Shortfall Risk Through Fenchel Duality

2018 ◽  
Author(s):  
Zhenyu Cui ◽  
Jun Deng

2018 ◽  
Vol 05 (02) ◽  
pp. 1850019 ◽  
Author(s):  
Zhenyu Cui ◽  
Jun Deng

In this paper, we propose a Fenchel duality approach to study the minimization problem of the shortfall risk. We consider a general increasing and strictly convex loss function, which may be more general than the situation of convex risk measures usually assumed in the literature. We first translate the associated stochastic optimization problem to an equivalent static optimization problem, and then obtain the explicit structure of the optimal randomized test for both complete and incomplete markets. For the incomplete market case, to the best of our knowledge, we obtain for the first time the explicit randomized test, while previous literature only established the existence through the supermartingale optional decomposition approach. We also solve the shortfall risk minimization problem for an insider through the enlargement of filtrations approach.







2000 ◽  
Vol 4 (2) ◽  
pp. 117-146 ◽  
Author(s):  
Hans Föllmer ◽  
Peter Leukert




2022 ◽  
Author(s):  
Erick Delage ◽  
Shaoyan Guo ◽  
Huifu Xu

Utility-based shortfall risk measures effectively captures a decision maker's risk attitude on tail losses. In this paper, we consider a situation where the decision maker's risk attitude toward tail losses is ambiguous and introduce a robust version of shortfall risk, which mitigates the risk arising from such ambiguity. Specifically, we use some available partial information or subjective judgement to construct a set of plausible utility-based shortfall risk measures and define a so-called preference robust shortfall risk as through the worst risk that can be measured in this (ambiguity) set. We then apply the robust shortfall risk paradigm to optimal decision-making problems and demonstrate how the latter can be reformulated as tractable convex programs when the underlying exogenous uncertainty is discretely distributed.





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