kalman-filters

Dynamic beta estimation uses Kalman filters to compute time-varying regression coefficients in a state-space formulation. You define state transition and observation matrices to update beta estimates online as new price data arrives.

Yes, Kalman filters calculate adaptive hedge ratios in pairs trading by continuously updating the relationship between asset prices. The state-space framework adjusts the hedge ratio in real-time as market conditions evolve.

Online estimation requires defining four state-space matrices: F for state transition, H for observation, Q for process noise covariance, and R for measurement noise covariance. These parameters drive the filter's updating behavior.

Kalman filtering provides adaptive state-space estimates that adjust dynamically to changing relationships, unlike rolling regression which uses fixed window weights. This approach yields smoother signal stability for latent factor tracking.

Kalman filtering supports both backtesting and live deployment through online updating for real-time processing and optional smoothing for historical data analysis. This allows testing strategies on historical sequences before live execution.

A random-walk state formulation allows the Kalman filter to track evolving trends without assuming a fixed trajectory. This baseline state transition adapts naturally to shifting financial parameters and latent factors.