DP15618 Answering the Queen: Machine Learning and Financial Crises

Author(s): Jeremy FOULIARD, Michael J. Howell, Hélène Rey
Publication Date: December 2020
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Programme Areas: Financial Economics, International Macroeconomics and Finance
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=15618

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.