Risk allocation with Shapley value in the risk aggregation framework

2021 ◽  
Vol 16 (2) ◽  
pp. 4-10
Author(s):  
Antonio Lugoboni ◽  
◽  
Nicola Picchiotti ◽  
Andrea Spuntarelli ◽  
◽  
...  

The topic of risk aggregation arises from the need to incorporate in a single measure the overall exposure to the different risk types. In general, the methodologies adopted for the purposes of risk integration are based on the principle that the overall economic capital is lower than the simple algebraic sum of economic capital measures related to individual risks. This phenomenon, due to the existence of an imperfect correlation between the risks, determines, in line with portfolio theory, a "diversification benefit". The issue of risk allocation subsequently arises when the risk value of the diversified aggregated loss needs to be reassigned to the different risk classes. A similar issue has been solved in the framework of cooperative Game Theory, where the Shapley value provides a player-specific contribution of the total surplus generated by the coalition. The paper proposes a novel application of the Shapley formula in the ICAAP context (Pillar II - economic view). In particular, we show that the Shapley value is the unique solution to the allocation problem of an overall risk value, granting the fundamental requested properties, including the efficiency one. An exemplificative model application is reported, as well as a comparison with a benchmark methodology. The experimental part shows the advantages of the novel approach in terms of precision and reliability of the estimates. Finally, it is important to mention that the presented framework can be applied also in other contexts such as, for instance, in the risk class attribution of the operational risk.

2020 ◽  
Vol 21 (1) ◽  
Author(s):  
Min Woo Sun ◽  
Stefano Moretti ◽  
Kelley M. Paskov ◽  
Nate T. Stockham ◽  
Maya Varma ◽  
...  

2021 ◽  
Vol 50 (1) ◽  
pp. 78-85
Author(s):  
Ester Livshits ◽  
Leopoldo Bertossi ◽  
Benny Kimelfeld ◽  
Moshe Sebag

Database tuples can be seen as players in the game of jointly realizing the answer to a query. Some tuples may contribute more than others to the outcome, which can be a binary value in the case of a Boolean query, a number for a numerical aggregate query, and so on. To quantify the contributions of tuples, we use the Shapley value that was introduced in cooperative game theory and has found applications in a plethora of domains. Specifically, the Shapley value of an individual tuple quantifies its contribution to the query. We investigate the applicability of the Shapley value in this setting, as well as the computational aspects of its calculation in terms of complexity, algorithms, and approximation.


2016 ◽  
Vol 80 ◽  
pp. 21-24 ◽  
Author(s):  
Koji Yokote ◽  
Yukihiko Funaki ◽  
Yoshio Kamijo

Author(s):  
SILVIU GUIASU

A solution of n-person games is proposed, based on the minimum deviation from statistical equilibrium subject to the constraints imposed by the group rationality and individual rationality. The new solution is compared with the Shapley value and von Neumann-Morgenstern's core of the game in the context of the 15-person game of passing and defeating resolutions in the UN Security Council involving five permanent members and ten nonpermanent members. A coalition classification, based on the minimum ramification cost induced by the characteristic function of the game, is also presented.


2012 ◽  
Vol 7 (2) ◽  
pp. 169-180 ◽  
Author(s):  
Victor Ginsburgh ◽  
Israël Zang

AbstractWe suggest a new game-theory-based ranking method for wines, in which the Shapley Value of each wine is computed, and wines are ranked according to their Shapley Values. Judges should find it simpler to use, since they are not required to rank order or grade all the wines, but merely to choose the group of those that they find meritorious. Our ranking method is based on the set of reasonable axioms that determine the Shapley Value as the unique solution of an underlying cooperative game. Unlike in the general case, where computing the Shapley Value could be complex, here the Shapley Value and hence the final ranking, are straightforward to compute. (JEL Classification: C71, D71, D78)


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