# E-backtesting

arXiv: https://arxiv.org/abs/2209.00991 · HTML: https://mlsanity.com/paper/2209.00991/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2022.

## Built on by (most cited first, 6 of 6)

- [E-Values Expand the Scope of Conformal Prediction](https://mlsanity.com/paper/2503.13050/) (2025)
- [Online LLM watermark detection via e-processes](https://mlsanity.com/paper/2602.14286/) (2026)
- [Multi-Armed Sequential Hypothesis Testing by Betting](https://mlsanity.com/paper/2603.17925/) (2026)
- [Adaptive Window Selection for Financial Risk Forecasting](https://mlsanity.com/paper/2603.01157/) (2026)
- [Bet on Features: Anytime-Valid and Feature-Aware Auditing of Conditional Quantile Forecasters](https://mlsanity.com/paper/2607.11653/) (2026)
- [Always-On Experimentation](https://mlsanity.com/paper/2609.38695/) (2026)

## Abstract (arXiv)

In the recent Basel Accords, the Expected Shortfall (ES) replaces the Value-at-Risk (VaR) as the standard risk measure for market risk in the banking sector, making it the most important risk measure in financial regulation. One of the most challenging tasks in risk modeling practice is to backtest ES forecasts provided by financial institutions. To design a model-free backtesting procedure for ES, we make use of the recently developed techniques of e-values and e-processes. Backtest e-statistics are introduced to formulate e-processes for risk measure forecasts, and unique forms of backtest e-statistics for VaR and ES are characterized using recent results on identification functions. For a given backtest e-statistic, a few criteria for optimally constructing the e-processes are studied. The proposed method can be naturally applied to many other risk measures and statistical quantities. We conduct extensive simulation studies and data analysis to illustrate the advantages of the model-free backtesting method, and compare it with the ones in the literature.