Bootstrap Prediction In Univariate Volatility Models With Leverage Effect

dc.creatorTrucios
dc.creatorCarlos; Hotta
dc.creatorLuiz K.
dc.date2016-FEB
dc.date2016-06-07T13:37:06Z
dc.date2016-06-07T13:37:06Z
dc.date.accessioned2018-03-29T01:52:16Z
dc.date.available2018-03-29T01:52:16Z
dc.identifier
dc.identifierBootstrap Prediction In Univariate Volatility Models With Leverage Effect. Elsevier Science Bv, v. 120, p. 91-103 FEB-2016.
dc.identifier0378-4754
dc.identifierWOS:000364889700008
dc.identifier10.1016/j.matcom.2015.07.001
dc.identifierhttp://www.sciencedirect.com/science/article/pii/S0378475415001330
dc.identifierhttp://repositorio.unicamp.br/jspui/handle/REPOSIP/244416
dc.identifier.urihttp://repositorioslatinoamericanos.uchile.cl/handle/2250/1308114
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionThe 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.
dc.description120
dc.description
dc.description
dc.description91
dc.description103
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionLaboratory EPIFISMA
dc.descriptionFundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)
dc.descriptionFAPESP [2012/09596-0, 2013/00506-1]
dc.description
dc.description
dc.description
dc.languageen
dc.publisherELSEVIER SCIENCE BV
dc.publisher
dc.publisherAMSTERDAM
dc.relationMATHEMATICS AND COMPUTERS IN SIMULATION
dc.rightsembargo
dc.sourceWOS
dc.subjectConditional Heteroskedasticity
dc.subjectArch Models
dc.subjectTime-series
dc.subjectIntervals
dc.subjectReturns
dc.titleBootstrap Prediction In Univariate Volatility Models With Leverage Effect
dc.typeArtículos de revistas


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