English, Article, Journal or magazine article edition: Evaluating Value-at-Risk models via Quantile regressions Gaglianone, Wagner Piazza; Linton, Oliver; Lima, Luiz Renato Regis de Oliveira

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Bookmark: http://trove.nla.gov.au/version/23575
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Evaluating Value-at-Risk models via Quantile regressions
Author
  • Gaglianone, Wagner Piazza
  • Linton, Oliver
  • Lima, Luiz Renato Regis de Oliveira
Physical Description
  • preprint
Notes
  • 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.
  • RePEc:fgv:epgewp:679
Language
  • English
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