Semiparametric efficient adaptive estimation of the GJR-GARCH model

IF 1.3 Q2 STATISTICS & PROBABILITY

Statistics & Risk Modeling Pub Date : 2018-12-01 DOI:10.1515/strm-2017-0015

Nicola Ciccarelli

引用次数: 0

Abstract

Abstract In this paper we derive a semiparametric efficient adaptive estimator for the GJR-GARCH ( 1 , 1 ) {(1,1)} model. We first show that the quasi-maximum likelihood estimator is consistent and asymptotically normal for the model used in analysis, and we secondly derive a semiparametric estimator that is more efficient than the quasi-maximum likelihood estimator. Through Monte Carlo simulations, we show that the semiparametric estimator is adaptive for the parameters included in the conditional variance of the GJR-GARCH ( 1 , 1 ) {(1,1)} model with respect to the unknown distribution of the innovation.

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GJR-GARCH模型的半参数有效自适应估计

摘要本文推导了GJR-GARCH（1,1）{（1,1）}模型的半参数有效自适应估计量。我们首先证明了拟最大似然估计量对于分析中使用的模型是一致的和渐近正态的，然后我们推导了一个比拟最大似然估计器更有效的半参数估计器。通过蒙特卡罗模拟，我们证明了半参数估计器对于GJR-GARCH（1，1）{（1，）}模型的条件方差中包含的参数对于未知的创新分布是自适应的。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Statistics & Risk Modeling STATISTICS & PROBABILITY-

CiteScore

1.80

自引率

6.70%

发文量

期刊介绍： Statistics & Risk Modeling (STRM) aims at covering modern methods of statistics and probabilistic modeling, and their applications to risk management in finance, insurance and related areas. The journal also welcomes articles related to nonparametric statistical methods and stochastic processes. Papers on innovative applications of statistical modeling and inference in risk management are also encouraged. Topics Statistical analysis for models in finance and insurance Credit-, market- and operational risk models Models for systemic risk Risk management Nonparametric statistical inference Statistical analysis of stochastic processes Stochastics in finance and insurance Decision making under uncertainty.