Maximal signed volume for (multivariate) supermodular quasi-copulas
Matja\v{z} Omladi\v{c}, Martin Vuk, and Alja\v{z} Zalar

TL;DR
This paper introduces a new measure based on maximal negative volumes to better understand and position supermodular quasi-copulas in multivariate dependence modeling, extending previous concepts like ARV.
Contribution
It proposes an alternative, extendable method to the Average Rectangular Volume for multivariate supermodular quasi-copulas using maximal negative volumes, addressing a long-standing open problem.
Findings
The new measure effectively characterizes supermodular quasi-copulas in higher dimensions.
It extends the concept of volume measures from copulas to quasi-copulas.
The method helps practitioners select appropriate quasi-copulas for complex dependence structures.
Abstract
Copulas are the primary tool for dependence modeling in statistics, and quasi-copulas are their essential companions. The latter appear, say, as infima or suprema of sets of copulas; they form a huge class and have some unpleasant properties. Their statistical interpretation is challenged by the fact that they may lead to negative volumes of some boxes. So, numerous applications call for an intermediate class, and supermodular quasi-copulas are one of them, having many useful properties. An excellent measure, Average Rectangular Volume (ARV in short), to clarify and position this class was proposed in the seminal paper by Anzilli and Durante, The average rectangular volume induced by supermodular aggregation functions, J. Math. Anal. Appl. 555 (2026) 21 pp. While supermodularity is a bivariate notion, its extension to the -variate case for was recently emphasized in a key paper…
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Taxonomy
TopicsRisk and Portfolio Optimization · Financial Risk and Volatility Modeling · Stochastic processes and financial applications
