Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19

Wantanee Poonvoralak

Journal of Applied Statistics, 2023, vol. 50, issue 8, 1853-1875

Abstract: In this paper, reparameterization and student-t are applied to Stochastic Volatility (SV) model. We aim to reduce the amount of autocorrelation of the SV parameters and to introduce heavy-tailed model via the Bayesian computation of the Markov Chain Monte Carlo (MCMC) samplers. This research paper helps support better MCMC estimation of the SV model for volatile Asian FX series during Covid-19.

Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:taf:japsta:v:50:y:2023:i:8:p:1853-1875

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DOI: 10.1080/02664763.2022.2064440

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