Insurance typically benefits risk-averse individuals by pooling finite-mean risks. However, with infinite-mean distributions (e.g., Pareto, Fréchet), risk sharing can backfire, creating a "nondiversification trap." This applies to highly skewed distributions like Cauchy or catastrophic risks with infinite losses. Open questions remain about these complex scenarios.
Operational risk modeling requires flexible distributions for non-negative values, particularly those exhibiting heavy-tail behavior. Composite or spliced models, like composite Tukey-type distributions, are gaining attention for their ability to handle extreme and ordinary observations effectively. This paper explores the flexibility of such distributions, offering empirical validation with operational risk data.
This paper proposes a #credit#portfolio approach for evaluating #systemicrisk and attributing it across #financialinstitutions. The proposed model can be estimated from high-frequency credit default swap (#cds) data and captures risks from publicly traded #banks, privately held institutions, and coöperative banks. The approach overcomes limitations of earlier studies by accounting for correlated losses between institutions and also offers a modeling extension to account for #fattails and #skewness of #assetreturns. The model is applied to a universe of banks in #europe, highlighting discrepancies between the #capitaldequacy of the largest contributors to systemic risk and less systemically important banks.