E-backtesting

[2209.00991] E-backtesting Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantitative Finance > Risk Management arXiv:2209.00991 (q-fin) [Submitted on 27 Aug 2022 ( v1 ), last revised 15 Apr 2026 (this version, v6)] Title: E-backtesting Authors: Qiuqi Wang , Ruodu Wang , Johanna Ziegel View a PDF of the paper titled E-backtesting, by Qiuqi Wang and 2 other authors View PDF HTML (experimental) Abstract: 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. Subjects: Risk Management (q-fin.RM) ; Statistics Theory (math.ST); Methodology (stat.ME) Cite as: arXiv:2209.00991 [q-fin.RM] (or arXiv:2209.00991v6 [q-fin.RM] for this version) https://doi.org/10.48550/arXiv.2209.00991 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Qiuqi Wang [ view email ] [v1] Sat, 27 Aug 2022 03:29:13 UTC (2,715 KB) [v2] Sun, 2 Apr 2023 19:40:22 UTC (1,758 KB) [v3] Tue, 23 May 2023 14:53:24 UTC (383 KB) [v4] Mon, 12 Aug 2024 12:56:42 UTC (1,531 KB) [v5] Sun, 15 Dec 2024 01:14:59 UTC (1,479 KB) [v6] Wed, 15 Apr 2026 02:17:54 UTC (429 KB) Full-text links: Access Paper: View a PDF of the paper titled E-backtesting, by Qiuqi Wang and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: q-fin.RM < prev | next > new | recent | 2022-09 Change to browse by: math math.ST q-fin stat stat.ME stat.TH References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer ( What is the Explorer? ) Connected Papers Toggle Connected Papers ( What is Connected Papers? ) Litmaps Toggle Litmaps ( What is Litmaps? ) scite.ai Toggle scite Smart Citations ( What are Smart Citations? ) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv ( What is alphaXiv? ) Links to Code Toggle CatalyzeX Code Finder for Papers ( What is CatalyzeX? ) DagsHub Toggle DagsHub ( What is DagsHub? ) GotitPub Toggle Gotit.pub ( What is GotitPub? ) Huggingface Toggle Hugging Face ( What is Huggingface? ) ScienceCast Toggle ScienceCast ( What is ScienceCast? ) Demos Demos Replicate Toggle Replicate ( What is Replicate? ) Spaces Toggle Hugging Face Spaces ( What is Spaces? ) Spaces Toggle TXYZ.AI ( What is TXYZ.AI? ) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower ( What are Influence Flowers? ) Core recommender toggle CORE Recommender ( What is CORE? ) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs . Which authors of this paper are endorsers? | Disable MathJax ( What is MathJax? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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