基于杠杆效应和结构突变的HAR族模型及其对股市波动率的预测研究

The HAR-type models with leverage and structural breaks and their applications to the volatility forecasting of stock market

近年来,基于高频交易数据的HAR族模型在对各类金融市场波动率的预测研究中展现出了良好的预测效果.本文在4个经典或前沿的HAR族模型的基础上,考虑杠杆效应和结构突变因素对波动率的预测作用,构建4个带杠杆效应和结构突变的HAR族模型.接着,以上证综指和深证成指的5分钟高频交易数据为研究样本,对上述模型进行样本内和样本外分析,以此检验各成分对股市波动率的预测作用以及比较各模型的预测能力.实证结果显示:已实现波动率,连续波动率,下行波动率,上行波动率,杠杆效应和结构突变成分对股市波动率的预测作用较强,而跳跃波动率,符号跳跃方差对股市波动率的预测作用较弱;带杠杆效应和结构突变的HAR族模型对股市波动率的样本内拟合效果和样本外预测能力都明显优于相对应的不带杠杆和结构突变的HAR族模型,其中大多数情况下LHAR-CJ-SB模型展现出最高的拟合效果和预测精度.以上结果表明,杠杆效应和结构突变因素能有效提高HAR族模型的预测精度,所以在HAR族模型的构建中这两个因素不能被忽视.

Recently,the HAR-type models based on high-frequency transaction data have shown a good forecasting performance for the volatility of financial markets.On the basis of 4 existing HAR-type models,through adding the leverage and structural breaks,we develop 4 new HAR-type models with leverage and structural breaks.Then,we use high-frequency transaction data for five minutes of the Shanghai Composite Index and Shenzhen Component Index as the study sample,which respectively analyzes on all HAR-type models.The results indicate that the realized volatility,continuous volatility,upside volatility,downside volatility,leverage and structural breaks have obvious in-sample prediction power for the volatility in Chinese stock market,while the jump volatility and signed jump variation show weak in-sample predictive ability.In addition,we also find,compared with HAR-type models without leverage and structural breaks,the new HAR-type models with leverage and structural breaks have higher in-sample fitting capacity and out-of-sample predictive power for the volatility.In most cases,the LHAR-CJ-SB model exhibits the best in-sample and out-of-sample performances.Our results suggest that adding the leverage and structural breaks can improve the prediction performance of HAR-type models,so we cannot ignore these two factors when we build new HAR-type models.