| Full text | |
| Author(s): |
Total Authors: 2
|
| Affiliation: | [1] Univ Sao Paulo, Sao Carlos, SP - Brazil
[2] Univ Estadual Campinas, Campinas, SP - Brazil
Total Affiliations: 2
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| Document type: | Journal article |
| Source: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 44, n. 3, p. 683-693, 2015. |
| Web of Science Citations: | 1 |
| Abstract | |
In this article, we assess Bayesian estimation and prediction using integrated Laplace approximation (INLA) on a stochastic volatility (SV) model. This was performed through a Monte Carlo study with 1,000 simulated time series. To evaluate the estimation method, two criteria were considered: the bias and square root of the mean square error (smse). The criteria used for prediction are the one step ahead forecast of volatility and the one day Value at Risk (VaR). The main findings are that the INLA approximations are fairly accurate and relatively robust to the choice of prior distribution on the persistence parameter. Additionally, VaR estimates are computed and compared for three financial time series returns indexes. (AU) | |
| FAPESP's process: | 11/22317-0 - Multivariate GARCH models with skewed distributions |
| Grantee: | Ricardo Sandes Ehlers |
| Support Opportunities: | Regular Research Grants |