Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/244416
Type: Artigo
Title: Bootstrap prediction in univariate volatility models with leverage effect
Author: Trucíos, Carlos
Hotta, Luiz K.
Abstract: The EGARCH and GJR-GARCH models are widely used in modeling volatility when a leverage effect is present in the data. Traditional methods of constructing prediction intervals for time series normally assume that the model parameters are known, and the innovations are normally distributed. When these assumptions are not true, the prediction interval obtained usually has the wrong coverage. In this article, the Pascual, Romo and Ruiz (PRR) algorithm, developed to obtain prediction intervals for GARCH models, is adapted to obtain prediction intervals of returns and volatilities in EGARCH and GJR-GARCH models. These adjustments have the same advantage of the original PRR algorithm, which incorporates a component of uncertainty due to parameter estimation and does not require assumptions about the distribution of the innovations. The adaptations show good performance in Monte Carlo experiments. However, the performance, especially in volatility prediction, can be very poor in the presence of an additive outlier near the forecasting origin. The algorithms are applied to the daily returns series of the GBP/USD exchange rates. (C) 2015 International Association for Mathematics and Computers in Simulation (IMACS). Published by Elsevier B.V. All rights reserved.
The EGARCH and GJR-GARCH models are widely used in modeling volatility when a leverage effect is present in the data. Traditional methods of constructing prediction intervals for time series normally assume that the model parameters are known, and the innovations are normally distributed. When these assumptions are not true, the prediction interval obtained usually has the wrong coverage. In this article, the Pascual, Romo and Ruiz (PRR) algorithm, developed to obtain prediction intervals for GARCH models, is adapted to obtain prediction intervals of returns and volatilities in EGARCH and GJR-GARCH models. These adjustments have the same advantage of the original PRR algorithm, which incorporates a component of uncertainty due to parameter estimation and does not require assumptions about the distribution of the innovations. The adaptations show good performance in Monte Carlo experiments. However, the performance, especially in volatility prediction, can be very poor in the presence of an additive outlier near the forecasting origin. The algorithms are applied to the daily returns series of the GBP/USD exchange rates.
Subject: Previsão estatística
Valores estranhos (Estatistica)
Modelo GARCH
Bootstrap (Estatística)
Country: Países Baixos
Editor: Elsevier
Citation: Bootstrap Prediction In Univariate Volatility Models With Leverage Effect. Elsevier Science Bv, v. 120, p. 91-103 FEB-2016.
Rights: fechado
Identifier DOI: 10.1016/j.matcom.2015.07.001
Address: https://www.sciencedirect.com/science/article/pii/S0378475415001330
Date Issue: 2016
Appears in Collections:IMECC - Artigos e Outros Documentos

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