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

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 h...

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Autor principal: Poonvoralak, Wantanee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Taylor & Francis 2022
Materias:
Application Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228328/
https://www.ncbi.nlm.nih.gov/pubmed/37260472
http://dx.doi.org/10.1080/02664763.2022.2064440
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author Poonvoralak, Wantanee
author_facet Poonvoralak, Wantanee
author_sort Poonvoralak, Wantanee
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description 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.
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spelling pubmed-102283282023-05-31 Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19 Poonvoralak, Wantanee J Appl Stat Application Notes 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. Taylor & Francis 2022-04-26 /pmc/articles/PMC10228328/ /pubmed/37260472 http://dx.doi.org/10.1080/02664763.2022.2064440 Text en © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group https://creativecommons.org/licenses/by-nc-nd/4.0/This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/ (https://creativecommons.org/licenses/by-nc-nd/4.0/) ), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way.
spellingShingle Application Notes
Poonvoralak, Wantanee
Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title_full Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title_fullStr Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title_full_unstemmed Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title_short Bayesian Markov Chain Monte Carlo for reparameterized Stochastic volatility models using Asian FX rates during Covid-19
title_sort bayesian markov chain monte carlo for reparameterized stochastic volatility models using asian fx rates during covid-19
topic Application Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10228328/
https://www.ncbi.nlm.nih.gov/pubmed/37260472
http://dx.doi.org/10.1080/02664763.2022.2064440
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