We investigate a novel database of 10,217 extreme operational losses from the Italian bank UniCredit. Our goal is to shed light on the dependence between the severity distribution of these losses and a set of a set of macroeconomic, financial and firmspecific factors. To do so, we use Generalized Pareto regression techniques, where both the scale and shape parameters are assumed to be functions of these explanatory variables. We perform the selection of the relevant covariates with a state-of-the-art penalized-likelihood estimation procedure relying on L 1 -penalty terms. A simulation study indicates that this approach efficiently selects covariates of interest and tackles spurious regression issues encountered when dealing with integrated time series. Lastly, we illustrate the impact of different economic scenarios on the requested capital for operational risk. Our results have important implications in terms of risk management and regulatory policy.

For numerous applications, it is of interest to provide full probabilistic forecasts, which are able to assign plausibilities to each predicted outcome. Therefore, attention is shifting constantly from conditional mean models to probabilistic distributional models capturing location, scale, shape and other aspects of the response distribution. One of the most established models for distributional regression is the generalized additive model for location, scale and shape (GAMLSS). In high-dimensional data setups , classical fitting procedures for GAMLSS often become rather unstable and methods for variable selection are desirable. Therefore, a regularization approach for high-dimensional data setups in the framework of GAMLSS is proposed. It is designed for linear covariate effects and is based on L 1-type penalties. The following three penalization options are provided: the conventional least absolute shrinkage and selection operator (LASSO) for metric covariates, and both group and fused LASSO for categorical predictors. The methods are investigated both for simulated data and for two real data examples, namely Munich rent data and data on extreme operational losses from the Italian bank UniCredit.

Although hand grasping is ubiquitous in primate species, its origins remain uncertain. This is in part because uncertainty about hand skills and grasping strategies persists in strepsirrhines, a monophyletic group of primates located near the base of the primate tree. In this study, we report and discuss our observations of the different grasping strategies adopted by 85 captive individuals belonging to 22 species of strepsirrhines during the grasping of food items of different sizes and consistencies. Our results indicate that although strepsirrhines do not present variability in their hand-grip types (sole whole-hand power grip), they are able to adjust their grasping strategy depending on the properties of the food. Notably, they use the mouth when more precision is needed (i.e. to grasp small items). Moreover, grasping strategies adopted for big items differ depending on food consistency, revealing a new and potentially essential factor to consider in future research on grasping abilities. We believe that by looking across this important set of species in unconstrained standardized conditions, this study provides valuable insight for further comparative research on the potential selective pressures involved in the evolution of hand grasping.