Econometric Methods for Financial Crises.

Summary Known as Early Warning Systems (EWS), financial crisis prediction models are called upon to play a decisive role in the orientation of economic policies at both the microeconomic and macroeconomic levels. However, in the wake of the global financial crisis, major questions are being raised about their real predictive capacity. This applied econometrics thesis aims at proposing (i) a method for systematically evaluating the predictive capabilities of EWS and (ii) new EWS specifications to improve their performance. This work is divided into four chapters. The first one proposes an original test for evaluating predictions by confidence intervals based on the assumption of binomial distribution of the violation process. The second chapter proposes an econometric evaluation strategy of the predictive capabilities of EWS. We show that this evaluation should be based on the determination of an optimal threshold on the predicted probabilities of crisis occurrence as well as on the comparison of models.The third chapter reveals that the dynamics of crises (persistence) is an essential element of the econometric specification of EWS. The results show in particular that dynamic logit models have much better predictive capabilities than static models and Markovian models. Finally, in the fourth chapter we propose an original multivariate dynamic probit model that allows us to analyze the causality patterns between different types of crises (banking, exchange rate and debt). The empirical illustration clearly shows that the switch to trivariate modeling significantly improves the forecasts for countries experiencing all three types of crises.