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Forecasting Bond Risk Premia with Unspanned Macroeconomic Information

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  • Rui Liu

    (Palumbo Donahue School of Business, 811 Rockwell Hall, Duquesne University, Pittsburgh, PA 15282, USA)

Abstract

I provide evidence on the existence of unspanned macro risk. I investigate the usefulness of unspanned macro information for forecasting bond risk premia in a macro-finance term structure model from the perspective of a bond investor. I account for model uncertainty by combining forecasts with and without unspanned output and inflation risks optimally from the forecaster’s objective. Incorporating macro information generates significant gains in forecasting bond risk premia relative to yield curve information at long forecast horizons, especially when allowing for time-varying combination weight. These gains in predictive accuracy significantly improve investor utility.

Suggested Citation

  • Rui Liu, 2019. "Forecasting Bond Risk Premia with Unspanned Macroeconomic Information," Quarterly Journal of Finance (QJF), World Scientific Publishing Co. Pte. Ltd., vol. 9(01), pages 1-62, March.
    • Handle: RePEc:wsi:qjfxxx:v:09:y:2019:i:01:n:s2010139219400019
    DOI: 10.1142/S2010139219400019
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