Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization
This work addresses the need for more flexible statistical arbitrage strategies in finance, though it appears incremental as it builds on existing optimization techniques.
The authors tackled the problem of finding statistical arbitrages with more than two assets by formulating it as a portfolio optimization for highest volatility under price band and leverage constraints, and they developed a method using the convex-concave procedure to solve this non-convex problem, extending it to moving-band scenarios.
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.