Expected shortfall estimation and capital coverage under overlapping scenarios
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Martin Aichele, Igor Cialenco, Damian Jelito, Marcin Pitera · arXiv: 2610.41624v1 · Summary page
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Overlapping loss scenarios can make bank capital look safer
Banks often estimate expected shortfall—a measure of losses in the bad tail of outcomes—using overlapping multi-day returns. But the estimators commonly applied to those returns assume independent observations. A preprint combines simulations with market data on twelve risk factors and ninety-nine equity portfolios to investigate the mismatch. Overlapping sampling systematically lowers estimated expected shortfall, rather than merely making estimates noisier. Serial dependence pushes in the same direction, and heavy tails amplify both effects.
More conservative weighting reduces the understatement but does not eliminate it. In the market data, the confidence level at which estimated capital covers realized losses roughly doubles in median under overlapping sampling for every estimator examined. Meanwhile, the ratio linking the newer expected-shortfall capital metric to its value-at-risk predecessor changes comparatively little. A familiar comparison between risk measures can therefore look relatively stable even as capital coverage deteriorates. The authors advocate combining simulation-based checks with direct coverage diagnostics in model validation.
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