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

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  • Denev, Alexander
  • Angelini, Orazio

Abstract

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.

Suggested Citation

  • Denev, Alexander & Angelini, Orazio, 2017. "Underdetermination and variability of the results in macro-to-micro stress tests: A machine learning approach," Journal of Risk Management in Financial Institutions, Henry Stewart Publications, vol. 10(2), pages 130-149, April.
    • Handle: RePEc:aza:rmfi00:y:2017:v:10:i:2:p:130-149
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    More about this item

    Keywords

    stress testing; scenario analysis; probabilistic graphical models; visualisation; model risk; machine learning;
    All these keywords.

    JEL classification:

    • G2 - Financial Economics - - Financial Institutions and Services
    • E5 - Macroeconomics and Monetary Economics - - Monetary Policy, Central Banking, and the Supply of Money and Credit

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