Pairs Trading Methods: Cointegration, Copulas, PCA, and Sparse Portfolios
Summary
This lecture series surveys advanced pairs and statistical arbitrage methods. Topics include distance-based pair selection and dependence measures, cointegration with mean first-passage time for choosing trading boundaries, PCA strategies, machine learning for selecting pairs or modeling spreads, sparse mean-reverting portfolios, copula and vine-copula signals, and optimal rules for Ornstein–Uhlenbeck spreads. The material describes the concepts and approaches rather than presenting one unified strategy.
The document reports a Sharpe ratio of 1.44 for the cited PCA study over 1997–2007, while noting that the approach has strengths, weaknesses, and variants. It also flags a key limitation of distance-based methods: detected dependencies may be spurious. Copula sections emphasize assumptions and common pitfalls, and several topics are presented as lecture coverage without detailed empirical results. The series is therefore a roadmap of methods, not evidence that any single technique will work reliably in live trading.
Key ideas
- Distance-based pair selection is simple and widely cited, but it can identify spurious relationships.
- Cointegration models mean-reverting price spreads and can support optimization of trade boundaries and frequency.
- PCA and copula methods offer alternative ways to construct statistical arbitrage signals.
- Sparse mean-reverting portfolios aim to reduce the number of traded assets and associated costs.
- Machine learning can be applied to pair selection and spread modeling, but the document gives no universal performance guarantee.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.