 61* 8

169 3 24* 3 3 1 34

32 203

1994 95* 21 9

33 104* 26 9 184 3 26* 3 3 1 36 223

1995

45 163* 48 9 287 3 30* 4 3 2 42 329

1996

372 3 37* 4 3 3 50

1997 49 214* 70 9 422

425 3 38* 4 3

1998 49 252* 82 9 33 4 52 477

Panel C

Shenzhen B-shares Shenzhen composite

Year Shenzhen A-shares

Fin

Com Ind Mis Pro Uti Total Com Ind Mis Pro Uti Total Total

1993 1 24* 7 7 4 1 44 1 16* 1 3 1 22 66

2 88 3 24* 3 3 1

9 34

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315

–

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330

Table 3 (Continued)

Panel C

Shenzhen A-shares

Year Shenzhen B-shares Shenzhen composite

Fin Total Com

Com Ind Mis Pro Uti Ind Mis Pro Uti Total Total

2 88 3 26* 3 3

9 9 1 36 124

11 51* 6

1995

18 15 4 227 2 28* 3 3 7 43 270

1996 143* 24

3 348 2 36* 3 3 7

18 51

18 399

1997 36 230* 43

1998 37 268* 50 18 24 400 2 39* 3 3 7 54 454

a_{Con, consolidated enterprises; Com, commercial; Fin, finance; Ind, industrial; Mis, miscellaneous; Oth, others; Pro, property; Uti, utilities.} * Indicates the highest percentage in the year.

(17)

W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 331 The analysis of the preliminary statistics indicates that the distributions of all index returns are more leptokurtic than the normal, the returns exhibit autocorrela-tion, and their conditional variances are heteroskedastic. An EGARCH model along with the generalized error distribution (GED) is, therefore, recommended for the following empirical analysis.

3.3. Statistical methodology

3.3.1. Return and 6olatility beha6ior analysis

The daily returns are computed as the change in the logarithm of closing indices. The daily return of the HSCEI is:

Rt=ln(It)−ln(It-1) (1)

where Rt is the return of HSCEI at time t; It is the level of HSCEI at time t,

andIt−1 is the level of HSCEI at time t−1.

Similarly, the daily returns of other indices are also computed as Eq. (1). A set of equations for the HSCEI described in this section is also applied to the other three indices with different superscripts. The superscripts, †, ,

) are assigned to the same variable or parameter for the HSCCI, SHI and SZI, respectively.

The statistical methodology used to explore the return behavior and the volatility of the HSCEI, HSCCI, SHI and SZI is based on Nelson’s EGARCH model (Nelson, 1991). A conditional variance is added into the conditional mean equation for testing the relationship between mean and volatility in both index returns known as the EGARCH-in-mean (EGARCH-M) model. The H-Share Index

Table 4

Descriptive statisticsa

Shanghai composite

Red chip index Shenzhen composite H share index

Statistics

(HSCEI) (HSCCI) index (SHI) index (SZI)

Mean return −0.0004 0.0016 0.0009 0.0014

0.0010 0.0010

0.0003 Variance 0.0004

0.7122*** 0.4012*** Skewness 1.1529*** 0.5085***

Excess kurtosis 5.3585*** 5.6289*** 8.0707*** 7.0820*** −1793.2883***

−870.9845*** −1406.9349*** −664.8550***

Bera–Jarque statistics

6.6427 44.4567***

38.9747*** 6.2472

LB(Q)(10)

LB(Q2_{) (10)} _35.0966*** _44.8764*** _98.6237*** _113.5329*** a_LB₍_Q₎₍_n_{) and LB}₍_Q2₎₍_n_{), Ljung–Box}_Q_{statistics following a}_x2_with_n_dof.

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332

Table 5

EGARCH-M model-return and volatility without spillover effect The conditional return and conditional variance for Rt, Rt†, Rt

and Rt° are: Rt=i5=1liDi+ni=1fiRt−i+aht+ot (2a)

ht=exp

!

i5=1ciDi+ip=1diln(ht−i)+iq=1gig(zt−i)

"

(2b) Rt

†₌ i=1 5 _l

i †_D

i †₊

i=1 n _f

i †_R

t−i † ₊_a†_h

t †₊_o

t † _(2c)

ht†=exp

!

i5=1c†iDi†+ip=1di†ln(ht†−i)+iq=1gi†g(zt†−i)

"

(2d) Rt=5i=1liDi+ni=1fiRt−i+aht+ot (2e)

ht=exp

!

i5=1ciDi+ip=1diln(ht−i)+iq=1gig(zt−i)

"

(2f) Rt°=i5=1li°Di°+ni=1fi°Rt°−i+a°h°t+ot° (2g)

ht°=exp

!

i5=1ci°Di°+ip=1di° ln(ht° )−i+iq=1gi°g(zt° )−i

"

(2h)

whereRt, Rt†, Rt, Rt°return of HSCEI, HSCCI, SHI, and SZI at dayt, respectively; Di, Di†, Di, Di°dummy variable representing the dayi, (i=1, 2, 3, 4, 5), of the week for return of HSCEI, HSCCI, SHI, and SZI;u,u†_,_u_,_u_{°, asymmetry parameters of HSCEI, HSCCI, SHI, and SZI, respectively;} 6,6†,6,6°, tail thickness parameters. When6=2, the GED becomes the normal distribution. When6B_{2, the distribution of}_o

thas thicker tails than a normal distribution. When6\_{2, the distribution of}_o

thas thinner tails than a normal distribution.ot,ot †_,_o

,ot°, conditional error term of HSCEI, HSCCI, SHI, and SZI at dayt, respectively;zt,zt

†_,_z t

,zt°, standardized residuals HSCEI, HSCCI, SHI, and SZI at dayt, respectively;ht,ht †_,_h

_,_h

t $ , conditional variance HSCEI, HSCCI, SHI, and SZI at dayt, respectively.

Red chip index

Coefficient H-share index Coefficient Coefficient Shanghai composite Coefficient Shenzhen composite index (SZI)

(HSCCI) index (SHI)

(HSCEI)

Return equation:

l1 l1

−0.0022* g1 0.0041**

† _0.0005 _l

0.0008

l2 l2

−0.0014 g2† −0.0010 l2 −0.0009

−0.0017

l3 $

0.0008

0.0008 l3 0.0028*

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W . P . H . Poon , H .-G . Fung / J . of Multi . Fin . Manag . 10 (2000) 315 – 343 333

Table 5 (Continued)

−0.0019 l4

−0.0020

l4 g4

†

−0.0018* 0.0002

0.0026* l5

0.0042***

l5 g5

†

−0.0003 0.0007

f1 $ −0.0428 −0.0679** f1

f1 0.1793*** f1† 0.1954***

−0.3141 0.4950 a$

0.2094

a† _0.1718

a a

Variance equation:

$ ₋_1.2397***

c1†

−0.5917** −0.7917*** c1

−1.0099**

$ ₋_1.8879***

c2 −0.8146** c2† −1.3599*** c2

−1.8040*** c3 $ −1.4421*** −1.0828** c3

c3 −1.3982*** c3† −1.3993***

−0.8121** −1.4257*** c4

−1.4161***

† ₋_1.0881***

c4 c4

c5 $

−1.7297*** −1.4276***

c5 −1.1222*** c5

−1.3913***

c5 †

0.8157***

0.8790*** d1

0.7894***

d1† 0.8534*** d1

0.4167***

0.3548*** g1

0.5385***

g1† 0.4614*** g1 g1 u$ −0.1124 −0.2247 u

u _0.0775 u† 0.0959

0.9093*** 6$ 0.9803***

6†

V 1.0841*** 1.0282***

LB(Q)(10) 7.9085 5.3041

LB(Q)(10) 14.2972

LB(Q)(10) 10.7930 LB(Q)(10)

LB(Q2_{) (10)}

LB(Q2_{) (10)} _11.8271 _LB(_Q2_{) (10)} _9.0235 _LB(_Q2_{) (10)} _2.4738 _3.5148 * Denotes significance at the 10% levels.

** Denotes significance at the 5% levels. *** Denotes significance at the 1% levels.

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W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 334

(HSCEI) return process is modeled in Eq. (2a) and Eq. (2b) and other indices are formulated similarly with the above superscripts (†, ,

). The HSCEI return process is:

Rt=%i=1 5

liDi+%i=1

fiRt−i+aht+ot (2a)

ht=exp

!

%i=1

ciDi+%i=1

diln(ht−i)+%i=1

gig(zt−i)

"

(2b)

whereot, i.i.d. generalized error distribution (GED) with scaling6;g(z

t),uzt+zt−

Ezt; Ezt, v2 1/6

G(2/6)G(1/6); v, [2−2/6G(1/6)G(3/6)]1/2; z

t, ot/ht; Rt, return of

HSCEI at dayt; Di, dummy variable representing the dayi, (i=1, 2, 3, 4, 5), of the

week for return of HSCEI; u, asymmetry parameters of HSCEI;l,f,h,a,c,d,g, parameters of HSCEI; G(.), gamma function; 6, tail thickness parameters. When 6=2, the GED becomes the normal distribution. When6B2, the distribution ofot

has thicker tails than a normal distribution. When 6\2, the distribution ofot has

thinner tails than a normal distribution. ot, conditional error term of HSCEI at day

t;zt, standardized residuals HSCEI at dayt;ht, conditional variance HSCEI at day t.

Eq. (2a) is the conditional mean function which is specified as a linear function of day-of-the-week effects (i=1

liDi), past returns (i=1 n

fiRt−i), and the

condi-tional variance (aht). Statistically significant values forfiimply that past

informa-tion can be used to forecast current and future movements of the series. The parameteratests for linkages between the mean and variance conditional moments of the distribution of each return. A significant value for aimplies that conditional volatility triggers movements in the return.

The conditional variance in Eq. (2b) is specified as an exponential function of day-of-the-week effects (i=5 1ciDi), the natural logarithm of past conditional

variances (i=1p diln(ht−i)) and past volatility shocks (i=q 1gig(zt−i)). Significant

values fordiand giindicate that the volatility of index returns can be predicted by past volatility information and past unexpected volatility shocks.

The normal probability density function has been one of the most popular density functions used to characterize the distribution of financial time-series.9

However, our preliminary evidence presented in the previous section indicates that all index returns exhibit excess kurtosis beyond that permitted by the normal distribution, i.e. they are leptokurtic. To accommodate this need, the generalized error distribution is used.

The EGARCH-in-mean models are estimated by maximizing the following log-likelihood function, LT, (Nelson, 1991):

LT =%t=1

T _ln(₆

/v)−0.5ot/v6

−(1+6−1)ln(2)−ln[G(1/6)]−0.5 lnh

t (3)

where T is the number of observations.

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W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 335

3.3.2. Spillo6er effect analysis

The spillover analysis in terms of return and volatility among the four markets is investigated by the multivariate-EGARCH-M model.10 _{The models of spillovers}

between the HSCEI return and the other index returns (HSCCI, SHI, SZI) are written as:

Rt=%i=15 liDi+f1Rt−1+f2Rt−† 1+f3Rt−1+f4Rt−1 $

+aht+ot (4a)

ht=exp

!

%i=15 ciDi+d ln(ht−1)+g1g(zt−1)g2g(zt†−1)+g3g(zt−1)

+g4g(zt−1

)} (4b)

The conditional mean return presented in Eq. (4a) is specified as a linear function of day-of-the-week effects, past own returns (Rt−1), its own conditional variance

(ht), and past returns of other three indices ((Rt−1

† _{, R}

t−1

_{, R}

t−1

). A statistically significant value for f2, f3 and f4 indicates that the past returns of other indices correlates with the current and future return of the HSCEI, a result indicative of return spillovers from other markets to H share market.

The conditional variance ht in Eq. (4b) is specified as an exponential function of

day-of-the-week effects, natural logarithm of past conditional variance, past volatil-ity shock, and past volatilvolatil-ity shocks of other indices. A statistically significantg2,g3,

g4 implies that there is volatility spillover from the other markets to the H share market. The models of return and volatility spillover for the HSCCI, SHI and SZI follow Eq. (4a) and Eq. (4b).

We use a two-stage maximum likelihood (ML) estimation procedure to obtain the parameters of Eq. (4a) and Eq. (4b).11

In stage one, the ML method is used to estimate the four univariate models given by Eq. (2a) and Eq. (2b). These models are subsequently used to calculate standardized residuals for the four index returns. In stage two, parameter estimates for Eq. (4a) and Eq. (4b) are obtained by taking the standardized residuals of the other index returns (HSCCI, SHI, SZI) as

10_{We find that the four index returns are reasonably predicted by an AR(1)-EGARCH(1,1)-in-mean} for the univariate analysis; thus we continue to use AR(1)-EGARCH(1,1)-in-mean in the multivariate framework.

11_{We acknowledge the possibility of using truly multivariate EGARCH models suggested by an} annoymous referee. However, the multivariate density function for the GED is unknown, the truly multivariate EGARCH model requires stringent assumption of normality and the normality assumption does not hold in our data as shown in Table 4. Given our data characteristics, the EGARCH models with the GED error densities, therefore, appear to be a reasonable choice for our estimation. We do not model the joint distribution of stock returns using a vector autoregression model with errors following a multivariate exponential GARCH process that requires normality assumption like Christofi and Pericli (1999) and Bollerslev and Wooldridge (1992). We also thank the referee for pointing out the potential problems with generated regressors mentioned in Pagan (1984). As the error terms (i.e. the conditional variance terms) for each index returns are generated from individual univariate EGARCH estimations and then, subsequently used as independent variables in follow-up estimations, the generated conditional variances may be inconsistent estimators of the true conditional variances.

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W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 336

independent variables in the conditional volatility equation. A similar procedure is also applied to the other three indices (Theodossiou, 1994).

4. Discussion of empirical results

4.1. Return and 6olatility beha6ior analysis

Table 5 reports the results for the return behavior and the volatility of the HSCEI, HSCCI, SHI, and SZI by EGARCH-M model. We employ the iterative procedure by Berndt et al. (1994) to maximize the log-likelihood function LT and

determine the lag lengths for the conditional mean and variance on the basis of the Schwarz criterion (SC) and Ljung – Box QandQ2 _{statistics [LB}_(Q)_{and LB}_(Q2_)].12

Ljung – Box statistics are not significant at the 5% level, indicating that the model does not have autocorrelation and heteroscedasticity error. In all cases, an AR(1)-EGARCH(1,1)-mean process appears to provide an adequate representation of the time-series properties of each index return.13

The parameters (a,a†_, _a _, _a

) are statistically insignificant, indicating no relationship between the conditional variance and conditional mean of the index returns. The findings suggest that volatility does not have any significant impact on the future movement of the four index returns. These results are not surprising given the mixed results in the literature (Chan et al., 1992; Glosten et al., 1993; Whitelaw, 1994). The day-of-the-week effects in the mean equations vary among different indices. Monday and Thursday dummies have a negative effect on the HSCEI return. Wednesday and Friday dummies have a positive effect on the SHI and Monday and Friday dummies have a positive effect on the SZI. There is no day-of-the-week effect in the HSCCI mean equations. It is interesting to note that all day-of-the-week dummy variables in all volatility equations are negative and significant, suggesting that day-of-the-week has a significant negative impact on the volatility of all index returns. Cheung and Ng (1992b) find day-of-the-week effects in 251 AMEX-NYSE stock returns. We find that there are day-of-the-week effects in the HSCEI, HSCCI, SHI and SZI volatility.

There is evidence that past returns influence current and future returns with the exception of the SZI since f1,f1† and f1 are significant for the HSCEI, HSCCI,

and SHI, respectively.

The coefficients for past volatility shocks (g1,g1†, g1 , g

1) and past conditional

variances (d1,d1†,d1 ,d

1 ) are statistically significant, indicating that volatility terms

of all index returns are predictable using past information. The asymmetry

parame-12_{The Schwarz criterion used is defined as: SC}_{= −}_{(max L(}_x₎₋₍₁_/₂₎_k_log(_n_{)), where max L(}_x_{) is the} sample log-likelihood function evaluated at its maximum,kis the number of estimated parameters and

nis the sample size (Schwarz, 1978).

13_{Also an ARMA(1,1)-EGARCH(1,1)-M model was estimated. The results are less significant after} inclusion of the MA(1) term.

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W . P . H . Poon , H .-G . Fung / J . of Multi . Fin . Manag . 10 (2000) 315 – 343 337 Table 6

Multivariate EGARCH-M model-return and volatility with spillover effect The conditional return and conditional variance for Rt, Rt†, Rt

and Rt

$_are:

Rt=i5=1liDi+f1Rt−1+f2Rt−† 1+f3Rt−1

+f4Rt−1

$ ₊_a_h

t+ot (4a)

ht=exp

!

i=5 1ciDi+dln(ht−1)+g1g(zt−1)+g2g(z†t−1)+g3g(zt−1

)+g4g(zt−1

$ _)} _(4b)

Rt†=i=5 1li†Di†+f1†Rt-1+f2†R†t−1+f3†Rt−1

+f4†Rt−1

$ ₊_a†_h

†₊_o

† _(4c)

ht†=exp

!

i=5 1ci†Di†+d†ln(ht−† 1)+g1†g(zt−1)+g2†g(zt−† 1)+g3†g(zt−1

)+g4†g(zt−1

$ _)} _(4d)

=i5=1li

+f1

Rt-1+f2

Rt−1

+f3

Rt−1

+f4

Rt−1

+aht

+ot (4e)

=exp

!

i=1

5 _c

+dln(ht−1

)+g1

g(zt−1)+g2

g(zt−1

)+g3

g(zt−1

)+g4

g(zt−1

)} (4f) Rt

=i=5 1li

+f1

Rt-1+f2

Rt−† 1+f3

Rt−1

+f4

Rt−1

+a$

+ot

(4g)

$₌_exp

!

i=1

5 _c

$_D

$₊_d$_ln(_h

t−1

$ ₎₊_g

$_g₍_z

t−1)+g2

$_g₍_z

t−1 † ₎₊_g

$_g₍_z

t−1

)+g4

$_g₍_z

t−1

$ _)} _(4h)

where Rt, Rt†, Rt

, Rt

$_{, return of HSCEI, HSCCI, SHI, and SZI at day}_t_{, respectively; D}

i, Di†, Di

, Di

$_{, dummy variable representing the day of the week}_i

(i.e.,i=1, 2, 3, 4, 5) for return of HSCEI, HSCCI, SHI, and SZI;u,u†_,_u_,_u$_{, asymmetry parameters of HSCEI, HSCCI, SHI, and SZI, respectively;}

6,6†,6

,6$_{, tail thickness parameters. When}

6=2, the GED becomes the normal distribution. When6B2, the distribution ofo_thas thicker tails than a

normal distribution. When6\2, the distribution ofo_thas thinner tails than a normal distribution.o_t,o_t†,o_t

,ot

$_{, conditional error term of HSCEI,}

HSCCI, SHI, and SZI at dayt, respectively;zt,zt†,zt

,zt

$_{, standardized residuals HSCEI, HSCCI, SHI, and SZI at day}_t_{, respectively;}_h

t,ht†,ht

,ht

$_,

conditional variance HSCEI, HSCCI, SHI, and SZI at dayt, respectively.

Coefficient Shanghai composite

H-share index

Coefficient Coefficient Red chip index Coefficient Shenzhen composite

index (SHI)

(HSCEI) (HSCCI) index (SZI)

Return equation:

0.0015 l1

$ _0.0043***

l1†

l1 −0.0025* 0.0003

$ ₋_0.0010

−0.0019 l2 −0.0007

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Table 6 (Continued)

0.0023 l3

−0.0005

l3 0.0006 l3† 0.0010

l4 l4

−0.0021 l4† −0.0001 l4 −0.0011

−0.0016

$ _0.0044***

0.0016

l5 −0.0011 l5

0.0008

l5†

0.0046

0.1764*** f1

$ _0.0079

f1† 0.0130 f1

$ _0.0496*

f2 0.0520 f2† 0.1966*** f2 0.0388

$ ₋_0.0204

0.0057

f3†

0.0553* 0.0183 f3

−0.1050**

−0.0263 f4

$ ₋_0.0208

f4† −0.0063 f4 f4

a _0.1182 _a$ ₋_0.1044

0.2349

a a† ₋_0.0604

Variance equation:

$ ₋_0.9509*

−0.8196**

c1 −1.7460*** c1

−0.8918***

c1†

−1.6898***

−1.8454*** c2

$ ₋_1.6611***

c2† −1.3690*** c2

c3 $

c3 −2.4750*** c3† −1.4142*** c3 −1.1938**

−0.8055*

$ ₋_1.0905**

−1.3035*** −1.9475*** c4†

c4 c4

−1.2018***

−1.1820***

−2.3833*** c5

$ ₋_1.5268***

c5† −1.4909*** c5

d 0.8401*** d$ _0.8239***

d 0.7412*** d† _0.8448***

0.2938***

0.2476** g1

$ ₋_0.1561*

g1† −0.1028 g1

$ _0.0881

−0.2487***

g2 0.2946*** g2

0.5268***

g2†

0.1740

0.2089 g3

$ ₋_0.9105

g3† 0.0832 g3

0.2273** g4 $

1.3580*

g4†

g4 −0.1489 0.0238

−0.1035 −0.4176

0.1002

u 0.0749 u†

0.9307

1.1041*** 6† 1.0937*** 6 6$ 0.9958***

LB(Q)(10)

LB(Q)(10) 7.7220 LB(Q)(10) 13.3038 LB(Q)(10) 6.4682 7.8581

LB (Q2_{) (10)} _3.3651 12.9814 _{LB (}_Q2_{) (10)} 8.9716 LB (Q2_{) (10)} 2.2999

LB (Q2_{) (10)}

* Denotes significance at the 10% levels. ** Denotes significance at the 5% levels. *** Denote significance at the 1% levels.

(25)

W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 339 ters (u,u†_,

u1, u

1) are different in signs for the different index returns, implying

that unexpected positive (positive shocks) and unexpected negative return (negative shocks) of all indices have asymmetric effects on volatility. These results indicate that the EGARCH model is reasonably well specified in this study. In addition, given (g1,g1†,g1 andg

1) parameters are positive and significant, the positive values

foru,u† _{imply that positive shocks have a larger impact on future volatility of the}

HSCEI and HSCCI returns than negative shocks and the contrary result applies to the SHI and SZI returns, u , u

. These findings suggest that stocks listed in the Hong Kong market (H shares and red chips) are more sensitive to ‘good’ news than ‘bad’ news, while stocks listed in the China market are more sensitive to ‘bad’ news than ‘good’ news. Hong Kong investors appear to be optimistic to news while Chinese investors (i.e. investors in China) are more pessimistic because returns on Chinese stocks are affected more frequently by negative rumors (economic or political). The difference in attitude can have a substantial impact on the stock market. It is usual to observe the negative innovations (bad news) inciting bigger response in the literature. It is interesting to note the opposite results reported for the Hong Kong market. One possible explanation is when good news are released by Chinese companies, they tend to be highly inflated, as a result, Hong Kong investors are somewhat skeptical of these good news from the Chinese market. Therefore, we observe a greater sensitivity of the Hong Kong market to the good news from the Chinese market.

The estimated values of the scale parameter 6 for all index returns are 1.0841,

1.0282, 0.9093 and 0.9803 and they are significant at the 1% level. Because the estimated parameter,6is B2, the distribution ofotwill have a thicker tail than the

normal distribution. These results suggest that the distributions of the all index returns are significantly thicker-tailed than the normal distribution. Alternatively, we can interpret that these distributions are beyond the range permitted by the normal distribution. Therefore, the empirical results support the use of GED assumption in this study.

4.2. Spillo6er effect analysis

Table 6 presents the results of return and volatility spillover of the four markets using the multivariate EGARCH-M analysis. Past returns in the HSCEI and HSCCI have a positive impact on their own current and future returns. The past red chips (HSCCI) return has a positive impact on the current SZI return while the past SZI return has a negative impact on the current SHI return. The past SHI return, on the other hand, has a positive impact on the current H shares (HSCEI) return. These results are indicative of significant mean spillovers from the red-chip market to the Shenzhen stock market, then from the Shenzhen stock market to the Shanghai stock market, and finally from the Shanghai stock market to the H share market. The results imply that the red chips impact directly or indirectly on all other China-backed markets.

Results of the conditional variance equations depict the presence of significant conditional heteroscedasticity in the raw data series of all returns. That is, the

(26)

W.P.H.Poon,H.-G.Fung/J.of Multi.Fin.Manag.10 (2000) 315 – 343 340

coefficients for one-lag conditional variance (d1,d1 †_,

d1 ,d

1) and own past volatility

shock (g1,g2 †_,

4 ) are statistically significant, and the conditional volatility of both

index returns are predictable using past information. The only exception is the coefficient of the one-lag conditional variance for the SHI return, which is statisti-cally insignificant.

Past volatility shock in the red chips (HSCCI) return has a negative impact on current volatility in the SHI return and has a positive influence on current volatility in the HSCEI return. The past volatility shocks in the HSCEI and SZI have a positive impact on current volatility in the SHI return. Moreover, past volatility shock in the HSCEI return has a negative influence on current volatility in the SZI return. The negative spillover is likely due to a possible overreaction in one market followed by an underreaction in another market (De Bondt and Thaler, 1985). In addition, if the Chinese stock markets are partially segmented (Poon et al., 1998), information may not spread to other markets rapidly. That is, a big change in volatility in one market may result a small change in volatility in another market. Our findings indicate there is volatility spillover from the red chip market to the H share market and the Shanghai stock market; then from the H share market to the Shenzhen stock market and Shanghai stock market; and finally from the Shenzhen stock market to the Shanghai stock market. These results also suggest that (1) Shanghai stock market is the only market that responds to the lagged information of other stock markets, and (2) red chips are initiating information for all the other China-backed securities.

5. Conclusions

Using an EGARCH-in-mean model along with the generalized error distribution, this paper has explored the stochastic return and volatility behavior of the four Chinese stock indices: HSCEI, HSCCI, SHI, and SZI. The paper has analyzed the return and volatility spillover effects among the four markets and investigates the extent to which volatility affects future movements in these markets.

All day-of-the-week dummy variables are negatively significant in all volatility equations. There is no linkage between the conditional mean and volatility in all index returns. Both current and future conditional returns and volatility in all indices can be predicted by past information with the exception of the return on the Shenzhen Composite Index. The unexpected returns of all indices have asymmetric impacts on volatility. The volatility of stocks which are listed in the Hong Kong stock market are more sensitive to ‘good’ news than ‘bad’ news, while those listed in the China stock market are more sensitive to ‘bad’ news than ‘good’ news.

Our empirical findings show that there are significant return spillover effects from the HSCCI to the SZI, then from the SZI to the SHI, and from the SHI to the HSCEI. The results imply that there are significant return transmissions from the red-chip market to the Shenzhen equity market, then from the Shenzhen equity market to the Shanghai equity market, and finally from the Shanghai equity market to the H-share market. We also find volatility spillovers from the red-chip market

(1)

W . P . H . Poon , H .-G . Fung / J . of Multi . Fin . Manag . 10 (2000) 315 – 343

Table 6 (Continued)

0.0023 l3

−0.0005

l3 0.0006 l3† 0.0010

l4 l4

−0.0021 l4† −0.0001 l4 −0.0011

−0.0016

$ _0.0044***

0.0016

l5 −0.0011 l5

0.0008

l5†

0.0046

0.1764*** f1

$ _0.0079

f1† 0.0130 f1

$ _0.0496*

f2 0.0520 f2† 0.1966*** f2 0.0388

$ ₋_0.0204

0.0057

f3†

0.0553* 0.0183 f3

−0.1050**

−0.0263 f4

$ ₋_0.0208

f4† −0.0063 f4

a _0.1182 _a$ ₋_0.1044

0.2349

a a† ₋_0.0604

Variance equation:

$ ₋_0.9509*

−0.8196**

c1 −1.7460*** c1

−0.8918***

c1†

−1.6898***

−1.8454*** c2

$ ₋_1.6611***

c2† −1.3690*** c2

c3 −2.4750*** c3† −1.4142*** c3 −1.1938**

−0.8055*

$ ₋_1.0905**

−1.3035***

−1.9475*** c4†

c4 c4

−1.2018***

−1.1820***

−2.3833*** c5

$ ₋_1.5268***

c5† −1.4909*** c5

d 0.8401*** d$ _0.8239***

d 0.7412*** d† _0.8448***

0.2938***

0.2476** g1

$ ₋_0.1561*

g1† −0.1028 g1

$ _0.0881

−0.2487***

g2 0.2946*** g2

0.5268***

g2†

0.1740

0.2089 g3

$ ₋_0.9105

g3† 0.0832 g3

0.2273** g4

1.3580*

g4†

g4 −0.1489 0.0238

−0.1035

−0.4176

0.1002

u 0.0749 u†

0.9307

1.1041*** 6† 1.0937*** 6 6$ 0.9958***

LB(Q)(10)

LB(Q)(10) 7.7220 LB(Q)(10) 13.3038 LB(Q)(10) 6.4682 7.8581

LB (Q2_{) (10)} _3.3651

12.9814 _{LB (}_Q2_{) (10)} 8.9716 LB (Q2_{) (10)} 2.2999

LB (Q2_{) (10)}

* Denotes significance at the 10% levels. ** Denotes significance at the 5% levels. *** Denote significance at the 1% levels.

(2)

ters (u,u†_, u1, u

1) are different in signs for the different index returns, implying

given (g1,g1†,g1 andg

1) parameters are positive and significant, the positive values

foru,u† _{imply that positive shocks have a larger impact on future volatility of the}

HSCEI and HSCCI returns than negative shocks and the contrary result applies to

the SHI and SZI returns, u , u

The estimated values of the scale parameter 6 for all index returns are 1.0841,

1.0282, 0.9093 and 0.9803 and they are significant at the 1% level. Because the

estimated parameter,6is B2, the distribution ofo_twill have a thicker tail than the

4.2. Spillo6er effect analysis

Results of the conditional variance equations depict the presence of significant conditional heteroscedasticity in the raw data series of all returns. That is, the

(3)

coefficients for one-lag conditional variance (d1,d1 †_,

d1 ,d

1) and own past volatility

shock (g1,g2

†_,

4 ) are statistically significant, and the conditional volatility of both

index returns are predictable using past information. The only exception is the coefficient of the one-lag conditional variance for the SHI return, which is statisti-cally insignificant.

5. Conclusions

(4)

to the Shanghai equity market and the H-share market; then from the H-share market to the Shanghai equity and the Shenzhen equity market; and finally from the Shenzhen equity market to the Shanghai equity market. The study demonstrates that red chips play a leading role in the flow of information among China-backed securities. This may be due to the fact that: (1) red chips are better managed and their management is more transparent as a result of their management style; and (2) investors are more conscious of economic content of news as Hong Kong market is subject to less manipulation vis-a`-vis the China stock market.

Acknowledgements

Poon is grateful for a research grant from the Research Committee of Lingnan University, Hong Kong. The authors wish to thank Professor Marie Sushka, Professor Myron Slovin, Professor Yiuman Tse, Professor Michael Firth, Professor Oliver Rui and an anonymous referee for their constructive comments and criti-cisms, which have greatly improved this paper. The authors acknowledge the editorial assistance provided by Peter Jackson of the Language Centre at Lingnan University, Hong Kong and thank Wong Hong for her research assistance. Any errors that remain are ours.

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