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On E-Backtesting: Generalizations and Sample Size Determination

  • Dennis Oestmann
  • Thorsten Dickhaus
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Abstract

We present an approach for determining sample sizes required to detect underestimations of the expected shortfall with a prescribed power when applying the recently proposed e-backtesting procedure. We consider scenarios in which the value-at-risk at level p is always estimated correctly, while the difference between the true expected shortfall and the value-at-risk is underestimated by a given factor r. We show that exploiting the structure of the backtest e-statistic proposed for backtesting the expected shortfall at level p enables the derivation of approximate lower bounds for the required sample sizes by considering a sequence of independent and identically distributed Bernoulli random variables. We also discuss potential limitations of this approximation and compare the resulting sample size requirements with those obtained in practical applications using Monte Carlo simulations. Furthermore, we present generalizations of the e-backtesting procedure, in particular to risk measures which constitute Bayes pairs.

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Cite

BibTeXRIS
BibTeX
@misc{oestmann2026ebacktesting,
  title = {{On E-Backtesting: Generalizations and Sample Size Determination}},
  author = {Dennis Oestmann and Thorsten Dickhaus},
  howpublished = {arXiv},
  year = {2026},
  month = sep,
  doi = {10.48550/arxiv.2609.05089},
  eprint = {2609.05089},
  archivePrefix = {arXiv},
  url = {https://arxiv.org/abs/2609.05089},
}
RIS
TY  - UNPB
TI  - On E-Backtesting: Generalizations and Sample Size Determination
AU  - Dennis Oestmann
AU  - Thorsten Dickhaus
PY  - 2026
DA  - 2026/09/04
PB  - arXiv
DO  - 10.48550/arxiv.2609.05089
UR  - https://arxiv.org/abs/2609.05089
AB  - We present an approach for determining sample sizes required to detect underestimations of the expected shortfall with a prescribed power when applying the recently proposed e-backtesting procedure. We consider scenarios in which the value-at-risk at level p is always estimated correctly, while the difference between the true expected shortfall and the value-at-risk is underestimated by a given factor r. We show that exploiting the structure of the backtest e-statistic proposed for backtesting the expected shortfall at level p enables the derivation of approximate lower bounds for the required sample sizes by considering a sequence of independent and identically distributed Bernoulli random variables. We also discuss potential limitations of this approximation and compare the resulting sample size requirements with those obtained in practical applications using Monte Carlo simulations. Furthermore, we present generalizations of the e-backtesting procedure, in particular to risk measures which constitute Bayes pairs.
ER  -