中国股市波动率预测研究:基于实时已实现EGARCH-MIDAS模型
Forecasting Chinese Stock Market Volatility:A Real-Time Realized EGARCH-MIDAS Model
吴鑫育 1赵安 1谢海滨 2马超群3
作者信息
- 1. 安徽财经大学金融学院,蚌埠 233030
- 2. 对外经济贸易大学金融学院,北京 100029
- 3. 湖南大学工商管理学院,长沙 410082
- 折叠
摘要
本文构建了一个能够充分捕获高频数据信息、当前收益率信息以及波动率长记忆性的实时已实现 EGARCH-MIDAS(RT-REGARCH-MIDAS)模型对中国股市波动率进行建模和预测.采用上证综合指数(SSEC)和深证成份指数(SZSEC)5 分钟高频数据进行实证研究,结果表明:RT-REGARCH-MIDAS 模型相比其它众多竞争模型具有更好的收益率数据拟合效果,能够更好地描述股市波动性.利用稳健的损失函数以及模型置信集(MCS)检验作为判断准则,实证比较了该模型与其它竞争模型对中国股市波动率的样本外预测能力.实证结果表明:捕获高频数据信息、当前收益率信息和波动率长记忆性对于股市波动率预测具有重要作用;在众多竞争模型中,RT-REGARCH-MIDAS模型具有最为优越的波动率预测能力.进一步,采用不同的已实现测度、不同的预测窗口、不同的 MIDAS滞后阶数、不同的预测期以及样本外R2 检验进行稳健性检验,证实了该模型优越的波动率预测能力具有稳健性.最后,通过考察模型波动择时策略发现,该模型能够获得相比其它模型显著更高的投资组合经济价值.
Abstract
This paper proposes the real-time Realized EGARCH-MIDAS(RT-REGARCH-MIDAS)model which adequately captures the information content of high-frequency data,the current return information and the long memory of volatility to model and forecast Chinese stock market volatility.An empirical analysis based on the 5-minute high-frequency data of the Shanghai Stock Exchange Composite Index(SSEC)and the Shenzhen Stock Exchange Component Index(SZSEC)shows that the RT-REGARCH-MIDAS model outperforms a variety of competitor models in fitting the return data and can describe the stock market volatility better.Using robust loss functions and the model confidence set(MCS)test,the paper compares the out-of-sample forecasting ability of the model and other competitor models for Chinese stock market volatility.Our empirical results show that accounting for the information con-tent of high-frequency data,the current return information and the long memory of volatility plays an important role in forecasting stock market volatility.As a conse-quence,the proposed RT-REGARCH-MIDAS model performs the best in forecasting Chinese stock market volatility.Further,according to the robustness checks,the superior volatility forecasting ability of the model is robust to alternative realized measure,alternative forecast windows,alternative MIDAS lags,alternative forecast-ing horizons and out-of-sample R2 test.Finally,a volatility timing strategy shows that the proposed model yields more significant economic value of portfolio compared to the other models.
关键词
波动率预测/高频数据信息/当前收益率信息/波动率长记忆性/波动择时Key words
volatility forecasting/information content of high-frequency data/cur-rent return information/long memory volatility/volatility timing引用本文复制引用
基金项目
国家自然科学基金(71971001)
安徽省自然科学基金(2208085Y21)
安徽省高校杰出青年科研项目(2022AH020047)
安徽省高校学科(专业)拔尖人才学术资助项目(gxbjZD2022019)
安徽省高校优秀科研创新团队(2022AH010045)
安徽高校协同创新项目(GXXT-2021-078)
出版年
2024
NETL
NSTL
万方数据