Artículos de revistas
Bayesian Estimation And Prediction Of Stochastic Volatility Models Via Inla
Registro en:
Bayesian Estimation And Prediction Of Stochastic Volatility Models Via Inla. Taylor & Francis Inc, v. 44, p. 683-693 2015.
0361-0918
WOS:000342134100009
10.1080/03610918.2013.790444
Autor
Ehlers
Ricardo; Zevallos
M.
Institución
Resumen
Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) 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. 44 3
683 693 Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Laboratorio EPIFISMA Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP) FAPESP [2011/22317-0]