Convex Risk Measures for the Aggregation of Multiple Information Sources and Applications in Insurance

Abstract

We propose a novel class of convex risk measures, based on the concept of the Fréchet mean, designed in order to handle uncertainty which arises from multiple information sources regarding the risk factors of interest. The proposed risk measures robustly characterize the exposure of the firm, by filtering out appropriately the partial information available in individual sources into an aggregate model for the risk factors of interest. Importantly, the proposed risks can be expressed in closed analytic forms allowing for interesting qualitative interpretations as well as comparative statics and thus facilitate their use in the everyday risk management process of the insurance firms. The potential use of the proposed risk measures in insurance is illustrated by two concrete applications, capital risk allocation and premia calculation under uncertainty.

32 pages

References in corpus (2)

• Pricing, Hedging and Optimally Designing Derivatives Via Minimization of Risk Measures
• Analysis of nonlinear modes of variation for functional data

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