Convergence assessment techniques for Markov chain Monte Carlo
Statistics and Computing
Bayesian estimation of the Gaussian mixture GARCH model
Computational Statistics & Data Analysis
Analysis of filtering and smoothing algorithms for Lévy-driven stochastic volatility models
Computational Statistics & Data Analysis
Implied volatility in oil markets
Computational Statistics & Data Analysis
Leverage, heavy-tails and correlated jumps in stochastic volatility models
Computational Statistics & Data Analysis
Pricing currency options with support vector regression and stochastic volatility model with jumps
Expert Systems with Applications: An International Journal
Computational Statistics & Data Analysis
| Hi-index | 0.05 |
Stochastic volatility models are important tools for studying the behavior of many financial markets. For this reason a number of versions have been introduced and studied in the recent literature. The goal is to review and compare some of these alternatives by using Bayesian procedures. The quantity used to assess the goodness-of-fit is the Bayes factor, whereas the ability to forecast the volatility has been tested through the computation of the one-step-ahead value-at-risk (VaR). Model estimation has been carried out through adaptive Markov chain Monte Carlo (MCMC) procedures. The marginal likelihood, necessary to compute the Bayes factor, has been computed through reduced runs of the same MCMC algorithm and through an auxiliary particle filter. The empirical analysis is based on the study of three international financial indexes.