A Markov Chain Quasi-Monte Carlo Method for Bayesian Estimation of Stochastic Volatility Model

2010 ◽  
Author(s):  
Eric Fung ◽  
Ching-Wah Ho ◽  
Tak-Kuen Siu ◽  
Wing-Keung Wong
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Monte Carlo Method ◽  
Bayesian Estimation ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Quasi Monte Carlo ◽  
Volatility Model

scholarly journals Particle Markov Chain Monte Carlo Techniques of Unobserved Component Time Series Models Using Ox

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Nima Nonejad
Keyword(s):  
Time Series ◽  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Stochastic Volatility ◽  
Stochastic Volatility Model ◽  
Time Series Models ◽  
Unobserved Component ◽  
Volatility Model ◽  
Component Time

AbstractThis paper details particle Markov chain Monte Carlo (PMCMC) techniques for analysis of unobserved component time series models using several economic data sets. The objective of this paper is to explain the basics of the methodology and provide computational applications that justify applying PMCMC in practice. For instance, we use PMCMC to estimate a stochastic volatility model with a leverage effect, Student-t distributed errors or serial dependence. We also model time series characteristics of monthly US inflation rate by considering a heteroskedastic ARFIMA model where heteroskedasticity is specified by means of a Gaussian stochastic volatility process.

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