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Forecasting Bond Risk Premia with Unspanned Macroeconomic Information

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

Quarterly Journal of Finance (QJF), 2019, vol. 09, issue 01, 1-62

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.

Keywords: Unspanned macro risk; bond risk premia; forecast combination; model uncertainty; economic value (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1142/S2010139219400019

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