Time-varying conditional dependence in Chinese stock markets

Abstract

This article explores the dynamics of the dependence between 'A' and 'B' share indices on the Shanghai and Shenzhen securities exchanges. While the marginal behaviour of each stock index is modelled by an asymmetric Student-t distribution, the nature of the dependence is captured through a copula representation. Our results confirm the already documented time-varying pattern of the dependence structure. Moreover, we show that regional and world shocks as represented by the Hang Seng Asia and the S&P 500 indices affect the marginal distributions of Chinese 'A' and 'B' stock indices, but do not influence the dynamics of their dependence.

Linkages between Shanghai and Hong Kong stock indices

“…λ and η are the degrees of freedom and skewness parameters. The estimated value of these two parameters for Shanghai are very close to those found in Ane et al (2008). The difference between Shanghai and Hong Kong is that in the latter case returns do not have volatility effect in the mean equation while in Shanghai investors would expect higher returns the higher the volatility.…”

“…Given that the persistence of volatility is a stylised fact in financial series (Taylor, 1986), and especially in emerging markets as shown by Ane et al (2008), we specify an AR(1)-FIGARCH-m as the mean equation…”

“…7 The only study we are aware of that employs copulas in the analysis of the Chinese stock markets is Ane et al (2008). Ane et al (2008) studied the relationship between the Chinese markets of Shanghai and Shenzhen exchanges using the Cook-Johnson (Clayton) copula over the period from January 1996 to December 2003.…”

“…Ane et al (2008) studied the relationship between the Chinese markets of Shanghai and Shenzhen exchanges using the Cook-Johnson (Clayton) copula over the period from January 1996 to December 2003. They found persistent features of dependence between Shanghai and Shenzhen stock returns.…”

Copula Models for Sociology: Measures of Dependence and Probabilities for Joint Distributions

“…For the Frank and the Normal, both l L ¼ 0 and l U ¼ 0, demonstrating that while each can model dependency in cases of concordance (or discordance) near the tails, the copula is not well suited for the relationship between rare joint values far out in the tails. Those in finance have used tail dependency to understand phenomena such as interest rate and stock market shocks (Ane, Ureche-Rangau, and Labidi-Makni 2008;Junker et al 2006) or costs of insurance claims (Frees and Valdez 2008). With methods to model tail dependency, sociologists may find a useful avenue for modeling rare events or values and heavy-tailed distributions.…”