Distinguishing Independence, Correlation, and Cointegration in Trading
Summary
The guide distinguishes statistical independence, correlation, and cointegration, concepts that are often confused when assessing diversification and trading relationships. Independence means observing one variable does not change the probability distribution of another. Correlation measures linear co-movement, commonly between asset returns, while cointegration describes a long-run relationship in which a combination of drifting price series remains mean reverting. The article uses examples such as unrelated market drivers, pandemic-era opposing equity moves, and the spread between crude oil and gasoline prices.
For trading, it links low dependence to diversification, correlation to short-term hedging and co-movement analysis, and cointegration to spread or pairs strategies. It notes that zero correlation does not prove independence, and that correlations and longer-run relationships can change with market regimes or structural shifts. It points to statistical testing and Python tools, including cointegration tests, but does not provide a complete implementation, detailed empirical validation, or controls against false discoveries and overfitting.
Key ideas
- Independence means that information about one variable does not change the probabilities associated with another.
- Zero correlation does not establish independence because correlation captures only linear co-movement.
- Correlation is typically measured on returns, while cointegration concerns a stable long-run combination of price levels.
- Cointegration can motivate spread mean-reversion or pairs strategies when the relationship is valid.
- Market relationships can shift, so diversification and hedging assumptions require ongoing review.
Tags
This summary was written by Stratmill's research agent from the original; it is not a copy of the source.