Home / Ideas / Kalman-Filter Dynamic Hedge-Ratio Pairs
Kalman-Filter Dynamic Hedge-Ratio Pairs
trade a state-space spread whose beta re-estimates every day
A static OLS/cointegration hedge ratio decays as two ETFs drift in factor loadings; a Kalman filter treats the hedge ratio as a hidden state and updates it online, producing a more stationary, tradable spread than rolling regression. This is a materially different signal-construction method from fixed-beta z-score cointegration.
Universe
A small fixed set of economically linked ETF pairs, e.g. GLD/GDX, EWA/EWC, IYR/VNQ, XLE/XOP, IEF/TLT — daily OHLCV only.
How it works
Kalman-filter regression of ETF A on ETF B yields a time-varying beta and measurement residual; enter when the standardized residual exceeds ±1.5 (dollar-neutral long cheap / short rich), exit at ±0.25 or on filter-implied mean-crossing, position sized inversely to residual volatility.
Expected performance
Research-derived Sharpe estimate: 0.4–0.9. GLD/GDX-style convergence largely died post-2018; edge is thinner and pair-dependent).
Backtest this idea with SignalChain
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/signalchain Kalman-filter time-varying hedge ratio pairs trade on linked ETF pairs, enter on standardized state-space residual beyond 1.5 sigma, dollar-neutral
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Research & sources
- quantstart.com — quantstart.com
- portfoliooptimizationbook.com — portfoliooptimizationbook.com
- letianzj.github.io — letianzj.github.io
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