作　　者： ; ;

机构地区： 西安交通大学金禾经济研究中心

出　　处： 《预测》 2009年第5期20-26,共7页

摘　　要： 使用高频数据构造的实现类波动率估计量已经越来越多的用于波动率模型预测精度的衡量与比较。本文采用来自沪深两市的主要股指日内高频数据,构建了两类日内波动率的非参估计量实现波动RV与实现极差RR,分别用于GARCH和CARR类模型的比较与扩展;并使用Mincer-Zarnowitz(MZ)回归方程和基于此基础之上的一个渐进正态检验统计量对各模型的相对优劣及统计显著性进行对比研究。结果显示,标准CARR表现最好,而GARCH类扩展以及CARR类扩展模型均未能显著提升模型的预测能力,从实证上说明了CARR模型使用每日价格极差信息对波动率建模是充分有效的。 The realized volatility estimators constructed by high-frequency data have gained popularity in measuring and comparing the forecast abilities of different volatility models. We employ intra-day high frequency data of two main indexes in Shanghai and Shenzhen Stock Markets in constructing two kinds of non-parametric estimators realized volatility and realized range, which are used to compare GARCH and CARR families and build their extensions. Mincer-Zarnowitz regression equation and an asymptotic normal statistics based the equation are used to judge the satistical significance of their superiority. The results show that standard CARR model performs the best, and GARCH extensions as well as those CARR extensions do not improve the standard model＇ s forecasting ability in the sense of statistical significance, which demonstrates empirically that modeling volatility with daily price ranges of CARR model is highly efficient.

关 键 词： 实现极差 实现波动 高频数据

领　　域： [经济管理]