A0184
Title: Stochastic volatility in mean: Efficient analysis by a generalized mixture sampler
Authors: Siddhartha Chib - Washington University in Saint Louis (United States)
Yasuhiro Omori - University of Tokyo (Japan)
Daichi Hiraki - University of Tokyo (Japan) [presenting]
Abstract: The simulation-based Bayesian analysis of stochastic volatility in mean (SVM) models is considered. Extending the highly efficient Markov chain Monte Carlo mixture sampler for the SV model proposed in existing 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.
