Towards a New Early Warning System of Financial Crises

Abstract

This paper develops a new Early Warning System (EWS) model for predicting financial crises, based on a multinomial logit model. It is shown that EWS approaches based on binomial discrete-dependent-variable models can be subject to what we call a post-crisis bias. This bias arises when no distinction is made between tranquil periods and crisis/post-crisis periods. We show that applying a multinomial logit model is a valid way of solving this problem and constitutes a substantial improvement in the ability to forecast financial crises. The empirical results reveal that the model would have correctly predicted a large majority of crises in emerging markets since the 1990s. Moreover, we derive general results about the optimal design of EWS models, which allows policy-makers to make an optimal choice based on their degree of risk-aversion against unanticipated financial crises.