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...

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...

We introduce a smooth transition Generalized Pareto (GP) regression model to study the link between extreme losses and the economic context. The advantage of our approach consists in specifying a time-varying dependence structure between financial factors and the severity distribution of the...

We propose a dynamic measure of extremal connectedness across investment styles of hedge funds. Using multivariate extreme value regression techniques, we estimate this measure conditional on factors reflecting the economic uncertainty and the state of the financial markets, and derive several...