Answering the Queen: Machine Learning and Financial Crises / Jeremy Fouliard, Michael Howell, Hélène Rey.

Format:

Book

Author/Creator:

Fouliard, Jeremy.

Contributor:

National Bureau of Economic Research.

Howell, Michael.

Rey, Hélène.

Series:

Working Paper Series (National Bureau of Economic Research) no. w28302.

NBER working paper series no. w28302

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2020.

Summary:

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, allowing us to gain some insights into economic mechanisms underlying the building of risk in economies.

Notes:

Print version record

December 2020.