Comparing stochastic volatility models through Monte Carlo simulations
| Autor: | Davide Raggi, Silvano Bordignon |
|---|---|
| Rok vydání: | 2006 |
| Předmět: |
Statistics and Probability
MCMC Markov chain Stochastic volatility Stochastic modelling Applied Mathematics Monte Carlo method Markov chain Monte Carlo Bayes factor Jump diffusion Marginal likelihood Particle filters VaR Statistics::Computation Computational Mathematics symbols.namesake Computational Theory and Mathematics Econometrics symbols Particle filter Mathematics |
| Zdroj: | Computational Statistics & Data Analysis. 50:1678-1699 |
| ISSN: | 0167-9473 |
| DOI: | 10.1016/j.csda.2005.02.004 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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