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Yield Curve Forecasting with the Burg Model

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  • Pierre Rostan
  • Rachid Belhachemi
  • François‐Eric Racicot

Abstract

We introduce a versatile and robust model that may help policymakers, bond portfolio managers and financial institutions to gain insight into the future shape of the yield curve. The Burg model forecasts a 20‐day yield curve, which fits a pth‐order autoregressive (AR) model to the input signal by minimizing (least squares) the forward and backward prediction errors while constraining the autoregressive parameters to satisfy the Levinson–Durbin recursion. Then, it uses an infinite impulse response prediction error filter. Results are striking when the Burg model is compared to the Diebold and Li model: the model not only significantly improves accuracy, but also its forecast yield curves stick to the shape of observed yield curves, whether normal, humped, flat or inverted. Copyright © 2016 John Wiley & Sons, Ltd.

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  • Pierre Rostan & Rachid Belhachemi & François‐Eric Racicot, 2017. "Yield Curve Forecasting with the Burg Model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(1), pages 91-99, January.
    • Handle: RePEc:wly:jforec:v:36:y:2017:i:1:p:91-99
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    Cited by:

    1. Pierre Rostan & Alexandra Rostan, 2023. "The benefit of the Covid‐19 pandemic on global temperature projections," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(8), pages 2079-2098, December.

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