correlation-analysis

To find cointegrated pairs for pairs trading, you can run Engle-Granger and Johansen cointegration tests across a broad asset universe. This identifies candidate pairs with long-run equilibrium relationships, enabling end-to-end pair-trading signal generation.

Return correlation analysis supports Pearson, Spearman, and Kendall correlation coefficients. It also provides rolling metrics and spread-based signals to quantify co-movement and discover highly correlated candidate assets for pairs trading strategies.

You can calculate a dynamic hedge ratio using the Kalman filter method to support robust hedging in pairs trading. This approach estimates half-life and adjusts the hedge ratio dynamically, complementing standard cointegration tests for Z-score signal generation.

Yes, the analysis supports sector clustering to reveal sector structure and diversification opportunities. It also performs realized correlation and cross-market linkage analysis to study dynamic relationships across different markets.

Correlation measures short-term co-movement between asset returns, while cointegration identifies a long-run equilibrium relationship between price series. Pairs trading requires cointegration tests like Engle-Granger and Johansen to ensure the spread is mean-reverting.

You should avoid relying solely on correlation for pairs trading when assets lack a long-run equilibrium, as high correlation does not guarantee mean-reverting spreads. Use cointegration tests and half-life estimation to validate robust hedging opportunities instead.