Predicting Banking Crises: A Horse Race using Dynamic-Recursive Forecasting

Mary Dawood, Nicholas Horsewood, Frank Strobel

Research output: Contribution to conference (unpublished)Paperpeer-review

Abstract

This study conducts a horse race of recently developed econometric methods that pre- dict banking crises and applies a more accurate dynamic-recursive forecasting technique to improve their predictive performance. Our results show that simple pooled models that account for regional heterogeneity and the entire crisis period outperform more complex models and the practice of dropping post-crisis periods. Finally, the dynamic signal ex- traction approach is recommended for policymakers who value avoiding banking crises at all costs, while the binomial logit model is more suitable for less conservative policymakers who consider the economic and social costs of false alarms.
Original languageEnglish
Number of pages35
Publication statusPublished - 2019
EventFinancial Management Association Annual Meeting 2019 - New Orleans, United States
Duration: 23 Oct 201926 Oct 2019
https://www.fma.org/past-programs

Conference

ConferenceFinancial Management Association Annual Meeting 2019
Abbreviated titleFMA Annual Meeting 2019
Country/TerritoryUnited States
CityNew Orleans
Period23/10/1926/10/19
Internet address

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