Urn models and other approaches to risk and tails, with applications in risk management and climatology

This dissertation collects three scientific contributions, already published in international peer-reviewed journals, plus some extra considerations and work-in-progress. First, we present a model based on reinforced urn processes, which conjugates to the right-censored recovery process, and empirically apply it to the time series of recovery...

An Urn-Based Nonparametric Modeling of the Dependence between PD and LGD with an Application to Mortgages

We propose an alternative approach to the modeling of the positive dependence between the probability of default and the loss given default in a portfolio of exposures, using a bivariate urn process. The model combines the power of Bayesian nonparametrics and statistical learning, allowing for the elicitation and the exploitation of experts’...

Stability-Based Model Selection in Non-Stationary Environment

Model selection is associated to model assessment, which is the problem of comparing different models, or model hyperparameters, for a particular learning task. It constitutes a fundamental step in building machine learning models. The central question is: How a model will work in the future? In this thesis, a new model selection scheme for...

Loss Given Default for residential real estate banks: Evidence from the Euro area

Loss given default (LGD) for residential real estate loans is affected by real estate market trends due to the impact on the value of debtors’ main collateral. Banks specialized in real estate lending are expected to be better at selecting lending opportunities, properly evaluating real estate collaterals, and managing the recovery process. The...