考虑跳跃和隔夜波动的中国股票市场波动率建模与预测

中国管理科学 ›› 2014, Vol. 22 ›› Issue (6): 114-124.

考虑跳跃和隔夜波动的中国股票市场波动率建模与预测

孙洁

上海财经大学经济学院, 上海 200433

收稿日期:2013-03-29 修回日期:2013-09-26 出版日期:2014-06-20 发布日期:2014-06-26
作者简介:孙洁（1985- ），女（汉族），山东人，上海财经大学经济学院，博士研究生，研究方向：金融计量.

Modeling and Forecasting the Volatility of China Stock Market Considering the Impact of Jump and Overnight Variance

SUN Jie

School of Economics, Shanghai University of Finance and Economics, Shanghai 200433, China

Received:2013-03-29 Revised:2013-09-26 Online:2014-06-20 Published:2014-06-26

摘要/Abstract

摘要： 本文用已实现波动率（Realized Volatility， RV）度量上证综指和深证成指在交易时间内的波动率，并将其分解为连续路径变差部分和由跳跃引起的非连续部分。这两部分与隔夜波动率共同构成日波动率。本文对日波动率的三个组成部分建立HAR-CJN模型，探究了波动率不同成分之间的相互影响以及在预测中的作用。结果表明连续变差对日波动率的各组成部分均有显著的正向影响，在预测中的贡献最大；而跳变差的影响一般比连续变差的要弱，且随着滞后期的长短而有所不同。样本外预测结果显示HAR-CJN模型的预测表现要远远优于GARCH族模型，并在向前一天和一月的预测中优于普通的HAR-RV模型。

关键词: 已实现波动率, 跳跃, 隔夜波动率, 预测

Abstract: Daily volatility of Shanghai Stock Exchange Composite Index and Shenzhen Stock Exchange Component Index are decomposed into three components, which are the continuous sample-path variation, the discontinuous variation due to jumps and the overnight variance. Then HAR-CJN model is proposed to study the interaction of the three components and their impact on forecasting. The results show that the continuous variation has positive impact on each of the three components and contributes the most in forecasting, while the impact from jump variation is generally weaker than that from continuous variation and varies in direction and size as the length of lag-period changes. The out-of-sample forecast results show that HAR-CJN model outperforms traditional GARCH model considerably, and also outperforms the popular realized volatility model HAR-RV in the one-day-ahead and one-month ahead forecast.

Key words: realized volatility, jump, overnight variance, forecast

中图分类号:

F830

孙洁. 考虑跳跃和隔夜波动的中国股票市场波动率建模与预测[J]. 中国管理科学, 2014, 22(6): 114-124.

SUN Jie. Modeling and Forecasting the Volatility of China Stock Market Considering the Impact of Jump and Overnight Variance[J]. Chinese Journal of Management Science, 2014, 22(6): 114-124.

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