دانلود مقاله ISI انگلیسی شماره 12627
عنوان فارسی مقاله

اثر سیاست باز در وابستگی بین بازار سهام چینی 'A' و دیگر بازارهای سهام: دیدگاه بخش صنعت

کد مقاله سال انتشار مقاله انگلیسی ترجمه فارسی تعداد کلمات
12627 2011 26 صفحه PDF سفارش دهید محاسبه نشده
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عنوان انگلیسی
Effects of the open policy on the dependence between the Chinese ‘A’ stock market and other equity markets: An industry sector perspective
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : Journal of International Financial Markets, Institutions and Money, Volume 21, Issue 1, February 2011, Pages 49–74

کلمات کلیدی
وابستگی - عضو رابط - چی پلات - کندال پلات - برآورد نیمه پارامتریک -
پیش نمایش مقاله
پیش نمایش مقاله اثر سیاست باز در وابستگی بین بازار سهام چینی 'A'  و دیگر بازارهای سهام: دیدگاه بخش صنعت

چکیده انگلیسی

This paper investigates the effect of the open policy introduced in 2002 to allow foreigners to invest in the Chinese ‘A’ share market on the Chinese domestic capital market, especially on the dependence between the financial index returns of the ‘A’ shares and those of some emerging markets, specifically Hong Kong, Singapore, Thailand, Korea and Taiwan as well as the developed markets US, Japan and Australia. The results of nonparametric plots and copula model estimates of these dependence structures provide evidence of weak dependence in these markets before the introduction of the open policy, except for the US and Japan, and the tail dependence is found to be insignificant for all country pairs. These dependence structures are adequately captured by Clayton and normal copula models. On the other hand, in the period 2002–2009, there is significant dependence in all but the Korean market, as indicated by Symmetric Joe-Clayton, Clayton and rotated Gumbel copula models. Further, the significant lower tail dependence of the ‘A’ shares with other markets was found, except for the US, Japan and Korea, which indicates that the financial sectors returns in these five pair markets move downwards together. These findings have implications for international portfolio diversification and financial market participants.

مقدمه انگلیسی

Due to the reforms of the economic system and the open policies introduced in the last two decades, China is experiencing a speedy growth in stock markets and trading activities. The market capitalization of the Shanghai and Shenzhen stock markets has expanded tremendously to 34,000 billion RMB in 2007, while the value in 2005 was only 3000 billion RMB. China's stock market has unique features in the segmentation of the ‘A’ and ‘B’ share markets in the way of being open to the outside world: until December 2002, the ‘A’ share market was restricted to domestic investors only, while the ‘B’ share market was traded only by foreign investors until February 2001. The apparent segmentation in ‘A’ and ‘B’ shares markets is established to protect domestic financial stability. However, since 2002, some important stock market liberalization polices are implemented in mainland China according to the commitment of being a member in the WTO. Foreign investors have been allowed to invest in the ‘A’ shares, which was exclusively available for domestic investors previously. There are two main policies to allow foreign investors to invest in the ‘A’ shares. The first policy allows foreign investors to buy ‘non-traded’ shares from domestic companies in the financial industry such as securities companies and fund management companies. There are several regulations announced in 2002. The regulation on setting-up of foreign-shared securities companies and the regulation on establishment of foreign-shared fund management companies were carried out on July 1, 2002. In addition, foreign investors could combine domestic listed companies legally as well after 2002. The policy on acquisition of listed companies was announced in September 2002 and carried out on December 1, 2002. The second one is the QFII (Qualified Foreign Institution Investment) scheme. It allows QFII to invest in local currency and use the specific accounts investing in the ‘A’ share markets. The return on the investments, including dividend, capital gain from investments, can be legally exchanged into foreign currency and be repatriated. This scheme was announced on November 7, 2002 and made effective on December 1, 2002. This policy attracts quality foreign institutions to participate into domestic securities markets and introduce concepts of sensible investment which improves the stability of capital markets. Impacted both by the open market policies and globalization of economy, Chinese capital markets are expected to strengthen their association with other capital markets in the world. The primary objective of this paper is to investigate the dependence between the Chinese ‘A’ share market and some selected developed and emerging equity markets before and after the open stock markets policy was implemented. Some existing empirical work on China's financial integration has focused on the whole Chinese mainland stock market, which has used the composite index of Shanghai and Shenzhen combining ‘A’ with ‘B’ shares, while some other studies separate the two classes of shares and mainly focus on the ‘A’ shares. Some of these studies are briefly discussed below. In this paper, we will analyse daily returns of financial sectors in the ‘A’ share market to examine the effect of the open ‘A’ share market policies on the dependence of the ‘A’ shares and some emerging and developed markets. Since the Asian financial crisis an extensive body of literature has explored the interrelationships between financial markets, and as such as we focus on the Chinese financial sectors whose development level and linkage with other markets is strongly related to the development of regional financial markets. Thus, we explore China's financial openness policies in the context of the integration of regional financial markets and the dependence of their financial sectors. Then we employ copula models to capture these dependence structures. It is an essential part of financial research in taking co-movement or dependence of financial markets into account since it is the foundation of portfolio selection. Further, it is helpful for risk management. This is important for two reasons: one is to see if there is an increase in dependence when both market returns are large and negative, diversification fails when needed most. Further, the left tail dependence of stock markets indicates that when one market collapses, the other one happens in unison. Hatemi-J and Roca (2004) study the interdependence between China, Hong Kong, Singapore and Taiwan over the period January 1993 to September 2001 using the causality test based on Toda and Yamamoto (1995). They utilise the causality test with leveraged adjustments using the bootstrap to get more reliable results, and find that Hong Kong has no influence on other markets in the periods neither before nor after the Asian crisis. They also find a causal influence of the US market on the Chinese market after the Asian crisis. Girardin and Liu (2007) examine the integration of the ‘A’ shares with the S&P 500 and Hang Seng index over the period 1992–2005. This study sheds new light on the cointegration test with a major focus on the role of temporal aggregation, by applying a regime-switching Markov process into the cointegration equation to obtain a regime-dependent coefficient. With the end-of-week closing prices, the study indicates segmentation of the ‘A’ share market with other markets. However, with the weekly-averaged indices, China's market was cointegrated with the US up to late 1996, followed by a gradual rise in the relationship with the Hong Kong market, see also Cheng and Glascock (2006) and Tian (2008). In the previous literature, the Granger causality test has been used widely to determine the lead–lag relationships between the Chinese stock market and other equity markets, while cointegration tests have been applied to measure the long-run financial integration of markets. Based on the CAPM model, Wang and Di Iorio, 2007a and Wang and Di Iorio, 2007b investigates the integration of the ‘A’ shares, ‘B’ shares and ‘H’ shares with both the Hong Kong market and world market using monthly data in a long period from 1995 to 2004. They are also unable to document the integration of the ‘A’ shares with the world stock markets in the full sample period, while the sub-period test indicates a move from segmentation to integration between the ‘A’ shares and the Hong Kong market. While most research has focused on the level of returns, the volatility behaviour of the ‘A’ shares has not been taken into account in terms of the dependence structure extensively. Lin et al. (2009) study the variance, covariance and correlations for the mainland Chinese stock market and its relationship to world markets, including the five largest Asian markets, the US market and the three main European markets. The Dynamic Conditional Correlation (DCC) model with asymmetric correlation structure is applied in the study. Consistent with other studies, this study has not found any evidence of the integration of Chinese ‘A’ share market and other financial markets. Most of the existing studies on the association between different markets mainly use the linear regression model with the corresponding coefficients to capture lead–lag or long-run relationship (Hatemi-J and Roca, 2004 and Cheng and Glascock, 2006) or use the linear correlation coefficient (Lin et al., 2009 and Cheng and Glascock, 2006). However, although the linear correlation is easy to calculate, it turns out that the linear correlation is not a good measure of dependence for financial data, most of which are not jointly elliptically distributed. A discussion of the shortcomings of the linear correlation is provided in Embrechts et al. (2000). Since the linear correlation only can capture the symmetric linear dependence in data, it has no properties of invariance under nonlinear strictly increasing transformation. In addition, as long as the second moment of the variables is infinite, the linear correlation cannot be defined. Another flexible way to capture a more complete view of dependence is to consider the multivariate distribution of variables. If we know the joint distribution of variates, we could fully describe the inter-relationships of different markets. However, the commonly used multivariate distribution functions, such as multivariate normal function and multivariate Student's t function, can capture only limited shapes of joint distributions and the dependence structures. Sklar (1959) proved that the multivariate distribution could be fully and uniquely characterized by its marginal distributions and a copula function, which is able to summarize the nature of the dependence, for example, between several financial series. The tool we use to model various dependent structures in this paper is the copula function, which overcomes the traditional correlation approaches by having no restrictions on a normal marginal distribution hypothesis. There is strong empirical support that fat tail, excess kurtosis and non-normality are the key features of stock returns distributions. The copula function has been used in financial applications in a number of recent studies due to its flexible construction of multivariate distributions exhibiting a rich pattern of tail behaviour and different types of asymmetric dependence. The copula function has become a popular alternative to linear correlation in financial risk management when the extreme events need to be captured, such as market risk measurement, more specifically as value at risk, and insurance risk measurement. Embrechts et al. (2003) provide a detailed survey of these applications in finance. In this paper, we re-examine the issues of interdependence between the Chinese ‘A’ shares market and other emerging equity markets (Hong Kong, Singapore, Korea, Thailand and Taiwan) and three developed securities markets (US, AUS and Japan). Moreover, this paper differs from previous studies in three aspects. First, our study differs from others in terms of the use of statistical methodology. We use the copula model to measure the inter-relationships of financial markets, following the Chi-plot and Kendall-plot and judging the existence of significance dependence. These two plots are directly related to copula models. See, for example, Fisher and Switzer, 1985 and Fisher and Switzer, 2001 and Genest and Boies (2003) for details. We use these plots in order to assess the significance of dependence between two returns and to choose an appropriate copula model, see, for example, Silvapulle and Zhang (2007) and Silvapulle et al. (2008). A semiparametric procedure, which is found to be robust for mis-specified marginal distributions (Kim et al., 2007) is employed to estimate the copula parameters and hence the tail dependence parameters. Secondly, our study concentrates on the effect of China's open stock market policy on the dependence structure of the Chinese mainland equity market with the foreign markets. As we know, the Chinese stock market seems to be isolated from the outside world even in adverse financial situations, for example, the Asian financial crisis in 1997. The policy of opening ‘A’ shares market to foreign investors is expected to have an impact on domestic markets structure and interdependence. The previous studies on dependence mainly regard the 1997 Asian financial crisis as a cut-off point, while our cut-point is the period July 2002 to December 2002 when China introduced the open ‘A’ share market policy. Thirdly, the data we use here are based on financial sectors instead of the whole market. Although China's financial open policy is expected to impact the whole market, we only concentrate on the analysis on the financial sectors in order to acquire more information on the Chinese financial industry and its relationship with other financial sectors in the region. The remainder of the article is organized as follows. Section 2 introduces the methodology of this paper. Section 3 discusses the data, reports and analyses the results, and provides the empirical findings. Finally, Section 4 concludes this paper.

نتیجه گیری انگلیسی

This paper investigates the changing dependence structure in the daily financial index of the Chinese ‘A’ share market and some emerging markets Hong Kong, Singapore, Thailand, Korea and Taiwan as well as the developed markets US, Japan and Australia for the pre- and post-open policy period. Our study sheds light on the research based on an industry sector as well as an evaluation for the open policy of Chinese capital market, which was introduced in 2002. The empirical analysis was carried out for the sample period January 1, 1995 to December 31, 2009. We use the Chi-plot proposed by Fisher and Switzer, 1985 and Fisher and Switzer, 2001 and the Kendall-plot proposed by Genest and Boies (2003) to examine the significance of underlying dependence and assess the tail dependence for both periods. We find that the dependence is significant for the Hong Kong and Singapore markets with China, while the other Asian markets with China A shares exhibits a weak association after the open policy implemented. The semi-parametric method is used to estimate the copula model parameters, and the tail dependence parameters. The semi-parametric method is found to be robust against misspecification of marginal distributions (Kim et al., 2007). We use model selection criteria (AIC and BIC) and the conditional copula function to identify suitable copulas for the daily returns data. As indicated by Chi- and Kendall-plots and the criterion such as AIC/BIC, the Symmetric Joe-Clayton Copula is found to be suitable for HK, Singapore and Korean markets; the rotated Gumbel copula is appropriate for Thai and Australia, and the Clayton copula fits well for Taiwan and Japan. The dependence in the some pair markets has significant asymmetry in the tail dependence, which means the higher left tail dependence than the right tail dependence. The objective of our study is to find evidence of whether or not the introduction of China's open policy in 2002 has affected the dependence of Chinese financial sector on some developed and emerging markets. In order to establish evidence, we estimated the copula models for the two sub-sample periods without making the models complex by introducing dynamics in the dependence. However, an interesting issue for future research is to explore the dynamic structure of market dependence, because the relationship of financial return series may evolve over time. Patton (2006) introduced the concept of the conditional copula and explored the asymmetric dependence of the exchange rates by the dynamic copula function with time-varying parameters. Rodriguez (2007) and Okimoto (2008) find the evidence of changing tail dependence and asymmetries in international equity markets by a mixture of the copulas with a Markov switching model. For the first period Jan 1995 to June 2002, a weak dependence structure between the Chinese financial sector and other countries was found, except for the US and Japan, according to the nonparametric plots as well as the copula model estimates. However, there is no tail dependence in these markets. These findings are not consistent with previous empirical study which concludes that the ‘A’ share market was segmented from outside world. The robust methods used in this study revealed the presence of weak dependence. During the period of the open policy in effect, the financial index of Chinese ‘A’ shares was associated intensively with Hong Kong, Singapore and Thailand, followed by the weak dependence in the pairs with Australia, Taiwan and Japan, while still remaining largely isolated from Korea. Since the post-open policy sample covers the period of the recent global financial crisis, the findings of closer co-movement between ‘A’ shares and other Asian markets has confirmed the effect of open policies in another aspect. Further, for all the pairs, except Korea, Japan and the US, the lower tail dependence is found to be significant. These results show that these financial indexes often experience downward movements together. The results have implications for international portfolio diversification.

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