Using skewed exponential power mixture for VaR and CVaR forecasts to comply with market risk regulation

Samir Saissi Hassani () and Georges Dionne ()
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Samir Saissi Hassani: HEC Montreal, Canada Research Chair in Risk Management

No 23-2, Working Papers from HEC Montreal, Canada Research Chair in Risk Management

Abstract: We demonstrate how a mixture of two SEP3 densities (skewed exponential power distribution of Fernández et al., 1995) can model the conditional forecasting of VaR and CVaR to efficiently cover market risk at regulatory levels of 1% and 2.5%, as well as at the additional 5% level. Our data consists of a sample of market asset returns, relating to a period of extreme market turmoil, showing typical leptokurtosis and skewness. The SEP3 mixture outcomes are benchmarked using various competing models, including the generalized Pareto distribution. Appropriate scoring functions quickly highlight valuable models, which undergo conventional backtests. As an additional backtest, we argue for and apply the CVaR part of the optimality test of Patton et al. (2019) to assess the conditional adequacy of CVaR. An additional aim of this paper is to present a collaborative framework that relies on both comparative and conventional backtesting tools, all in compliance with the recent Basel regulation for market-risk.

Keywords: Conditional forecasting; VaR; CVaR; backtesting; Basel regulation for market risk; heavy tailed distributions (search for similar items in EconPapers)
JEL-codes: C44 C46 C52 G21 G24 G28 G32 (search for similar items in EconPapers)
Pages: 58 pages
Date: 2023-03-10
New Economics Papers: this item is included in nep-rmg
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