Shrinkage estimators of the covariance matrix are known to improve the stability over time of the Global Minimum Variance Portfolio (GMVP), as they are less error-prone. However, the improvement over the empirical covariance matrix is not optimal for small values of n, the estimation sample...

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 new approach to date extreme financial cycles. Elaborating on recent methods in extreme value theory, it elaborates an extension of the famous calculus rule to detect extreme peaks and troughs. Applied on United-States stock market since 1871, it leads to a dating of these...

Traditionally, nancial crisis Early Warning Systems (EWSs) rely on macroeconomic leading indicators to forecast the occurrence of such events. This paper extends such discrete-choice EWSs by taking into account the persistence of the crisis phenomenon. The dynamic logit EWS is estimated using an...

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...

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...

In this paper we propose a multivariate dynamic probit model. Our model can be considered as a non-linear 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...