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A comparison of Expected Shortfall estimation models

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  • Righi, Marcelo Brutti
  • Ceretta, Paulo Sergio

Abstract

We investigate whether there is a pattern regarding the quality of several models and methods in expected shortfall (ES) estimation, considering distinct asset classes, estimation windows and significance levels. We use unconditional, conditional and quantile/expectile regression-based models. We assess the performance of the models using the usual ES backtest and a proposed test based on the dispersion truncated by VaR. A Monte Carlo experiment indicates that our test has more power than the usual tests. The empirical results emphasize several patterns, such as certain estimation models produce risk estimates more parsimonious than the others do. In addition, VaR estimation is important for ES estimation, once incorrect violation can lead to low p-values in the backtests. Moreover, small estimation windows lead to inferior ES estimation results, as well as there are distinctions in the results for different quantiles of interest. Regarding performance, there is a predominance of conditional models, especially those that respect the stylized facts of financial returns.

Suggested Citation

  • Righi, Marcelo Brutti & Ceretta, Paulo Sergio, 2015. "A comparison of Expected Shortfall estimation models," Journal of Economics and Business, Elsevier, vol. 78(C), pages 14-47.
    • Handle: RePEc:eee:jebusi:v:78:y:2015:i:c:p:14-47
    DOI: 10.1016/j.jeconbus.2014.11.002
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    More about this item

    Keywords

    Risk measures; Expected shortfall; Risk estimation; Backtesting;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • C1 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General

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