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Prediction bias correction for dynamic term structure models

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  • Raviv, Eran

Abstract

When the yield curve is modelled using an affine factor model, residuals may still contain relevant information and do not adhere to the familiar white noise assumption. This paper proposes a pragmatic way to improve out of sample performance for yield curve forecasting. The proposed adjustment is illustrated via a pseudo out-of-sample forecasting exercise implementing the widely used Dynamic Nelson–Siegel model. Large improvement in forecasting performance is achieved throughout the curve for different forecasting horizons. Results are robust to different time periods, as well as to different model specifications.

Suggested Citation

  • Raviv, Eran, 2015. "Prediction bias correction for dynamic term structure models," Economics Letters, Elsevier, vol. 129(C), pages 112-115.
    • Handle: RePEc:eee:ecolet:v:129:y:2015:i:c:p:112-115
    DOI: 10.1016/j.econlet.2015.01.022
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    Cited by:

    1. Januj Juneja, 2018. "Empirical performance of Gaussian affine dynamic term structure models in the presence of autocorrelation misspecification bias," Review of Quantitative Finance and Accounting, Springer, vol. 50(3), pages 695-715, April.

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    More about this item

    Keywords

    Yield curve; Nelson–Siegel; Time varying loadings; Factor models;
    All these keywords.

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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