Comparison of MCMC Methods for Estimating Stochastic Volatility Models
Computational Economics
Comparison of MCMC Methods for Estimating Stochastic Volatility Models
Computational Economics
Bayesian analysis of stochastic volatility models with mixture-of-normal distributions
Mathematics and Computers in Simulation
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This article investigates performances of MCMC methods to estimate stochastic volatility models on simulated and real data. There are two efficient MCMC methods to generate latent volatilities from their full conditional distribution. One is the mixture sampler and the other is the multi-move sampler. There is another efficient method for latent volatilities and all parameters called the integration sampler, which is based on the mixture sampler. This article proposes an alternative method based on the multi-move sampler and finds evidence that it is the best method among them.