The central question of this paper is how to enhance supervised learning algorithms with fairness requirement ensuring that any sensitive input does not "`unfairly"' influence the outcome of the learning algorithm. To attain this objective we proceed by three steps. First after introducing...

We define several concepts of dependence between default risk and recovery risk, in a factor model framework. These concepts are illustrated and compared from the perspective of structural models: Merton (1974)'s single horizon and single firm model, multi-factor extensions, possibly under a...

We derive tests of stationarity for univariate time series by combining change‐point tests sensitive to changes in the contemporary distribution with tests sensitive to changes in the serial dependence. The proposed approach relies on a general procedure for combining dependent tests based on...