correlation-analysis

To calculate dynamic hedge ratios for statistical arbitrage, apply a Kalman filter to the price series of your asset pairs. This method continuously updates the hedge ratio based on new market data, optimizing Z-score entry and exit signals for temporary price divergences.

Regime-conditional correlation analyzes asset relationships by segmenting market data into different volatility or trend states. This technique identifies how cross-market linkages shift during specific market regimes, providing deeper context for risk-managed portfolio construction and quantitative signal generation.

Yes, you can use pandas and statsmodels to estimate mean-reversion half-life by analyzing the Ornstein-Uhlenbeck process of the spread. This calculation determines the expected time for a cointegrated pair's price divergence to revert to its mean, guiding statistical arbitrage workflows.

Yes, quantitative correlation analysis works for cross-market linkages by evaluating co-movements across different financial assets. It performs multi-asset clustering and regime-conditional correlation to identify stable statistical relationships, satisfying requirements for quantitative signal generation and risk management.