Outperforming a Benchmark with $\alpha$-Bregman Wasserstein divergence

TL;DR

This paper introduces the $oldsymbol{ extalpha}$-Bregman Wasserstein divergence for active portfolio management, enabling asymmetric penalization of gains and losses, and characterizes the optimal strategy with explicit conditions and a numerical example.

Contribution

It proposes a novel divergence measure, the $oldsymbol{ extalpha}$-Bregman Wasserstein divergence, for outperforming benchmarks in portfolio optimization, with theoretical analysis and explicit solution conditions.

Findings

Existence and uniqueness of the optimal portfolio strategy.

Explicit conditions for binding divergence and budget constraints.

Numerical illustration in a geometric Brownian motion market model.

Abstract

We consider the problem of active portfolio management, where an investor seeks the portfolio with maximal expected utility of the difference between the terminal wealth of their strategy and a proportion of the benchmark's, subject to a budget and a deviation constraint from the benchmark portfolio. As the investor aims at outperforming the benchmark, they choose a divergence that asymmetrically penalises gains and losses as well as penalises underperforming the benchmark more than outperforming it. This is achieved by the recently introduced $\alpha$-Bregman-Wasserstein divergence, subsuming the Bregman-Wasserstein and the popular Wasserstein divergence. We prove existence and uniqueness, characterise the optimal portfolio strategy, and give explicit conditions when the divergence constraints and the budget constraints are binding. We conclude with a numerical illustration of the optimal quantile function in a geometric Brownian motion market model.