Bayesian estimation and the application of long memory stochastic volatility models

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• 作者：Lixia Xu ; Cihua Liu ; Gaoqin Nie
• 关键词：Long memory stochastic volatility ; ARFIMA ; Spectral ; Periodogram
• 刊名：Statistical Methodology
• 出版年：2006
• 出版时间：October 2006
• 年：2006
• 卷：3
• 期：4
• 页码：483-489
• 全文大小：251 K

文摘

A new sampling-based Bayesian approach to the long memory stochastic volatility (LMSV) process is presented; the method is motivated by the GPH-estimator in fractionally integrated autoregressive moving average (ARFIMA) processes, which was originally proposed by J. Geweke and S. Porter-Hudak [The estimation and application of long memory time series models, Journal of Time Series Analysis, 4 (1983) 221–238]. In this work, we perform an estimation of the memory parameter in the Bayesian framework; an estimator is obtained by maximizing the posterior density of the memory parameter. Finally, we compare the GPH-estimator and the Bayes-estimator by means of a simulation study and our new approach is illustrated using several stock market indices; the new estimator is proved to be relatively stable for the various choices of frequencies used in the regression.