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Effect Of Non-trading Day Stochastic Volatility Models

Posted on:2009-06-21Degree:MasterType:Thesis
Country:ChinaCandidate:W X YanFull Text:PDF
GTID:2199360245978915Subject:Finance
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Chinese stock market is an emerging market. Because of a late start, there has excessive government intervention and serious speculation in the market. These phenomena have shown that Chinese stock markets are still immature, and not sufficiently effective, so the study of Chinese stock markets is of great significance.A lot of empirical research literatures have found that there are many market anomalies in many stock markets. Non-trading effects is a kind of market anomalies. Information accumulates while financial markets are closed, and is subsequently reflected in prices after markets reopen. This is the so-called non-trading day effects. Financial markets are closed generally on holidays, a so non-trading day effect is also known as holiday effect. In this paper, we try to study of the non-trading effects in Chinese stock markets.In this paper, we analyze three models of non-trading day effects in stochastic volatility models with leverage effects, namely: (1) the performance of non-trading effects in the yield equation is same as the performance in the volatility equation; (2) only consider the performance of non-trading effects in the yield equation; (3) the performance of non-trading effects in the yield equation is different from the performance in the volatility equation. The objects of the study in this paper are Shanghai Composite Index and Shenzhen Component Index, the sample period is 16/12/1996 to 31/3/2008, giving 2724 daily observation and 1399 non-trading days, we use Monte Carlo maximum likelihood to estimate the parameters. The estimates imply that a non-trading day has approximately 20% of the effect to the Shanghai Composite Index of a trading day, and approximately 18% of the effect to the Shenzhen Component Index of a trading day.
Keywords/Search Tags:Non-trading day effects, Stochastic volatility, Leverage effects, Monte Carlo maximum likelihood, Importance sampling, Kalman filter
PDF Full Text Request
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