Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization

Author:

Kasper Johansson, Thomas Schmelzer, Stephen Boyd

Keyword:

Economics, Econometrics, Econometrics (econ.EM), Machine Learning (cs.LG), Portfolio Management (q-fin.PM)

journal:

date:

2024-02-12 00:00:00

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.

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