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Evaluating Value-at-Risk models via Quantile regressions

Wagner Gaglianone (), Oliver Linton () and Luiz Lima ()

No 679, FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) from EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil)

Abstract: This 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.

Date: 2008-09-04
New Economics Papers: this item is included in nep-ecm and nep-rmg
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http://bibliotecadigital.fgv.br/dspace/bitstream/10438/1718/2/VQR.pdf (application/pdf)

Related works:
Journal Article: Evaluating Value-at-Risk Models via Quantile Regression (2011) Downloads
Journal Article: Evaluating Value-at-Risk Models via Quantile Regression (2011) Downloads
Working Paper: Evaluating Value-at-Risk Models via Quantile Regression (2010) Downloads
Working Paper: Evaluating Value-at-Risk models via Quantile Regression (2009) Downloads
Working Paper: Evaluating Value-at-Risk Models via Quantile Regressions (2008) Downloads
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