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| Author: | Martino, S. (et al.) |
| Title: | Estimating stochastic volatility models using integrated nested Laplace approximations |
| Journal: | European Journal of Finance
2011 : AUG-SEP, VOL. 17:7-8, p. 487-503 |
| Index terms: | bayesian statistics models stochastic processes volatility data analysis |
| Freeterms: | approximate Bayesian inference integrated nested Laplace approximation (INLAs) latent Gaussian models Markov Chain Monte Carlo (MCMC) stochastic volatility model (SV) GARCH |
| Language: | eng |
| Abstract: | In this article, they solve the problem of inference for some SV models by applying a new inferential tool, integrated nested Laplace approximations (INLAs). INLA substitutes MCMC simulations with accurate deterministic approximations, making a full Bayesian analysis of many kinds of SV models extremely fast and accurate. Their hope is that the use of INLA will help SV models to become more appealing to the financial industry, where, due to their complexity, they are rarely used in practice. |
SCIMA