correlation-analysis

To test for cointegration, apply Engle-Granger and Johansen tests to evaluate whether asset price pairs share a long-term equilibrium. These methods identify stable relationships suitable for generating mean-reverting spreads and actionable pair-trading signals.

Correlation measures co-movement using metrics like Pearson, Spearman, or Kendall, while cointegration evaluates long-term equilibrium relationships. Cointegration reveals deeper structural links across multi-asset portfolios for hedging and diversification beyond simple price correlation.

To calculate dynamic hedge ratios, apply a Kalman filter to adjust the relationship between asset pairs over time. This generates Z-score based pair-trading signals with mean-reverting spreads that adapt to changing market conditions.

Yes, you can cluster assets by computing correlation metrics to reveal underlying sector structures. This clustering identifies diversification opportunities by grouping assets with similar co-movement patterns across multi-asset portfolios.

The best way to generate Z-score based pair-trading signals is to combine cointegration tests with half-life estimation and optional Kalman dynamic hedge ratios. This approach identifies mean-reverting spreads to support actionable entries and exits.

Use Johansen cointegration tests for multi-asset portfolios to evaluate multiple cointegrating vectors simultaneously. Engle-Granger is better suited for simple two-asset pair trading where you only need to test a single long-term equilibrium relationship.