correlation-analysis

You can validate long-run equilibria and hedge ratios using Engle-Granger and Johansen cointegration tests, which confirm whether two assets share a stable statistical relationship for pairs trading.

Correlation measures co-movement using Pearson, Spearman, or Kendall coefficients, while cointegration identifies long-run equilibria and hedge ratios to validate market-neutral trading relationships.

Rolling correlation diagnostics compute realized correlation over time, while Kalman filters estimate dynamic hedge ratios and half-life estimates to adapt spreads for risk management.

Yes, sector clustering and cross-market linkage analysis rely on scipy and statsmodels statistical libraries to process multi-asset universes, supporting diversification and hedging decisions across equities and sectors.

Co-movement discovery scans a target asset against a multi-asset universe to surface candidate pairs, followed by deep return-correlation analysis and regression-based beta to inform market-neutral strategies.

Correlation analysis alone is insufficient when assets lack cointegration; without long-run equilibria validated by Engle-Granger or Johansen tests, high return-correlation may produce unstable hedge ratios and trading signals.