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Underdetermination and variability of the results in macro-to-micro stress tests: A machine learning approach

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We investigate the impact of the uncertainties surrounding the modelling process when conducting a stress test. These uncertainties are due to several choices left to the modeller with regards to, among others, the variables to select, the data samples used for the calibration of the different models and how these models are combined together. We run tests to quantify the impact of these sources of uncertainty by using as an example the Federal Reserve System’s Comprehensive Capital Analysis and Review (FED CCAR) 2016 scenario. We conclude that the impact could be non-negligible as it adds substantial variability to the final results. We employ Probabilistic Graphical Models — a machine learning technique — to corroborate our findings.
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Keywords: stress testing, scenario analysis, probabilistic graphical models, visualisation, model risk, machine learning

Document Type: Research Article

Publication date: March 1, 2017

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  • Journal of Risk Management in Financial Institutions is the essential professional and research journal for all those involved in the management of risk at retail and investment banks, investment managers, broker-dealers, hedge funds, exchanges, central banks, financial regulators and depositories, as well as service providers, advisers, researchers and academics. Guided by expert Editors and an eminent Editorial Board, each quarterly 100-page issue does not publish advertising but rather in-depth articles, reviews and applied research by leading professionals and researchers in the field on six key inter-related areas: strategic and business risk, financial risk, including traditional/exotic credit, market and liquidity risks, operational risk, regulatory and legal risks, systemic risk, and sovereign risk.

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