Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check

Evaluating the Forecasting Performance of GARCH Models Using White’s Reality Check

dc.creatorSouza, Leonardo
dc.creatorVeiga, Alvaro
dc.creatorMedeiros, Marcelo C.
dc.date2005-05-01
dc.date.accessioned2022-11-03T21:17:58Z
dc.date.available2022-11-03T21:17:58Z
dc.identifierhttps://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/2671
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/5047731
dc.descriptionThe important issue of forecasting volatilities brings the difficult task of back-testing the forecasting performance. As volatility cannot be observed directly, one has to use an observable proxy for volatility or a utility function to assess the prediction quality. This kind of procedure can easily lead to poor assessment. The goal of this paper is to compare different volatility models and different performance measures using White’s Reality Check. The Reality Check consists of a non-parametric test that checks if any of a number of concurrent methods yields forecasts significantly better than a given benchmark method. For this purpose, a Monte Carlo simulation is carried out with four different processes, one of them a Gaussian white noise and the others following GARCH specifications. Two benchmark methods are used: the naive (predicting the out-of-sample volatility by in-sample variance) and the Riskmetrics methoden-US
dc.descriptionThe important issue of forecasting volatilities brings the difficult task of back-testing the forecasting performance. As volatility cannot be observed directly, one has to use an observable proxy for volatility or a utility function to assess the prediction quality. This kind of procedure can easily lead to poor assessment. The goal of this paper is to compare different volatility models and different performance measures using White’s Reality Check. The Reality Check consists of a non-parametric test that checks if any of a number of concurrent methods yields forecasts significantly better than a given benchmark method. For this purpose, a Monte Carlo simulation is carried out with four different processes, one of them a Gaussian white noise and the others following GARCH specifications. Two benchmark methods are used: the naive (predicting the out-of-sample volatility by in-sample variance) and the Riskmetrics methodpt-BR
dc.formatapplication/pdf
dc.languageeng
dc.publisherSociedade Brasileira de Econometriaen-US
dc.relationhttps://bibliotecadigital.fgv.br/ojs/index.php/bre/article/view/2671/1622
dc.sourceBrazilian Review of Econometrics; Vol. 25 No. 1 (2005); 43–66en-US
dc.sourceBrazilian Review of Econometrics; v. 25 n. 1 (2005); 43–66pt-BR
dc.source1980-2447
dc.subjectTime seriesen-US
dc.subjectGARCH modelsen-US
dc.subjectBootstrapen-US
dc.subjectReality checken-US
dc.subjectVolatilityen-US
dc.subjectFinancial econometricsen-US
dc.subjectMonte Carloen-US
dc.subjectForecastingen-US
dc.subjectRiskmetricsen-US
dc.subjectMoving averageen-US
dc.subjectC45en-US
dc.subjectC51en-US
dc.subjectC52en-US
dc.subjectC61en-US
dc.subjectG12en-US
dc.subjectTime seriespt-BR
dc.subjectGARCH modelspt-BR
dc.subjectBootstrappt-BR
dc.subjectReality checkpt-BR
dc.subjectVolatilitypt-BR
dc.subjectFinancial econometricspt-BR
dc.subjectMonte Carlopt-BR
dc.subjectForecastingpt-BR
dc.subjectRiskmetricspt-BR
dc.subjectMoving averagept-BR
dc.subjectC45pt-BR
dc.subjectC51pt-BR
dc.subjectC52pt-BR
dc.subjectC61pt-BR
dc.subjectG12pt-BR
dc.titleEvaluating the Forecasting Performance of GARCH Models Using White’s Reality Checken-US
dc.titleEvaluating the Forecasting Performance of GARCH Models Using White’s Reality Checkpt-BR
dc.typeinfo:eu-repo/semantics/article
dc.typeinfo:eu-repo/semantics/publishedVersion


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