My research interests lie at the intersection of probability, statistics, machine learning and risk theory. Extreme values, tail risk and small probability events are my daily bread.
An important field of application of my studies is quantitative risk management. For instance, I like to work with credit risk (PD, LGD, CVA, recovery processes), operational losses and dependence modeling. My approach combines tools from extreme value theory, inequality studies, distortion measures and machine learning.
Since my PhD studies, I have been investigating combinatorial stochastic processes, and urn models in particular, studying their properties, and applying them to very different fields, from fatigue analysis to risk management. Using urns, I have worked on alarm systems, as well as on alternative machine learning approaches.
Over the years I have also performed several empirical analyses, from the study of size distributions in industrial dynamics, to the tail risk of armed conflicts and the distribution of war casualties. More details can be found in my publications here below.
For my recent working papers, you can visit my SSRN page.
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