An Alternating Direction Method of Multipliers for Utility-based Shortfall Risk Portfolio Optimization

TL;DR

This paper introduces a fast, scalable ADMM-based algorithm for utility-based shortfall risk portfolio optimization, addressing computational challenges with novel semismooth Newton methods and demonstrating significant speed improvements.

Contribution

It develops a novel ADMM framework combined with semismooth Newton algorithms for efficient UBSR optimization, especially in high-dimensional settings.

Findings

Substantial speedup over existing solvers.

Effective handling of nonlinear feasibility constraints.

Theoretical convergence guarantees for the proposed algorithms.

Abstract

Utility-based shortfall risk (UBSR), a convex risk measure sensitive to tail losses, has gained popularity in recent years. However, research on computational methods for UBSR optimization remains relatively scarce. In this paper, we propose a fast and scalable algorithm for the UBSR-based portfolio optimization problem. Leveraging the Sample Average Approximation (SAA) framework, we reformulate the problem as a block-separable convex program and solve it efficiently via the alternating direction method of multipliers (ADMM). In the high-dimensional setting, a key challenge arises in one of the subproblems -- a projection onto a nonlinear feasibility set defined by the shortfall-risk constraint. We propose two semismooth Newton algorithms to solve this projection subproblem. The first algorithm directly applies a semismooth Newton iteration to the Karush-Kuhn-Tucker (KKT) system of the projection problem. The second algorithm employs an implicit function transformation of semismooth functions to reduce the problem to a univariate equation involving the Lagrange multiplier and achieves global superlinear convergence with enhanced numerical stability under mild regularity conditions. Theoretical convergence guarantees of the proposed algorithms are established, and numerical experiments demonstrate a substantial speedup over state-of-the-art solvers, particularly in high-dimensional regimes.