Can We Predict the Next Capital Account Crisis?

This paper uses binary classification trees (BCTs) to predict capital account crises. BCTs successively compare candidate variables and thresholds to split the data into two subsamples, allowing for a large number of indicators to be considered and complex interactions to emerge in a way that standard regressions cannot easily replicate. We identify a robust leading indicator role for three variables (international reserves, current account balance, and shortterm external debt) as well as a reserve cover measure that combines them. External indebtedness and domestic GDP growth forecasts are also important predictors of vulnerability. Out of sample, we were able to capture some of the main emerging market crises with relatively few false alarms but the overall out-of-sample performance of our forecasts was mixed. Global cyclical variables help explain vulnerability to crises but they are difficult to predict and, therefore, are of limited use for forecasting purposes.