Paper: arXiv 2402.08108

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper presents advanced mathematical optimization using convex-concave procedures and non-convex formulations (high math), while providing structured numerical experiments with backtest-ready metrics like profit and out-of-sample periods (moderate-high rigor).

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Find multi-asset statistical arbitrages"] --> B{"Data & Inputs"}
  B --> C["Asset Price Data<br>Lev. Constraint Bound<br>Band Width Parameters"]
  
  C --> D["Formulate NCCP<br>Maximize Volatility<br>Subject to Band & Lev."]
  
  D --> E["Sequential Convex<br>Optimization"]
  E --> F{"Convex-Concave<br>Procedure"}
  F -->|Converges| G["Optimal Portfolio<br>Moving-Band Arb. Strategy"]
  F -->|Iterate| E
  
  G --> H["Outcomes:<br>Generalizes pair trading to N assets<br>Efficient moving-band solver<br>Volatility scaling identified"]

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  2. Finding Moving-Band Statistical Arbitrages via Convex-Concave Optimization — → 2402.08108
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2 of 62 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.