Forecasting the realized volatility of CSI 300

Weijie Zhou, Jiao Pan and Xiaoli Wu

Physica A: Statistical Mechanics and its Applications, 2019, vol. 531, issue C

Abstract: According to the characteristics of realized volatility existing in Shanghai and Shenzhen 300 index (China Securities Index 300, CSI 300), the GARCH family model is introduced to describe the ARCH effect of error sequences in HAR, ARFIMA, and ARFIMAX models. Then, the HAR-GARCH family, the ARFIMA-GARCH family, and the ARFIMAX-GARCH family models set is proposed, which contains 33 kinds of models. Using the quasi maximum likelihood method, the parameters of the all models are estimated with normal (N) and skewed student t (SKST) distributions. By rolling window technology, one-step-ahead rolling prediction of realized volatility for CSI 300 is conducted. The results from prediction accuracy by model confidence set (MCS) test show that the realized volatility prediction models with skewed student t distribution possess higher precision than those of normal distribution in general. The ARFIMAX and other six models in ARFIMAX-GARCH family form the optimal prediction models set, which can forecast the realized volatility of CSI 300 with better accuracy.

Keywords: Realized volatility; Long-term memory; GARCH family model; Normal distribution; Skewed student t distribution; MCS test (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:531:y:2019:i:c:s037843711931057x

DOI: 10.1016/j.physa.2019.121799

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