On the solution uniqueness in portfolio optimization and risk analysis

TL;DR

This paper investigates the non-uniqueness of solutions in portfolio optimization and risk analysis using deviation measures, highlighting implications and proposing a method to identify a unique subgradient for better decision-making.

Contribution

It demonstrates the general non-uniqueness in solution and inverse problems in portfolio optimization with deviation measures and introduces a methodology to select a unique subgradient.

Findings

Non-uniqueness is common in solution and inverse problems.

A methodology to identify a unique 'special' subgradient is proposed.

The 'special' subgradient is the Stainer point of the subdifferential set.

Abstract

We consider the issue of solution uniqueness for portfolio optimization problem and its inverse for asset returns with a finite number of possible scenarios. The risk is assessed by deviation measures introduced by [Rockafellar et al., Mathematical Programming, Ser. B, 108 (2006), pp. 515-540] instead of variance as in the Markowitz optimization problem. We prove that in general one can expect uniqueness neither in forward nor in inverse problems. We discuss consequences of that non-uniqueness for several problems in risk analysis and portfolio optimization, including capital allocation, risk sharing, cooperative investment, and the Black-Litterman methodology. In all cases, the issue with non-uniqueness is closely related to the fact that subgradient of a convex function is non-unique at the points of non-differentiability. We suggest methodology to resolve this issue by identifying a unique "special" subgradient satisfying some natural axioms. This "special" subgradient happens to be the Stainer point of the subdifferential set.