Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/243303
Type: Artigo
Title: Bayesian estimation and prediction of stochastic volatility models via INLA
Author: Ehlers, Ricardo
Zevallos, M.
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.
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.
Subject: Teoria da previsão
Teoria bayesiana de decisão estatística
Teoria da estimativa
Sistemas estocásticos
Country: Estados Unidos
Editor: Taylor & Francis
Citation: Bayesian Estimation And Prediction Of Stochastic Volatility Models Via Inla. Taylor & Francis Inc, v. 44, p. 683-693 2015.
Rights: fechado
Identifier DOI: 10.1080/03610918.2013.790444
Address: https://www.tandfonline.com/doi/abs/10.1080/03610918.2013.790444
Date Issue: 2015
Appears in Collections:IMECC - Artigos e Outros Documentos

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