 Answering the Queen: Machine Learning and Financial CrisesJeremy Fouliard, Michael Howell, Helene Rey and Vania Stavrakeva No 15618, CEPR Discussion Papers from C.E.P.R. Discussion Papers Abstract: Financial crises cause economic, social and political havoc. Macroprudential policies are gaining traction but are still severely under-researched compared to monetary 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 aggregation and is “meta-statistical†, since we can incorporate any predictive model of crises in our analysis and test its ability to add information, without making any assumption on the data generating process. We predict systemic financial crises twelve quarters ahead out-of-sample with high signal-to-noise ratio. Our approach guarantees that picking certain time dependent sets of weights will be asymptotically similar for out-of-sample forecasts to the best ex post combination of models; it also guarantees that we outperform any individual forecasting model asymptotically. We analyse which models provide the most information for our predictions at each point in time and for each country, providing some insights into economic mechanisms underlying the buildup of risk in economies. Date: 2022-01 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:cpr:ceprdp:15618 Ordering information: This working paper can be ordered from Access Statistics for this paper More papers in CEPR Discussion Papers from C.E.P.R. Discussion Papers Centre for Economic Policy Research, 33 Great Sutton Street, London EC1V 0DX. |
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