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

Darjus Hosszejni and Gregor Kastner

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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.

Date: 2019-01, Revised 2019-11
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Citations: View citations in EconPapers (4)

Published in In R. Argiento, D. Durante, and S. Wade, editors, Bayesian Statistics and New Generations - Selected Contributions from BAYSM 2018, volume 296 of Springer Proceedings in Mathematics & Statistics, pages 75-83, Cham, 2019. Springer

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