A Review of Standard Spectral Risk Measures

Author(s):  
Mohammed Berkhouch ◽  
Ghizlane Lakhnati

Spectral risk measures are defined as the most attractive subclass of coherent quantile-based risk measures, with a remarkable aptitude for concretizing the decision-maker's subjective attitude toward risk. This chapter raises the problem of underrepresentation of the subclass of spectral risk measures by reviewing the standard spectral risk measures proposed in the literature. In parallel, a discussion about the approaches behind the conception of these risk measures is held. Through this discussion, the authors spot a number of problems with each of these proposals that stand against the reliable applicability of these risk measures in practice.

Author(s):  
Nicole Bäuerle ◽  
Alexander Glauner

AbstractWe study the minimization of a spectral risk measure of the total discounted cost generated by a Markov Decision Process (MDP) over a finite or infinite planning horizon. The MDP is assumed to have Borel state and action spaces and the cost function may be unbounded above. The optimization problem is split into two minimization problems using an infimum representation for spectral risk measures. We show that the inner minimization problem can be solved as an ordinary MDP on an extended state space and give sufficient conditions under which an optimal policy exists. Regarding the infinite dimensional outer minimization problem, we prove the existence of a solution and derive an algorithm for its numerical approximation. Our results include the findings in Bäuerle and Ott (Math Methods Oper Res 74(3):361–379, 2011) in the special case that the risk measure is Expected Shortfall. As an application, we present a dynamic extension of the classical static optimal reinsurance problem, where an insurance company minimizes its cost of capital.


2017 ◽  
Vol 24 (4) ◽  
pp. 29-45
Author(s):  
Ho Hong Hai ◽  
Nguyen Thi Hoa

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