Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
Econometrics
2024-02-14 v1 Machine Learning
Portfolio Management
Abstract
We propose a new method for finding statistical arbitrages that can contain more assets than just the traditional pair. We formulate the problem as seeking a portfolio with the highest volatility, subject to its price remaining in a band and a leverage limit. This optimization problem is not convex, but can be approximately solved using the convex-concave procedure, a specific sequential convex programming method. We show how the method generalizes to finding moving-band statistical arbitrages, where the price band midpoint varies over time.
Keywords
Cite
@article{arxiv.2402.08108,
title = {Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization},
author = {Kasper Johansson and Thomas Schmelzer and Stephen Boyd},
journal= {arXiv preprint arXiv:2402.08108},
year = {2024}
}