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Kalman Filter (Finance)/Evidence
Method evidence record

Kalman Filter (Finance)

The Kalman filter is a recursive algorithm that estimates financial models with time-varying parameters, hidden factors, and noisy observations inside a dynamic state-space framework. The structural time series treatment was set out by Harvey (1989), with state-space and regime-switching extensions developed by Kim and Nelson (1999); it is widely applied to pairs trading, time-varying beta estimation, and yield-curve modelling.

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Source record

Citations copied verbatim from the method’s source record. No claim-level verification is inferred from them.

Kalman Filter — Financial State-Space Model
Taxonomic method record · regression-model / finance
  • Harvey, A. C. (1989). Forecasting, Structural Time Series Models and the Kalman Filter. Cambridge University Press. · ISBN 978-0521405737
  • Kim, C. J. & Nelson, C. R. (1999). State-Space Models with Regime Switching. MIT Press. · ISBN 978-0262112383
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Related methods

Generated from the method graph and shown as machine-suggested relations — no evidence claim is inferred.

Same method familyARIMAmachine-suggested · Relational suggestion, not evidence.Same method familyFactor Risk Modelmachine-suggested · Relational suggestion, not evidence.Same method familyLong-Memory Modelsmachine-suggested · Relational suggestion, not evidence.Same method familyPrincipal Component Risk Factorsmachine-suggested · Relational suggestion, not evidence.Same method familyStochastic Volatility Modelmachine-suggested · Relational suggestion, not evidence.

Evidence status

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Bibliographic sources are present. Claim-level evidence review has not been performed.

Sources

2 recorded citations, copied from the method source record.

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