The dependence structure of max-stable random vectors can be characterized by their Pickands dependence function. In many applications, the extremal dependence measure varies with covariates. We develop a flexible, semi-parametric method for the estimation of non-stationary multivariate Pickands...

We model the severity distribution of operational loss data, conditionally to some covariates. Indeed, previous studies suggest that this distribution might be influenced by firm-specific factors. We introduce a conditional Generalized Pareto model for the tail of the severity distribution,...

In this paper, we provide a novel way to estimate the out-of-sample predictive ability of a trading rule. Usually, this ability is estimated using a sample-splitting scheme, true out-of-sample data being rarely available. We argue that this method makes poor use of the available data and creates...

For numerous applications it is of interest to provide full probabilistic forecasts, which are able to assign probabilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape...

We study the link between the volatility of exchange rates and interest rate differentials (IRD), motivated by the importance of currency carry trade activities in exchange rate dynamics. We examine this link by means of an extended stochastic volatility model, for which we detail an efficient...

In this paper, we propose an unified econometric strategy to revisit the predictive contentof interest rates for exchange rate returns. The novelty of our approach is to take into account dependencies of higher orders by allowing for a time-varying asymmetry componentin the distribution of...