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

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Abstract

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.
Original languageEnglish
Title of host publicationBayesian Statistics and New Generations - Selected Contributions from BAYSM 2018
Editors Raffaele Argiento, Daniele Durante, Sara Wade
Place of PublicationCham
PublisherSpringer
Pages75 - 83
ISBN (Print)978-3-030-30610-6
Publication statusPublished - 2019

Austrian Classification of Fields of Science and Technology (ÖFOS)

  • 102022 Software development
  • 101018 Statistics
  • 502025 Econometrics
  • 101026 Time series analysis

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