Working Paper: CEPR ID: DP15618
Authors: Jeremy Fouliard; Michael Howell; Hélène Rey; Vania Stavrakeva
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.
Keywords: No keywords provided
JEL Codes: No JEL codes provided
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| online machine learning framework (C45) | predict systemic financial crises (G01) |
| aggregating various predictive models (C52) | predict systemic financial crises (G01) |
| time-dependent weights for model aggregation (C22) | improve crisis prediction accuracy (H12) |
| predictive performance metrics (C52) | capture dynamics of pre-crisis periods (E32) |