Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage

Hosszejni, Darjus ORCID: and Kastner, Gregor ORCID: (2019) Approaches Toward the Bayesian Estimation of the Stochastic Volatility Model with Leverage. In: Bayesian Statistics and New Generations - Selected Contributions from BAYSM 2018. Springer Proceedings in Mathematics & Statistics, 296. Springer, Cham. pp. 75-83. ISBN 978-3-030-30610-6


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The sampling efficiency of MCMC methods in Bayesian inference for stochastic volatility (SV) models is known to highly depend on the actual parameter values, and the effectiveness of samplers based on different parameterizations varies significantly. We derive novel algorithms for the centered and the non-centered parameterizations of the practically highly relevant SV model with leverage, where the return process and innovations of the volatility process are allowed to correlate. Moreover, based on the idea of ancillarity-sufficiency interweaving (ASIS), we combine the resulting samplers in order to guarantee stable sampling efficiency irrespective of the baseline parameterization.We carry out an extensive comparison to already existing sampling methods for this model using simulated as well as real world data.

Item Type: Book Section
Keywords: ancillarity-sufficiency interweaving strategy (ASIS), auxiliary mixture sampling, Bayesian inference, Markov chain Monte Carlo (MCMC), state-space model
Divisions: Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Version of the Document: Draft
Variance from Published Version: Typographical
Depositing User: Gregor Kastner
Date Deposited: 29 Nov 2019 07:34
Last Modified: 22 Nov 2020 04:44
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