# Kastner, Gregor, Frühwirth-Schnatter, Sylvia. 2011. Improving MCMC Efficiency for Bayesian SV Estimation by Non-Centering and Interweaving. 17th European Young Statisticians Meeting, Universidade Nova de Lisboa, Portugal, 05.09.-09.09. Invited Talk

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## Abstract

This talk considers Bayesian inference for stochastic volatility (SV) models using efficient MCMC inference. Our method is based on the popular approximation of the log $\chi^2$-distribution by a mixture of 10 normal distributions which allows to sample the latent volatilities simultaneously, however, we introduce several improvements. First, rather than using standard forward-filtering-backward-sampling to draw the volatilities, we apply a sparse Cholesky factor algorithm to the high-dimensional joint density of all volatilities. This reduces computing time considerably because it allows joint sampling without running a filter. Second, we consider various reparameterizations of the augmented SV model. Under the standard parameterization, augmented MCMC estimation turns out to be inefficient, especially if the volatility of volatility parameter in the latent state equation is small. By considering a non-centered version of the SV model, this parameter is moved to the observation equation. Using MCMC estimation for this transformed model reduces the inefficiency factor in particular for the volatility of volatility parameter considerably. Finally, we adopt an interweaving strategy outperforming both centered and non-centered parameterizations in terms of sampling efficiency with respect to all parameters.

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## Publication's profile

Status of publication Published WU Paper presented at an academic conference or symposium English Improving MCMC Efficiency for Bayesian SV Estimation by Non-Centering and Interweaving 17th European Young Statisticians Meeting 2011 05.09.-09.09 Portugal Universidade Nova de Lisboa http://www.fct.unl.pt/17eysm Y

## Associations

People
Kastner, Gregor (Details)
Frühwirth-Schnatter, Sylvia (Details)
Organization
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1105 Computer software (Details)
1162 Statistics (Details)
5323 Econometrics (Details)
5701 Applied statistics (Details)
5707 Time series analysis (Details)