Quotation Kastner, Gregor, Frühwirth-Schnatter, Sylvia. 2010. Efficient Bayesian Inference for Stochastic Volatility Models. 4th CSDA International Conference on Computational and Financial Econometrics (CFE'10), Senate House, University of London, Großbritannien, 10.12.-12.12.


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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 $\chi^2$-distribuion by a mixture of 10 normal distribution 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 parametrization, augmented MCMC estimation turns out to be inefficient, in particular for the volatility of volatility parameter in the latent state equation. 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 volatility of volatility parameter considerably.

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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Paper presented at an academic conference or symposium
Language English
Title Efficient Bayesian Inference for Stochastic Volatility Models
Event 4th CSDA International Conference on Computational and Financial Econometrics (CFE'10)
Year 2010
Date 10.12.-12.12
Country United Kingdom
Location Senate House, University of London
URL http://www.cfe-csda.org/cfe10/

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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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