Bayesian inference for a mixture double autoregressive model

被引：6

作者：

Yang, Kai ^{[1
]}

Zhang, Qingqing ^{[1
]}

Yu, Xinyang ^{[1
]}

Dong, Xiaogang ^{[1
]}

机构：

[1] Changchun Univ Technol, Sch Math & Stat, Changchun 130012, Peoples R China

来源：

STATISTICA NEERLANDICA | 2023年 / 77卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Bayesian estimation; Bayes factor; financial time series; mixture double autoregressive model; heteroscedasticity test; STOCHASTIC VOLATILITY MODELS; TIME-SERIES; LONG MEMORY; GARCH; ESTIMATORS;

D O I：

10.1111/stan.12281

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

摘要：

This paper considers a mixture double autoregressive model with two components, which can flexibly capture the features usually exhibited by many financial returns such as heteroscedasticity, large kurtosis and multimodal marginals. Bayesian method based on modern Markov Chain Monte Carlo (MCMC) technology is used to estimate the model parameters. The heteroscedasticity test problem for the underlying process is also addressed by means of Bayes factor. The performances of the proposed methods are evaluated via some simulations. It is shown that the MCMC algorithm is an effective tool to deal with the mixture model. Finally, the proposed model is applied to the S&P500 index data.set.

引用

页码：188 / 207

页数：20

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