Expected shortfall reliability—added value of traditional statistics and advanced artificial intelligence for market risk measurement purposes
EntityUAM. Departamento de Matemáticas
10.3390/math9172142Mathematics 9.17 (2021): 2142
SubjectsFRTB; GAN; SMOTE; Expected Shortfall; EM-Fittings; Market Risk; Matemáticas
Rights© 2021 by the authors. Licensee MDPI, Basel, Switzerland
Esta obra está bajo una Licencia Creative Commons Atribución 4.0 Internacional.
The Fundamental Review of the Trading Book is a market risk measurement and management regulation recently issued by the Basel Committee. This reform, often referred to as “Basel IV”, intends to strengthen the financial system. The newest capital standard relies on the use of the Expected Shortfall. This risk measure requires to get sufficient information in the tails to ensure its reliability, as this one has to be alimented by a sufficient quantity of relevant data (above the 97.5 percentile in the case of the regulation or interest). In this paper, after discussing the relevant features of Expected Shortfall for risk measurement purposes, we present and compare several methods allowing to ensure the reliability of the risk measure by generating information in the tails. We discuss these approaches with respect to their relevance considering the underlying situation when it comes to available data, allowing practitioners to select the most appropriate approach. We apply traditional statistical methodologies, for instance distribution fitting, kernel density estima-tion, Gaussian mixtures and conditional fitting by Expectation-Maximisation as well as AI related strategies, for instance a Synthetic Minority Over-sampling Technique implemented in a regression environment and Generative Adversarial Nets
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