Effective Algorithms for Optimal Portfolio Deleveraging Problem with Cross Impact

TL;DR

This paper develops advanced algorithms to solve the complex, NP-hard optimal portfolio deleveraging problem considering cross-asset impacts, providing convergence guarantees and demonstrating effectiveness through numerical experiments.

Contribution

It introduces a successive convex optimization approach and a global algorithm combining relaxation and branch-and-bound for the OPD problem with cross impact.

Findings

The SCO algorithm converges to a KKT point.

The global algorithm finds solutions within a specified tolerance.

Numerical experiments validate the effectiveness of the proposed methods.

Abstract

We investigate the optimal portfolio deleveraging (OPD) problem with permanent and temporary price impacts, where the objective is to maximize equity while meeting a prescribed debt/equity requirement. We take the real situation with cross impact among different assets into consideration. The resulting problem is, however, a non-convex quadratic program with a quadratic constraint and a box constraint, which is known to be NP-hard. In this paper, we first develop a successive convex optimization (SCO) approach for solving the OPD problem and show that the SCO algorithm converges to a KKT point of its transformed problem. Second, we propose an effective global algorithm for the OPD problem, which integrates the SCO method, simple convex relaxation and a branch-and-bound framework, to identify a global optimal solution to the OPD problem within a pre-specified $\epsilon$-tolerance. We establish the global convergence of our algorithm and estimate its complexity. We also conduct numerical experiments to demonstrate the effectiveness of our proposed algorithms with both the real data and the randomly generated medium- and large-scale OPD problem instances.