Bayesian semiparametric stochastic volatility modeling

被引:69
|
作者
Jensen, Mark J. [1 ]
Maheu, John M. [2 ]
机构
[1] Fed Reserve Bank Atlanta, Atlanta, GA USA
[2] Univ Toronto, Toronto, ON M5S 1A1, Canada
关键词
Bayesian nonparametrics; Dirichlet process mixture prior; Markov chain Monte Carlo; Mixture models; Stochastic volatility; MONTE-CARLO METHODS; LIKELIHOOD INFERENCE; DIRICHLET MIXTURES; DISTRIBUTIONS; VARIANCE; LEVERAGE;
D O I
10.1016/j.jeconom.2010.01.014
中图分类号
F [经济];
学科分类号
02 ;
摘要
This paper extends the existing fully parametric Bayesian literature on stochastic volatility to allow for more general return distributions. Instead of specifying a particular distribution for the return innovation, nonparametric Bayesian methods are used to flexibly model the skewness and kurtosis of the distribution while the dynamics of volatility continue to be modeled with a parametric structure. Our semiparametric Bayesian approach provides a full characterization of parametric and distributional uncertainty. A Markov chain Monte Carlo sampling approach to estimation is presented with theoretical and computational issues for simulation from the posterior predictive distributions. An empirical example compares the new model to standard parametric stochastic volatility models. (C) 2010 Elsevier B.V. All rights reserved.
引用
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页码:306 / 316
页数:11
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