This paper proposes an original and unified toolbox to evaluate financial crisis early-warning systems (EWS). It presents four main advantages. First, it is a model free method which can be used to assess the forecasts issued from different EWS (probit, logit, Markov switching models, or...

Traditionally, financial crisis Early Warning Systems (EWSs) have relied on macroeconomic leading indicators when forecasting the occurrence of such events. This paper extends such discrete-choice EWSs by taking the persistence of the crisis phenomenon into account. The dynamic logit EWS is...

In this article we propose a multivariate dynamic probit model. Our model can be viewed as a nonlinear VAR model for the latent variables associated with correlated binary time-series data. To estimate it, we implement an exact maximum likelihood approach, hence providing a solution to the...

This paper proposes a very simple test of Granger (1969) non-causality for heterogeneous panel data models. Our test statistic is based on the individual Wald statistics of Granger non causality averaged across the cross-section units. First, this statistic is shown to converge sequentially to a...

This paper proposes a new duration-based backtesting procedure for value-at-risk (VaR) forecasts. The GMM test framework proposed by Bontemps (2006) to test for the distributional assumption (i.e., the geometric distribution) is applied to the case of the VaR forecasts validity. Using simple...

Cet article élabore une nouvelle méthodologie de datation des cycles financiers extrêmes. S?appuyant sur la théorie des valeurs extrêmes, il étend la « calculus rule » afin de détecter les pics et les creux exceptionnels. Appliquée sur des séries financières américaines...

This paper proposes a component approach to systemic risk which allows to decompose the risk of the aggregate financial system (measured by Expected Shortfall) while accounting for the firm characteristics. Developed by analogy with the Component Value-at-Risk concept, our new systemic risk...