Bayesian estimation for a semiparametric nonlinear volatility model

被引：5

作者：

Hu, Shuowen ^{[1
]}

Poskitt, D. S. ^{[2
]}

Zhang, Xibin ^{[2
]}

机构：

[1] Natl Australia Bank, Melbourne, Vic, Australia

[2] Monash Univ, Monash Business Sch, Dept Econometr & Business Stat, Clayton, Vic, Australia

来源：

ECONOMIC MODELLING | 2021年 / 98卷

关键词：

Backtesting; Cross-validation; Nadaraya-Watson estimator; Unknown error distribution; Value-at-risk; STOCHASTIC VOLATILITY; TIME-SERIES; P; 100; ARCH; INFERENCE;

D O I：

10.1016/j.econmod.2020.11.005

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper presents a new volatility model which extends the nonstationary nonparametric volatility model of Han and Zhang (2012) by including an ARCH(1) component This model also allows the errors to be independent and follow an unknown distribution. A Bayesian sampling algorithm is presented to estimate the ARCH coefficient and smoothing parameters. Empirical results show that the proposed model outperforms its competitors under several evaluation criteria.

引用

页码：361 / 370

页数：10

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