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Maximizing equity market sector predictability in a Bayesian time-varying parameter model

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  • Johnson, Lorne D.
  • Sakoulis, Georgios

Abstract

The Kalman filter methodology is employed to develop a dynamic sector allocation model for US equities. Bayesian parameter estimation and model selection criteria result in significantly improved sector return predictability over static or rolling parameter specifications. A simple trading strategy illustrates how widely tested financial and economic variables can be used as inputs in for a potentially profitable investment strategy.

Suggested Citation

  • Johnson, Lorne D. & Sakoulis, Georgios, 2008. "Maximizing equity market sector predictability in a Bayesian time-varying parameter model," Computational Statistics & Data Analysis, Elsevier, vol. 52(6), pages 3083-3106, February.
    • Handle: RePEc:eee:csdana:v:52:y:2008:i:6:p:3083-3106
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    2. Massimo Guidolin & Manuela Pedio, 2022. "Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns," Forecasting, MDPI, vol. 4(1), pages 1-32, February.

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