Estimation of stochastic volatility models via auxiliary particles filter
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| Autores: | , , |
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| Formato: | artículo original |
| Estado: | Versión publicada |
| Fecha de Publicación: | 2019 |
| Descripción: | The growing interest in the study of volatility for series of financial instruments leads us to propose a methodology based on the versatility of the Sequential Monte Carlo (SMC) methods for the estimation of the states of the general stochastic volatility model (GSVM). In this paper, we proposed a methodology based on the state space structure applying filtering techniques such as the auxiliary particles filter for estimating the underlying volatility of the system. Additionally, we proposed to use a Markov chain Monte Carlo (MCMC ) algorithm, such as is the Gibbs sampler for the estimation of the parameters. The methodology is illustrated through a series of returns of simulated data, and the series of returns corresponding to the Standard and Poor’s 500 price index (S&P 500) for the period 1995 − 2003. The results show that the proposed methodology allows to adequately explain the dynamics of volatility when there is an asymmetric response of this to a shock of a different sign, concluding that abruptchanges in returns correspond to high values in volatility. |
| País: | Portal de Revistas UCR |
| Institución: | Universidad de Costa Rica |
| Repositorio: | Portal de Revistas UCR |
| Lenguaje: | Español |
| OAI Identifier: | oai:portal.ucr.ac.cr:article/36221 |
| Acceso en línea: | https://revistas.ucr.ac.cr/index.php/matematica/article/view/36221 |
| Palabra clave: | stochastic volatility models space state models auxiliary particles filter. modelos de volatilidad estocástica modelos espacio estado filtro auxiliar de partículas |