Quotation Kastner, Gregor, Frühwirth-Schnatter, Sylvia. 2011. Efficient Bayesian Inference for Stochastic Volatility Models. Workshop on Bayesian Inference for Latent Gaussian Models with Applications, Universität Zürich, Schweiz, 02.02.-05.02.


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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.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Poster presented at an academic conference or symposium
Language English
Title Efficient Bayesian Inference for Stochastic Volatility Models
Event Workshop on Bayesian Inference for Latent Gaussian Models with Applications
Date 02.02.-05.02
Location Universität Zürich
Country Switzerland
Year 2011
URL http://www.math.uzh.ch/index.php?konferenzdetails0&key1=215&L=1

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Kastner, Gregor (Details)
Frühwirth-Schnatter, Sylvia (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)
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