Numerical computation of convex risk measures

Papayiannis, Georgios I.; Yannacopoulos, A. N.

doi:10.1007/s10479-016-2284-3

Cited by 4 publications

(1 citation statement)

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“…Apart from its use in information theory it has played a crucial role in the study of model uncertainty in economic theory (see e.g. Hansen and Sargent (2008)) and has also been used in the study of risk management through the definition of the so called entropic risk measures for the case of single priors (see Föllmer and Knispel (2011); see also Papayiannis and Yannacopoulos (2016b)). On account of the popularity of entropy and in order to generalize the entropic risk measures first proposed in Föllmer and Knispel (2011) for the single prior case to the multi-prior case we extend our definition of Fréchet risk measures to include pseudo-metrics rather than just metrics, and choose d to be the Kullback-Leibler divergence (KL).…”

Section: 3mentioning

confidence: 99%

Convex risk measures for the aggregation of multiple information sources and applications in insurance

Papayiannis

Yannacopoulos

2018

Scandinavian Actuarial Journal

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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.

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Section: 3mentioning

confidence: 99%