基于广义已实现测度的中国股市波动预测与 VaR 度量

白娟娟, 师荣蓉

系统科学与数学 ›› 2021, Vol. 41 ›› Issue (3) : 653-666.

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PDF(664 KB)
系统科学与数学 ›› 2021, Vol. 41 ›› Issue (3) : 653-666. DOI: 10.12341/jssms20218

基于广义已实现测度的中国股市波动预测与 VaR 度量

    白娟娟1,师荣蓉2
作者信息 +

The Volatility Forecasting and VaR Measurement of Chinese Stock Market Based on Generalized Realized Measures

    BAI Juanjuan1 ,SHI Rongrong2
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摘要

基于上证综合指数和深证成份指数, 文章将广义已实现测度引入 ARFIMA-Realized GARCH 模型, 同时考虑已实现方差、已实现极差、已实现双幂次变差和已实现极差双幂次变差, 比较不同已实现测度下模型的波动率预测能力和 VaR 度量效果. 实证结果表明:\ ARFIMA-Realized GARCH 模型能够充分捕获波动率的非对称性、长记忆性和持续性等特征; 采用已实现方差的\ ARFIMA-Realized GARCH 模型具有最优的波动率预测能力; 已实现平均绝对离差能够改进模型的拟合效果, 并且引入已实现风险值显著提高了\ ARFIMA-Realized GARCH 模型的\ VaR 预测精度.

Abstract

Based on Shanghai Composite Index and Shenzhen Component Index, the paper introduces generalized realized measures to the ARFIMA-Realized GARCH model and considers realized variance, realized range-based volatility, realized bipower variance and realized range-based bipower variance at the same time, then volatility forecasting ability and VaR measurement effect of models under different realized measures are compared. The results show that the ARFIMA-Realized GARCH model can fully capture the asymmetry,\ long term memory and persistence of volatility. With realized variance, the ARFIMA-Realized GARCH model has the best volatility forecasting ability. The realized mean absolute deviation can improve the fitting effect of the model, and the introduction of realized value-at-risk significantly improves the VaR forecasting accuracy of ARFIMA-Realized GARCH model.

关键词

ARFIMA-Realized GARCH 模型, 已实现测度, 波动率预测, VaR 度量.

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白娟娟, 师荣蓉. 基于广义已实现测度的中国股市波动预测与 VaR 度量. 系统科学与数学, 2021, 41(3): 653-666. https://doi.org/10.12341/jssms20218
BAI Juanjuan, SHI Rongrong. The Volatility Forecasting and VaR Measurement of Chinese Stock Market Based on Generalized Realized Measures. Journal of Systems Science and Mathematical Sciences, 2021, 41(3): 653-666 https://doi.org/10.12341/jssms20218
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