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Expected Business Conditions and Bond Risk Premia

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  • Eriksen, Jonas N.

Abstract

In this article, I study the predictability of bond risk premia by means of expectations to future business conditions using survey forecasts from the Survey of Professional Forecasters. I show that expected business conditions consistently affect excess bond returns and that the inclusion of expected business conditions in standard predictive regressions improve forecast performance relative to models using information derived from the current term structure or macroeconomic variables. The results are confirmed in a real-time out-of-sample exercise, where the predictive accuracy of the models is evaluated both statistically and from the perspective of a mean-variance investor that trades in the bond market.

Suggested Citation

  • Eriksen, Jonas N., 2017. "Expected Business Conditions and Bond Risk Premia," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(4), pages 1667-1703, August.
    • Handle: RePEc:cup:jfinqa:v:52:y:2017:i:04:p:1667-1703_00
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    Citations

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    Cited by:

    1. Mirco Rubin & Dario Ruzzi, 2020. "Equity Tail Risk in the Treasury Bond Market," Papers 2007.05933, arXiv.org.
    2. Su, Hao & Ying, Chengwei & Zhu, Xiaoneng, 2022. "Disaster risk matters in the bond market," Finance Research Letters, Elsevier, vol. 47(PA).
    3. Wang, Yunqi & Zhou, Ti, 2023. "Out-of-sample equity premium prediction: The role of option-implied constraints," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 199-226.
    4. João F. Caldeira, 2020. "Investigating the expectation hypothesis and the risk premium dynamics: new evidence for Brazil," Empirical Economics, Springer, vol. 59(1), pages 395-412, July.
    5. Mirco Rubin & Dario Ruzzi, 2020. "Equity tail risk in the treasury bond market," Temi di discussione (Economic working papers) 1311, Bank of Italy, Economic Research and International Relations Area.
    6. Wan, Runqing & Fulop, Andras & Li, Junye, 2022. "Real-time Bayesian learning and bond return predictability," Journal of Econometrics, Elsevier, vol. 230(1), pages 114-130.
    7. 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.
    8. Daniel Borup & Jonas N. Eriksen & Mads M. Kjær & Martin Thyrsgaard, 2020. "Predicting bond return predictability," CREATES Research Papers 2020-09, Department of Economics and Business Economics, Aarhus University.
    9. Leippold, Markus & Yang, Hanlin, 2019. "Particle filtering, learning, and smoothing for mixed-frequency state-space models," Econometrics and Statistics, Elsevier, vol. 12(C), pages 25-41.

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    More about this item

    JEL classification:

    • E43 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Interest Rates: Determination, Term Structure, and Effects
    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • E47 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Forecasting and Simulation: Models and Applications
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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