Quantum Annealing Algorithm for Expected Shortfall based Dynamic Asset Allocation

TL;DR

This paper introduces a quantum annealing algorithm in QUBO form for dynamic asset allocation that optimizes expected shortfall risk, aiming to improve computational efficiency over classical methods.

Contribution

It presents a novel quantum annealing approach for expected shortfall constrained asset allocation, avoiding complex statistical models and aligning risk targets with market volatility.

Findings

Algorithm formulated in QUBO suitable for quantum annealers

Potential for faster solutions with quantum systems compared to classical algorithms

Addresses practical needs of financial practitioners for tail risk management

Abstract

The 2008 mortgage crisis is an example of an extreme event. Extreme value theory tries to estimate such tail risks. Modern finance practitioners prefer Expected Shortfall based risk metrics (which capture tail risk) over traditional approaches like volatility or even Value-at-Risk. This paper provides a quantum annealing algorithm in QUBO form for a dynamic asset allocation problem using expected shortfall constraint. It was motivated by the need to refine the current quantum algorithms for Markowitz type problems which are academically interesting but not useful for practitioners. The algorithm is dynamic and the risk target emerges naturally from the market volatility. Moreover, it avoids complicated statistics like generalized pareto distribution. It translates the problem into qubit form suitable for implementation by a quantum annealer like D-Wave. Such QUBO algorithms are expected to be solved faster using quantum annealing systems than any classical algorithm using classical computer (but yet to be demonstrated at scale).