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Predicting Bond Betas using Macro-Finance Variables

Author

Listed:
  • Nektarios Aslanidis

    (University Rovira Virgili, CREIP)

  • Charlotte Christiansen

    (Aarhus University and CREATES)

  • Andrea Cipollini

    (University of Palermo)

Abstract

We conduct in-sample and out-of-sample forecasting using the new approach of combining explanatory variables through complete subset regressions (CSR). We predict bond CAPM betas and bond returns conditioning on various macro-fi?nance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high-yield corporate bonds. The CSR method performs well in predicting bond betas, especially in-sample, and, mainly high-yield bond betas when the focus is out-of-sample. Bond returns are less predictable than bond betas.

Suggested Citation

  • Nektarios Aslanidis & Charlotte Christiansen & Andrea Cipollini, 2017. "Predicting Bond Betas using Macro-Finance Variables," CREATES Research Papers 2017-01, Department of Economics and Business Economics, Aarhus University.
    • Handle: RePEc:aah:create:2017-01
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    References listed on IDEAS

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    Cited by:

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    2. Ermolov, Andrey, 2022. "Time-varying risk of nominal bonds: How important are macroeconomic shocks?," Journal of Financial Economics, Elsevier, vol. 145(1), pages 1-28.

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    More about this item

    Keywords

    bond betas; complete subset regressions; corporate bonds; macro-?finance variables; model confi?dence set; risk-return trade-off.;
    All these keywords.

    JEL classification:

    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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