基于动态估计误差的中国股市波动率建模与预测

发布时间：2018-01-28 11:03

本文关键词： 已实现波动率动态估计误差高频数据跳跃行为杠杆效应　出处：《中国管理科学》2017年09期 　论文类型：期刊论文

【摘要】：本文设波动率的估计误差服从异方差假定,在对已实现波动率进行建模时,根据方差变化来设定模型的自回归系数,构建基于高频数据的HARQ(F)模型。在此基础上,考虑中国股市波动的跳跃行为及杠杆效应,先后构建了HARQ(F)-CJ模型和LHARQ(F)-CJ模型,以改善波动率模型的拟合效果和预测能力。本文假设,当期已实现波动率或其连续成分的估计误差的方差越大,它对未来真实波动率的解释力度则越差,因而其对应系数越小。对上证综合指数近15年的五分钟高频数据进行实证研究发现,基于估计误差异方差假定的动态系数能够提高已实现波动率模型的拟合效果和预测能力。其中,对日回归系数进行基于估计误差方差的动态调整是模型改进的关键。同时考虑中国股市波动的跳跃行为及杠杆效应的LHARQ-CJ模型在所有模型中表现最优。
[Abstract]:In this paper, the estimation error of volatility is assumed to be heteroscedasticity, and the autoregressive coefficient of the model is set according to the variance change when the realized volatility is modeled. On the basis of the HARQF model based on high frequency data, the jumping behavior and leverage effect of Chinese stock market volatility are considered. The HARQ(F)-CJ model and the LHARQ(F)-CJ model are constructed successively to improve the fitting effect and prediction ability of the volatility model. The greater the variance of the estimation error of the current realized volatility or its continuous components, the worse the interpretation of the future true volatility. Therefore, the smaller the corresponding coefficient is, the more empirical research on the five minute high frequency data of Shanghai Composite Index in the past 15 years has been carried out. The dynamic coefficient based on the estimation error variance assumption can improve the fitting effect and prediction ability of the realized volatility model. Dynamic adjustment of the daily regression coefficient based on estimated error variance is the key to the improvement of the model. The LHARQ-CJ model, which takes into account the jumping behavior of Chinese stock market volatility and the leverage effect, is the best among all the models. Excellent.
【作者单位】：中共泰安市委党校;山东大学经济学院;
【基金】：教育部人文社科规划基金项目(13YJAZH091)
【分类号】：F224;F832.51
【正文快照】： 1引言股市波动率的建模与预测一直以来是金融理论研究的重要命题之一。它对资产组合选择、金融资产及其衍生品定价、以及金融机构的风险管理都具有重要意义。20世纪80年代起,国内外学者提出了基于低频数据的GARCH类模型和SV类模型对股市波动率进行估计和预测,很好地刻画了股市

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