On the solution uniqueness in portfolio optimization and risk analysis

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 Steiner point of the subdifferential set.

Individual and cooperative portfolio optimization as linear program

“…Hence, one can easily find the optimal value v * . However, this method does not return optimal y * in (5), which corresponds to the optimal portfolio weights. We next prove that y * can also be computed from a linear program.…”

“…Following [5], we call deviation measure D finitely generated if the corresponding risk envelope E is a convex hull of a finite number of points. For example, standard deviation is not finitely generated, while the mean absolute deviation and CVaR deviation are.…”

“…In this section, we study optimization problem (5) with ρ given by (7), where Q is defined in (10). The goal is to show that this problem can be reduced to linear programming.…”

“…Proof Vector y = y * μ y * satisfies the constraint μ y ≥ 1 in (5). Hence, by optimality of y * in (5…”

“…For every y ∈ R n with μ y > 0, vector y = y μ y satisfies the constraint μ y ≥ 1 in (5). Hence, by optimality of y * in (5), v * ≤ ρ(y ) = 1 μ y ρ(y), or v * μ y ≤ ρ(y).…”