Towards A New Early Warning System of Financial Crises

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, when economic fundamentals are largely sound and sustainable, and crisis/post-crisis periods, when economic variables go through an adjustment process before reaching a more sustainable level or growth path. We show that applying a multinomial logit model, which allows distinguishing between more than two states, 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, for a set of 32 open emerging markets from 1993 till the present, the model would have correctly predicted a large majority of crises in emerging markets. 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.