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Answering the Queen: Machine learning and financial crises

Jérémy Fouliard, Michael Howell and Helene Rey ()

No 926, BIS Working Papers from Bank for International Settlements

Abstract: Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary policy and fiscal policy. We use the general framework of sequential predictions also called online machine learning to forecast crises out-of-sample. Our methodology is based on model averaging and is "meta-statistic" since we can incorporate any predictive model of crises in our set of experts and test its ability to add information. We are able to predict systemic financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio in most cases. We analyse which experts provide the most information for our predictions at each point in time and for each country, allowing us to gain some insights into economic mechanisms underlying the building of risk in economies.

JEL-codes: E37 E44 G01 (search for similar items in EconPapers)
Pages: 66 pages
Date: 2021-02
New Economics Papers: this item is included in nep-big, nep-cmp, nep-cwa, nep-fdg, nep-fmk and nep-mac
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Related works:
Working Paper: Answering the Queen: Machine Learning and Financial Crises (2020) Downloads
Working Paper: Answering the Queen: Machine Learning and Financial Crises (2020) Downloads
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