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Backtesting expected shortfall and beyond

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  • Kaihua Deng
  • Jie Qiu

Abstract

We conduct a comprehensive study of the performance of leading backtesting procedures for expected shortfall. The tests differ in their analytical complexity, stability over different models, sensitivity to the sample sizes (both estimation and backtesting), and computational burden. The best performing scenario depends on the interaction between estimation error and backtesting error. We document that the speed of convergence to the nominal size also varies across tests. Traditional tests may fail to validate the candidate model, in which case we show that a scoring function test based on the joint elicitability of VaR-ES may have merit from a model comparison perspective.

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  • Kaihua Deng & Jie Qiu, 2021. "Backtesting expected shortfall and beyond," Quantitative Finance, Taylor & Francis Journals, vol. 21(7), pages 1109-1125, July.
    • Handle: RePEc:taf:quantf:v:21:y:2021:i:7:p:1109-1125
    DOI: 10.1080/14697688.2020.1834120
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    Cited by:

    1. Fortin, Alain-Philippe & Simonato, Jean-Guy & Dionne, Georges, 2023. "Forecasting expected shortfall: Should we use a multivariate model for stock market factors?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 314-331.
    2. Michael Grabchak & Eliana Christou, 2021. "A note on calculating expected shortfall for discrete time stochastic volatility models," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-16, December.

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