CHINA FINANCIAL RESEARCH NETWORK
2012-04-24 第4卷 第9期
James Madison UniversityDepartment of Finance and Business Law, College of Business
Tsinghua UniversitySchool of Economics and Management
National University of SingaporeNUS Business School
David E. Allen
Edith Cowan UniversitySchool of Accounting,Finance and Economics
Hui He James Madison UniversityDepartment of Finance and Business Law, College of Business
Are the returns of Chinese American Depositary Receipts (ADR) more affected by the U.S. stock market or their underlying home market? Since there is non-synchronous trading between U.S. and the Chinese stock markets, we decompose the Chinese ADR daily returns into day and night returns to investigate the different market factors in Chinese ADR pricing. This paper also attempts to separate "homeless" ADRs from home-based ADRs to see if they are affected differently by market factors. We include a sample of 76 Chinese ADRs with the daily data from January 2000 to July 2010. Through regression and Vector Autoregressive analyses, we find that the U.S. market dominates the day returns of Chinese ADRs. We also find the Hong Kong market factor dominates the ADR night returns over the mainland China market for the whole sample. These results are particularly strong for “homeless” ADRs.
Chun Liu Tsinghua UniversitySchool of Economics and Management
We propose a new joint model of intraday returns and durations to study the dynamics of several Chinese stocks. We include IBM from the U.S. market for comparison purposes. Flexible innovation distributions are used for durations and returns, and the total variance of returns is decomposed into different volatility components associated with different transaction horizons. Our new model strongly dominates existing specifications in the literature. The conditional hazard functions are non-monotonic and there is strong evidence for different volatility components. Although diurnal patterns, volatility components, and market microstructure implications are similar across the markets, there are interesting differences. Durations for lightly traded Chinese stocks tend to carry more information than heavily traded stocks. Chinese investors usually have longer investment horizons, which may be explained by the specific trading rules in China.
Swee-Sum Lam National University of SingaporeNUS Business School
Our study is the first to examine the effect of policy instability on interest rates. China offers a natural setting for the experiment because financial market liberalization policy flip-flops recur. When a policy is reversed, interest rate level and spread can increase or decrease in the interbank repo market. Accounting for the bureaucratic quality of policymaking, we find that the nonpredictable, non-credible and non-timely reversal of an existing policy is related to higher interest rate spread and volatility, which represent higher risk premia in interest rates. Conversely, predictable, credible and timely reversal is related to lower interest rate spread and volatility. Our results suggest that bureaucratic quality is a moderating factor and high bureaucratic quality can reduce the risk premia of policy instability being priced in interest rates.
David E. Allen Edith Cowan UniversitySchool of Accounting,Finance and Economics
This paper examines whether there is evidence of spillovers of volatility from the Chinese stock market to its neighbours and trading partners, including Australia, Hong Kong, Singapore, Japan and USA. China's increasing integration into the global market may have important consequences for investors in related markets. In order to capture these potential eects, we explore these issues using an Autoregressive Moving Average (ARMA) return equation. A univariate GARCH model is then adopted to test for the persistence of volatility in stock market returns, as represented by stock market indices. Finally, univariate GARCH, multivariate VARMA-GARCH, and multivariate VARMA-AGARCH models are used to test for constant conditional correlations and volatility spillover eects across these markets. Each model is used to calculate the conditional volatility between both the Shenzhen and Shanghai Chinese markets and several other markets around the Pacic Basin Area, including Australia, Hong Kong, Japan, Taiwan and Singapore, during four distinct periods, beginning 27 August 1991 and ending 17 November 2010. The empirical results show some evidence of volatility spillovers across these markets in the pre-GFC periods, but there is little evidence of spillover eects from China to related markets during the GFC. This is presumably because the GFC was initially a US phenomenon, before spreading to developed markets around the globe, so that it was not a Chinese phenomenon.
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