Quantile-Based Skewness for Fuzzy Numbers with Probabilistic Foundations: With an Application in Portfolio Optimization

TL;DR

This paper presents a new probabilistically grounded skewness coefficient for fuzzy numbers that improves computational efficiency and provides nuanced asymmetry insights, with practical application in portfolio optimization.

Contribution

It introduces a novel, parameter-free fuzzy skewness measure with a rigorous probabilistic foundation and demonstrates its scalability and utility in portfolio optimization.

Findings

The new coefficient is computationally more efficient, with nearly logarithmic processing time growth.

It offers a dual measure capturing intrinsic and overall asymmetry of fuzzy numbers.

Application in portfolio optimization shows improved performance over existing fuzzy skewness measures.

Abstract

This paper introduces a novel parameter free skewness coefficient for fuzzy numbers, addressing a critical gap in quantifying asymmetry under imprecision. Existing fuzzy literature substitutes membership functions for probability density functions in moment-based skewness, lacking rigorous theoretical grounding. Our coefficient, however, rigorously establishes a probabilistic foundation, making it both probabilistically meaningful and fully compliant with the semantics of fuzzy set theory. Our approach interprets a fuzzy number's left and right membership function components as cumulative and survival probability functions of associated random variables. This provides a robust probabilistic foundation for its $\alpha$-cuts as generalized quantiles representing values that are "at least $\alpha$-probable", thereby instantiating the well-grounded dualism between probability and possibility theory. As a quantile-based measure, the proposed coefficient offers invariance under scale and location transformations. Crucially, its superior computational efficiency, empirically demonstrating an almost logarithmic reduction in portfolio optimization processing time with increasing assets, enables significant scalability for real-world applications. The coefficient comprises two complementary constituents: an "inner" measure quantifying the intrinsic skewness of the underlying probabilistic distributions, and an "outer" measure capturing the fuzzy number's overall profile asymmetry. This dual structure offers nuanced insights into a fuzzy number's asymmetry. We demonstrate its practical utility and computational advantages within a fuzzy mean-variance-skewness portfolio optimization framework, comparing its performance with two of the most highly cited original moment-based fuzzy skewness coefficients from the literature.