Generalized FGM dependence: geometrical representation and convex bounds on sums

Hélène Cossette, Etienne Marceau, Alessandro Mutti and Patrizia Semeraro ()
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Hélène Cossette: Université Laval, École d’actuariat
Etienne Marceau: Université Laval, École d’actuariat
Alessandro Mutti: Politecnico di Torino, Department of Mathematical Sciences
Patrizia Semeraro: Politecnico di Torino, Department of Mathematical Sciences

Statistical Papers, 2025, vol. 66, issue 7, No 5, 40 pages

Abstract: Abstract Building on the one-to-one relationship between generalized FGM copulas and multivariate Bernoulli distributions, we prove that the class of multivariate distributions with generalized FGM copulas is a convex polytope. Therefore, we find sharp bounds in this class for many aggregate risk measures, such as value-at-risk, expected shortfall, and entropic risk measure, by enumerating their values on the extremal points of the convex polytope. This is infeasible in high dimensions. We overcome this limitation by considering the aggregation of identically distributed risks with generalized FGM copula specified by a common parameter p. In this case, the analogy with the geometrical structure of the class of Bernoulli distribution allows us to provide sharp analytical bounds for convex risk measures.

Keywords: Multivariate Bernoulli distributions; GFGM copulas; Huang-Kotz FGM copulas; Risk measures; Convex order (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s00362-025-01775-x

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