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Forecasting stock market volatility: Do realized skewness and kurtosis help?

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  • Mei, Dexiang
  • Liu, Jing
  • Ma, Feng
  • Chen, Wang

Abstract

In this study, we investigate the predictability of the realized skewness (RSK) and realized kurtosis (RKU) to stock market volatility, that has not been addressed in the existing studies. Out-of-sample results show that RSK, which can significantly improve forecast accuracy in mid- and long-term, is more powerful than RKU in forecasting volatility. Whereas these variables are useless in short-term forecasting. Furthermore, we employ the realized kernel (RK) for the robustness analysis and the conclusions are consistent with the RV measures. Our results are of great importance for portfolio allocation and financial risk management.

Suggested Citation

  • Mei, Dexiang & Liu, Jing & Ma, Feng & Chen, Wang, 2017. "Forecasting stock market volatility: Do realized skewness and kurtosis help?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 481(C), pages 153-159.
    • Handle: RePEc:eee:phsmap:v:481:y:2017:i:c:p:153-159
    DOI: 10.1016/j.physa.2017.04.020
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    References listed on IDEAS

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    More about this item

    Keywords

    Volatility forecasts; Realized skewness and kurtosis; Realized volatility; HAR-RV; MF-DFA;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • E27 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - Forecasting and Simulation: Models and Applications
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications

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