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Answering the Queen: Machine Learning and Financial Crises

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  • Rey, Hélène
  • FOULIARD, Jeremy
  • Howell, Michael

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.

Suggested Citation

  • Rey, Hélène & FOULIARD, Jeremy & Howell, Michael, 2022. "Answering the Queen: Machine Learning and Financial Crises," CEPR Discussion Papers 15618, C.E.P.R. Discussion Papers.
  • Handle: RePEc:cpr:ceprdp:15618
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    Cited by:

    1. Coimbra, Nuno & Kim, Daisoon & Rey, Hélène, 2022. "Central Bank Policy and the concentration of risk: Empirical estimates," Journal of Monetary Economics, Elsevier, vol. 125(C), pages 182-198.
    2. Nina Boyarchenko & Giovanni Favara & Moritz Schularick, 2022. "Financial Stability Considerations for Monetary Policy: Empirical Evidence and Challenges," Staff Reports 1003, Federal Reserve Bank of New York.
    3. Daniel Stempel & Johannes Zahner, 2022. "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics 202232, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
    4. Fernandez-Gallardo, Alvaro, 2023. "Preventing financial disasters: Macroprudential policy and financial crises," European Economic Review, Elsevier, vol. 151(C).
    5. Casabianca, Elizabeth Jane & Catalano, Michele & Forni, Lorenzo & Giarda, Elena & Passeri, Simone, 2022. "A machine learning approach to rank the determinants of banking crises over time and across countries," Journal of International Money and Finance, Elsevier, vol. 129(C).
    6. Bluwstein, Kristina & Buckmann, Marcus & Joseph, Andreas & Kapadia, Sujit & Şimşek, Özgür, 2023. "Credit growth, the yield curve and financial crisis prediction: Evidence from a machine learning approach," Journal of International Economics, Elsevier, vol. 145(C).
    7. Lanbiao Liu & Chen Chen & Bo Wang, 2022. "Predicting financial crises with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 871-910, August.
    8. Sebastian Edwards, 2021. "Macroprudential Policies and The Covid-19 Pandemic: Risks and Challenges For Emerging Markets," NBER Working Papers 29441, National Bureau of Economic Research, Inc.
    9. Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
    10. Yang Liu & Qingguo Zeng & Bobo Li & Lili Ma & Joaquín Ordieres‐Meré, 2022. "Anticipating financial distress of high‐tech startups in the European Union: A machine learning approach for imbalanced samples," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(6), pages 1131-1155, September.
    11. Dichtl, Hubert & Drobetz, Wolfgang & Otto, Tizian, 2023. "Forecasting Stock Market Crashes via Machine Learning," Journal of Financial Stability, Elsevier, vol. 65(C).
    12. Sonya Georgieva, 2023. "Application of Artificial Intelligence and Machine Learning in the Conduct of Monetary Policy by Central Banks," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 8, pages 177-199.

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    JEL classification:

    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G01 - Financial Economics - - General - - - Financial Crises

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