On Partial Stochastic Comparisons Based on Tail Values at Risk

Autor: Alfonso J. Bello, Julio Mulero, Miguel A. Sordo, Alfonso Suárez-Llorens
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: Mathematics, Vol 8, Iss 7, p 1181 (2020)
Druh dokumentu: article
ISSN: 2227-7390
DOI: 10.3390/math8071181
Popis: The tail value at risk at level p, with p ∈ ( 0 , 1 ) , is a risk measure that captures the tail risk of losses and asset return distributions beyond the p quantile. Given two distributions, it can be used to decide which is riskier. When the tail values at risk of both distributions agree, whenever the probability level p ∈ ( 0 , 1 ) , about which of them is riskier, then the distributions are ordered in terms of the increasing convex order. The price to pay for such a unanimous agreement is that it is possible that two distributions cannot be compared despite our intuition that one is less risky than the other. In this paper, we introduce a family of stochastic orders, indexed by confidence levels p 0 ∈ ( 0 , 1 ) , that require agreement of tail values at risk only for levels p > p 0 . We study its main properties and compare it with other families of stochastic orders that have been proposed in the literature to compare tail risks. We illustrate the results with a real data example.
Databáze: Directory of Open Access Journals
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