New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model

Mei Xiao; Zaiping Tao; Zhouyi Gu; Zhengxin Li; Xihui Chen

doi:10.1016/j.heliyon.2023.e14017

New practice for investors in Chinese stock market: From perspective of fractionally integrated realized GARCH model

Heliyon. 2023 Mar 3;9(3):e14017. doi: 10.1016/j.heliyon.2023.e14017. eCollection 2023 Mar.

Authors

Mei Xiao¹, Zaiping Tao², Zhouyi Gu², Zhengxin Li³, Xihui Chen⁴

Affiliations

¹ Library of Zhejiang Financial College, Hangzhou 310018, China.
² School of Information Technology,Zhejiang Financial College, Hangzhou 310018, China.
³ School of Management, Zhejiang Shuren University,Hangzhou 310015, China.
⁴ School of Management, Zhejiang University of Technology,Hangzhou 310023, China.

Abstract

In this paper, based on the Realized GARCH model, the fractional integration Realized GARCH model is proposed by combining long memory parameters with conditional variance and replacing the original realized measure with the realized measure obtained after daily, weekly and monthly weighting. Based on the 5-min high-frequency data of the SSE index, the fractional integration Realized GARCH model, Realized HAR GARCH model and Realized GARCH model are investigated for their fitting effect and predictive ability on market volatility, and Monte Carlo simulations are conducted from the error terms obeying normal distribution, t-distribution and chi-square distribution so as to compare the RMSE and MAE of the three types of models with respect to conditional variance. The empirical results show that the fractionally integrated Realized GARCH model is found to better capture the long-run correlation in volatility in certain intervals by comparing the theoretical and sample auto-correlation functions, while the overall predictive power of the model is better than the other two models. Finally, it provides technical support and suggestions for investors' risk control.

Keywords: Fractional integration realized GARCH; High frequency data; Long memory; Realized GARCH; Realized HAR GARCH.