A0604
Title: Stochastic volatility in mean: Efficient analysis by a generalized mixture sampler
Authors: Daichi Hiraki - University of Tokyo (Japan) [presenting]
Siddhartha Chib - Washington University in Saint Louis (United States)
Yasuhiro Omori - University of Tokyo (Japan)
Abstract: The simulation-based Bayesian analysis of stochastic volatility is considered in mean (SVM) models. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in prior studies, an accurate approximation of the non-central chi-squared distribution is developed as a mixture of thirty normal distributions. Under this mixture representation, the parameters and latent volatilities are sampled in one block. A correction of the small approximation error is also detailed by using additional Metropolis-Hastings steps. The proposed method is extended to the SVM model with leverage. The methodology and models are applied to excess holding yields in empirical studies, and the SVM model with leverage is shown to outperform competing volatility models based on marginal likelihoods.
