非线性GARCH族的模型平均估计方法  

Model Averaging Estimation Method for Nonlinear GARCH Family

作　　者：姚青松[1] 赵国庆[1] 刘庆丰 Yao Qingsong;Zhao Guoqing;Liu Qingfeng

机构地区：[1]中国人民大学经济学院 [2]日本国立小樽商科大学商学部

出　　处：《统计研究》2018年第5期119-128,共10页Statistical Research

摘　　要：本文研究了非线性GARCH族的模型平均估计方法,在备选模型集合包含拥有不同模型结构的非线性GARCH族的情况下,本文构建了非线性GARCH族的模型平均估计量,并给出相应的权重选择准则,在一定正则条件下,证明了上述模型平均估计量具有渐近最优性,即渐近实现真实最优的KL偏离度。蒙特卡洛模拟结果表明,在大部分情形下,本文提出的模型平均估计量取得了更小的相对KL偏离值。作为非线性GARCH族的模型平均估计方法的应用,本文对2016年6月1日至2017年6月1日上证指数的日波动率进行估计,与现有模型选择与模型平均估计方法相比较,本文的模型平均估计方法具有更高的精度。This paper uses the model averaging estimation for nonlinear GARCH family. As the optional model set contains nonlinear GARCH families with different functional forms,this paper constructs model averaging estimators with the nonlinear GARCH family,and presents a criterion for choosing the corresponding weights. Under certaincanonical conditions,this paper proves the optimality of the above-mentioned model averaging estimators,i. e.,asymptotically achieving the minimum of KL divergence. Monte Carlo simulation results indicate that under most of the situations,the model averaging estimators achieves smaller KL divergence in comparison with the existing model selection and averaging methods. In an empirical study with this method,based on the estimation done on the daily volatility of Shanghai Composite Index from June 1 st2016 to June 1 st2017,the model averaging estimators proposed in this paper have produced much accurate results than those from the other available model averaging estimation methods.

关 键 词：模型平均 非线性GARCH族 渐近最优性 已实现波动率 

分 类 号：O212[理学—概率论与数理统计]