Danger Zones for Banking Crises in Emerging Markets

This paper employs a recently developed statistical algorithm in order to build an early warning model for banking crises inemerging markets. The procedure creates many ‘artificial’ samples by iteratively perturbing the original data set and estimatesmany models from these samples. The final model is constructed by aggregation, so that, by construction, it is flexible enough toaccommodate new data for out-of-sample prediction. Out of a large number (540) of candidate explanatory variables, rangingfrom macroeconomic variables to balance sheet indicators, our procedure selects a handful of indicators (and their combina-tions) that is sufficient to generate accurate out-of-sample predictions of banking crises. Using data covering emerging marketsfrom 1980 to 2010, the model identifies two banking crisis’‘danger-zones’, e.g. economic configurations that are conducive tocrises. The first occurs when high interest rates on bank deposits, possibly reflecting liquidity risks and solvency fears, interactwith credit-booms and capital fl ights; the second occurs when an investment boom is financed by a large rise in banks’ net for-eign exposure. We compare our model to models derived by standard econometric techniques, and find that our approachdelivers much better out-of-sample predictions. Copyright © 2016 John Wiley & Sons, Lt