Semiparametric Bayesian inference of long-memory stochastic volatility models

被引:32
作者
Jensen, MJ [1 ]
机构
[1] Brigham Young Univ, Provo, UT 84602 USA
关键词
Dirichlet process prior; long memory; Markov chain Monte Carlo; Metropolis-Hastings; semiparametric; stochastic volatility; wavelets;
D O I
10.1111/j.1467-9892.2004.00384.x
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In this paper, a semiparametric, Bayesian estimator of the long-memory stochastic volatility model's fractional order of integration is presented. This new estimator relies on a highly efficient, Markov chain Monte Carlo (MCMC) sampler of the model's posterior distribution. The MCMC algorithm is set forth in the time-scale domain of the stochastic volatility model's wavelet representation. The key to and centerpiece of this new algorithm is the quick and efficient multi-state sampler of the latent volatility's wavelet coefficients. A multi-state sampler of the latent wavelet coefficients is only possible because of the near-independent multivariate distribution of the long-memory process's wavelet coefficients. Using simulated and empirical stock return data, we find that our algorithm produces uncorrelated draws of the posterior distribution and point estimates that rival existing long-memory stochastic volatility estimators.
引用
收藏
页码:895 / 922
页数:28
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