Kasper Johansson, Thomas Schmelzer, Stephen Boyd
arXiv 12 Feb 2024 · Econometrics · publishedOptimization and Engineering (2024) · 1 citations (OpenAlex)
arXiv:2402.08108 · PDF · DOI · OpenAlex · Extracted main text
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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The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | J. Bollinger (1992) Using bollinger bands | 0.811 | 4 | 2 | 100% |
| 2 | J. Bollinger (2002) Bollinger on Bollinger bands | 0.737 | 3 | 2 | 100% |
| 3 | E. Gatev, W. Goetzmann, and G. Rouwenhorst (2006) Pairs trading: Performance of a relative-value arbitrage rule | 0.693 | 5 | 1 | 100% |
| 4 | C. Krauss (2017) Statistical arbitrage pairs trading strategies: Review and outlook | 0.644 | 4 | 1 | 100% |
| 5 | T. Lipp and S. Boyd (2016) Variations and extension of the convex–concave procedure | 0.644 | 2 | 2 | 100% |
| 6 | X. Shen, S. Diamond, Y. Gu, and S. Boyd (2016) Disciplined convex-concave programming | 0.644 | 2 | 2 | 100% |
| 7 | C. Eduardo De Moura, A. Pizzinga, and J. Zubelli (2016) A pairs trading strategy based on linear state space models and the Kalman filter | 0.585 | 3 | 1 | 100% |
| 8 | C. Krauss, X. Do, and N. Huck (2017) Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500 | 0.585 | 3 | 1 | 100% |
| 9 | S. Sarmento and N. Horta (2020) A Machine Learning Based Pairs Trading Investment Strategy | 0.511 | 2 | 1 | 100% |
| 10 | M. Avellaneda and J. Lee (2010) Statistical arbitrage in the US equities market | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 50 scored citations.