MCMC Bayesian Estimation in FIEGARCH Models

被引：3

作者：

Prass, Taiane S. ^{[1
]}

Lopes, Silvia R. C. ^{[1
]}

Achcar, Jorge A. ^{[2
]}

机构：

[1] Univ Fed Rio Grande do Sul, Math Inst, Porto Alegre, RS, Brazil

[2] Univ Sao Paulo, Sch Med, Dept Social Med, Sao Paulo, SP, Brazil

来源：

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | 2016年 / 45卷 / 09期

关键词：

Bayesian inference; FIEGARCH processes; Long-range dependence; MCMC; 62F15; 62M10; LONG-MEMORY;

D O I：

10.1080/03610918.2014.932800

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：

Bayesian inference for fractionally integrated exponential generalized autoregressive conditional heteroscedastic (FIEGARCH) models using Markov chain Monte Carlo (MCMC) methods is described. A simulation study is presented to assess the performance of the procedure, under the presence of long-memory in the volatility. Samples from FIEGARCH processes are obtained upon considering the generalized error distribution (GED) for the innovation process. Different values for the tail-thickness parameter are considered covering both scenarios, innovation processes with lighter ( > 2) and heavier ( < 2) tails than the Gaussian distribution ( = 2). A comparison between the performance of quasi-maximum likelihood (QML) and MCMC procedures is also discussed. An application of the MCMC procedure to estimate the parameters of a FIEGARCH model for the daily log-returns of the S&P500 U.S. stock market index is provided.

引用

页码：3238 / 3258

页数：21

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