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dc.contributor.authorGaglianone, Wagner Piazza
dc.contributor.authorLinton, Oliver
dc.contributor.authorLima, Luiz Renato Regis de Oliveira
dc.date.accessioned2008-09-04T16:42:03Z
dc.date.accessioned2010-09-23T18:57:00Z
dc.date.available2008-09-04T16:42:03Z
dc.date.available2010-09-23T18:57:00Z
dc.date.issued2008-09-04
dc.identifier.urihttp://hdl.handle.net/10438/1718
dc.description.abstractThis paper is concerned with evaluating value at risk estimates. It is well known that using only binary variables to do this sacrifices too much information. However, most of the specification tests (also called backtests) avaliable in the literature, such as Christoffersen (1998) and Engle and Maganelli (2004) are based on such variables. In this paper we propose a new backtest that does not realy solely on binary variable. It is show that the new backtest provides a sufficiant condition to assess the performance of a quantile model whereas the existing ones do not. The proposed methodology allows us to identify periods of an increased risk exposure based on a quantile regression model (Koenker & Xiao, 2002). Our theorical findings are corroborated through a monte Carlo simulation and an empirical exercise with daily S&P500 time series.eng
dc.language.isoeng
dc.publisherFundação Getulio Vargas. Escola de Pós-graduação em Economiapor
dc.relation.ispartofseriesEnsaios Econômicos;679por
dc.subjectValue-at-riskeng
dc.subjectBacktestingpor
dc.subjectQuantile regressionseng
dc.titleEvaluating Value-at-Risk models via Quantile regressionseng
dc.typeWorking Papereng
dc.subject.areaEconomiapor
dc.contributor.unidadefgvEscolas::EPGEpor
dc.subject.bibliodataRisco (Economia)por
dc.contributor.affiliationFGV


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