A dominance approach for comparing the performance of VaR forecasting models
Laura Garcia-Jorcano and
Alfonso Novales ()
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Laura Garcia-Jorcano: Department of Economic Analysis and Finance (Area of Financial Economics), Facultad de Ciencias Jurídicas y Sociales Universidad de Castilla-La Mancha, Toledo, Spain.
No 2019-23, Documentos de Trabajo del ICAE from Universidad Complutense de Madrid, Facultad de Ciencias Económicas y Empresariales, Instituto Complutense de Análisis Económico
We introduce three dominance criteria to compare the performance of alternative VaR forecasting models. The three criteria use the information provided by a battery of VaR validation tests based on the frequency and size of exceedances, offering the possibility of efficiently summarizing a large amount of statistical information. They do not require the use of any loss function defined on the difference between VaR forecasts and observed returns, and two of the criteria are not conditioned on any significance level for the VaR tests. We use them to explore the potential for 1-day ahead VaR forecasting of some recently proposed asymmetric probability distributions for return innovations, as well as to compare the APARCH and FGARCH volatility specifications with more standard alternatives. Using 19 assets of different nature, the three criteria lead to similar conclusions, suggesting that the unbounded Johnson SU, the skewed Student-t and the skewed Generalized-t distributions seem to produce the best VaR forecasts. The added flexibility of a free power parameter in the conditional volatility in the APARCH and FGARCH models leads to a better fit to return data, but it does not improve upon the VaR forecasts provided by GARCH and GJR-GARCH volatilities.
Keywords: Value at risk; Backtesting; Forecast evaluation; Dominance; Conditional volatility models; Asymmetric distributions. (search for similar items in EconPapers)
JEL-codes: C52 C58 G17 G32 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-for, nep-ore and nep-rmg
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