基于贝叶斯因子模型金融高频波动率预测研究  

High-frequency volatility forecast of financial futures based on Bayesian factor model

作　　者：罗嘉雯[1] 陈浪南[2] 

机构地区：[1]华南理工大学工商管理学院,广州510006 [2]中山大学岭南学院,广州510275

出　　处：《管理科学学报》2017年第8期13-26,共14页Journal of Management Sciences in China

基　　金：教育部人文社会科学研究青年基金资助项目(17YJC630099);教育部人文社会科学研究规划基金资助项目(17YJA790011);中央高校基本科研业务费;广东省自然科学基金博士科研启动纵向协同管理试点资助项目(2017BQ014);中国博士后基金面上资助项目(2017M612674);广州市金融服务创新和风险管理基地资助项目

摘　　要：构建了包含时变系数和动态方差的贝叶斯HAR潜在因子模型(DMA(DMS)-FAHAR),并对我国金融期货(主要是股指期货和国债期货)的高频已实现波动率进行预测.通过构建贝叶斯动态潜在因子模型提取包含波动率变量、跳跃变量和考虑杠杆效应的符号跳跃变量等预测变量的重要信息.同时,在模型中加入了投机活动变量,以考察市场投机活动对中国金融期货市场波动率预测的影响.预测结果表明,时变贝叶斯潜在因子模型在所有参与比较的预测模型当中具有最优的短期、中期和长期预测效果.同时,具有时变参数和时变预测变量的贝叶斯HAR族模型在很大程度上提高了固定参数HAR族模型的预测能力.在股指期货和国债期货的预测模型中加入投机活动变量可以获得更好的预测效果.The realized volatilities of China＇ s financial futures is forecasted by constructing a Bayesian factor augmented heterogeneous autoregressive model （DMA （DMS）-FAHAR） with time-varying parameters and sto- chastic volatility. The Bayesian inference is employed to obtain the latent factors of the daily, weekly, and monthly predictor sets including the lagged volatility variables, jump variables, and signed jump variables. Speculation variables are used to investigate the impact of speculation activities on the volatility forecast. The results suggest that the Bayesian factor augmented HAR model performs best for short-term, mid-term, and long-term forecasts among all candidate forecast models. Meanwhile, the time-varying Bayesian HAR models have superior forecast performances compared with the fixed parameter HAR models. In addition, better fore- cast performances are achieved after incorporating the speculation variables into the forecast models for both the stock index futures and the Treasury futures.

关 键 词：已实现波动率的预测 HAR模型 金融期货 时变性 潜在因子 

分 类 号：F833-5[经济管理—金融学]