In the first part we consider a dynamical model for the number of defaults of a pool of names. The model is based on the notion of generalized Poisson process, allowing for more than one default in small time intervals, contrary to many alternative approaches to loss modeling. We illustrate how...

In measuring its Operational Risk VaR, a bank needs to pay attention when including external data in its analysis. Without careful consideration of the specific nature of the bank's risk there can be relevant systemic risk implications as pointed out in Torresetti and Nordio (2014). Based on...

Identifying the Maximum Domain of Attraction (MDA) of a given (severity) distribution in Operational Risk is not an easy task with a significant impact on the Value at Risk (VaR). One could resort to the result of Pickands (1975) and select a suitably high threshold to model the excesses so that...

Real operational loss data exhibit in some cases power laws on a wide part of the tail distributions, with sharp deviations far on the right suggesting they decrease to zero faster at infinity. Taking into account such deviations when modelling operational risk leads to great differences in VaR...

The strengthening of capital requirements has induced banks and traders to consider charging a so called capital valuation adjustment (KVA) to the clients in OTC transactions. This roughly corresponds to charge the clients ex-ante the profit requirement that is asked to the trading desk. In the...

We study conditions for existence, uniqueness and invariance of the comprehensive nonlinear valuation equations first introduced in Pallavicini et al (2011). These equations take the form of semi-linear PDEs and Forward-Backward Stochastic Differential Equations (FBSDEs). After summarizing the...