The Journal of Financial Research · 2001 · 165 citations · 16 references
BusinessEconomicsFinanceEconometricsFinancial EconomicsTime Series AnalysisFinancial Time Series AnalysisLong-memory ProcessesTime Series EconometricsTime Series ForecastingVolatility AnalysisVolatility ModelingAsset VolatilityGeneralized Autoregressive Conditional Heteroskedasticity ModelsInformation ArrivalLong Memory ModelsStock Market ReturnsTime-varying Volatility ModelsVolatilityVolatility Modelling
Abstract We examine time‐series features of stock returns and volatility, as well as the relation between return and volatility in four of China's stock exchanges. Variance ratio tests reject the hypothesis that stock returns follow a random walk. We find evidence of long memory of returns. Application of GARCH and EGARCH models provides strong evidence of time‐varying volatility and shows volatility is highly persistent and predictable. The results of GARCH‐M do not show any relation between expected returns and expected risk. Daily trading volume used as a proxy for information arrival time has no significant explanatory power for the conditional volatility of daily returns. JEL classification: G15
16
Generalized autoregressive conditional heteroskedasticity
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