GAS-SKST-F模型及其在高频多元波动率预测中的应用

doi:10.16381/j.cnki.issn1003-207x.2019.1105

中国管理科学 ›› 2022, Vol. 30 ›› Issue (1): 77-87.doi: 10.16381/j.cnki.issn1003-207x.2019.1105

GAS-SKST-F模型及其在高频多元波动率预测中的应用

鲁万波, 亢晶浩

西南财经大学统计学院，四川成都611130

收稿日期:2019-07-28 修回日期:2020-03-04 出版日期:2022-01-20 发布日期:2022-01-29
通讯作者: 鲁万波(1977-)，男(汉)，贵州贵阳人，西南财经大学统计学院，教授、博士生导师，研究方向：金融计量、风险管理，Email：luwb@swufe.edu.cn. E-mail:luwb@swufe.edu.cn
基金资助:
国家自然科学基金资助面上项目(71771187)；国家自然科学基金国际合作交流项目(72011530149)；国家自然科学基金地区科学基金项目(72163029)；西南财经大学“光华英才”百人计划；中央高校基本科研业务费专项资金(JBK190602)

GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast

LU Wan-bo, KANG Jing-hao

School of Statistics, Southwestern University of Finance and Economics, Chengdu 611130, China

Received:2019-07-28 Revised:2020-03-04 Online:2022-01-20 Published:2022-01-29
Contact: 鲁万波 E-mail:luwb@swufe.edu.cn

摘要/Abstract

摘要： 假定日收益率服从多元有偏学生t分布、已实现协方差矩阵服从矩阵F分布，本文构建了一种新的得分驱动模型：GAS-SKST-F模型。在该有偏厚尾多元波动率模型中，我们基于广义自回归得分(GAS)模型的基本思想对收益率和已实现协方差矩阵进行联合动态设定，协方差矩阵的更新过程依赖于收益率分布和已实现协方差矩阵分布联合似然函数的得分函数。已实现协方差测度在协方差矩阵的更新过程中发挥了重要的作用。基于20支上证50成分股高频数据的实证分析研究结果显示，与GAS-N-Wishart模型和GAS-tF模型相比，无论样本内还是样本外，GAS-SKST-F模型有着更加良好的样本内估计和样本外预测能力。

关键词: GAS-SKST-F模型, 多元有偏学生t分布, 多元波动率

Abstract: Modeling conditional dependency structure of financial assets through time-varying covariance is of great significance to capital risk management, option pricing and optimal portfolio. With the advent of high-frequency data, realized multivariate volatility model is widely applied in the financial field in recent years. Among the observation-driven models for time-varying parameters, the clear advantage of the generalized autoregressive score (GAS) model is that it exploits the complete density structure and likelihood information rather than theconditional moments information.

Key words: GAS-SKST-F model, multivariate skewed student t density, multivariate volatility

中图分类号:

F830.9

鲁万波, 亢晶浩. GAS-SKST-F模型及其在高频多元波动率预测中的应用[J]. 中国管理科学, 2022, 30(1): 77-87.

LU Wan-bo, KANG Jing-hao. GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast[J]. Chinese Journal of Management Science, 2022, 30(1): 77-87.

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GAS-SKST-F模型及其在高频多元波动率预测中的应用

GAS-SKST-F Model and Its Application in High Frequency Multivariate Volatility Forecast

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