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

Dexiang Mei, Jing Liu, Feng Ma and Wang Chen

Physica A: Statistical Mechanics and its Applications, 2017, vol. 481, issue C, 153-159

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.

Keywords: Volatility forecasts; Realized skewness and kurtosis; Realized volatility; HAR-RV; MF-DFA (search for similar items in EconPapers)
JEL-codes: C53 E27 E37 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (51)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:481:y:2017:i:c:p:153-159

DOI: 10.1016/j.physa.2017.04.020

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