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This paper investigates the high‐frequency volatility modeling and prediction for crude oil futures in China, a new asset class emerging in recent years. Two volatility measures, the realized variance (RV) and realized bi‐power variations (RBV) are constructed at various frequencies by virtue of 1‐minute crude oil futures prices. The distinctive components of these volatility estimators are further identified to exploit the information contents in the in‐sample explanatory power of the realized variance dynamics and the out‐of‐sample prediction of realized variance across different horizons, leading to four new HAR‐RV‐type models. First, the empirical results show that the continuous component of the weekly realized variance, representing investors' trading behavior in the medium‐term, is the dominant factor driving up volatility trends in China's crude oil futures market over a range of market conditions. Second, the monthly jump component in realized variance presents the significant in‐sample explanatory power, and yet marginally improves prediction performance in realized variance during the two out‐of‐sample periods. Finally, these results are robust toward various market/model setups, over day‐ and night‐trading hours, and across a range of prediction horizons and relative to prediction benchmarks. [ABSTRACT FROM AUTHOR]

In this article, we implement the Variance Ratio test to study on the intraday serial correlation of Shenzhen component index return series. The empirical research indicates significant positive serial correlation in China's stock market. For a better understanding of the dynamics of intraday serial correlation in China's stock market, we first investigate the presence of autocorrelation mode of serial correlation. Then, we construct the VR-HAR-RV and VR-HAR-RV-CJ models to explore the links between serial correlation and volatility. We find both the VR-HAR-RV and VR-HAR-RV-CJ models can partially explain the links between serial correlation and volatility, but the VR-HAR-RV-CJ model is more powerful than the VR-HAR-RV model. [ABSTRACT FROM AUTHOR]