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

DOI10.1080/02664763.2022.2064440OpenAlexW4224928706MaRDI QIDQ6176281

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Publication date: 25 July 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02664763.2022.2064440

zbMATH Keywords

Markov chain Monte Carlo (MCMC)Bayesian computationstochastic volatility modelAsian FX rates

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

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• Bayesian forecasting and dynamic models
• Statistical methods in finance
• Markov chain Monte Carlo in conditionally Gaussian state space models
• A Subordinated Stochastic Process Model with Finite Variance for Speculative Prices
• Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models
• Analytic Convergence Rates and Parameterization Issues for the Gibbs Sampler Applied to State Space Models
• Multivariate Stochastic Variance Models
• On Gibbs sampling for state space models
• Likelihood analysis of non-Gaussian measurement time series
• Variable dimension via stochastic volatility model using FX rates
• GMM estimation of a realized stochastic volatility model: A Monte Carlo study
• Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling.

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