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Predicting Bond Betas using Macro-Finance Variables

Nektarios Aslanidis (), Charlotte Christiansen () and Andrea Cipollini ()
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Nektarios Aslanidis: University Rovira Virgili, CREIP, Postal: Department of Economics, 43204 Reus, Catalonia, Spain
Charlotte Christiansen: Aarhus University and CREATES, Postal: Department of Economics and Business Economics, Fuglesangs Allé 4, 8210 Aarhus V, Denmark
Andrea Cipollini: University of Palermo, Postal: Department of Economics, Management and Statistics, University of Palermo, Viale delle Scienze, Palermo, Italy

CREATES Research Papers from Department of Economics and Business Economics, Aarhus University

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.

Keywords: bond betas; complete subset regressions; corporate bonds; macro-?finance variables; model confi?dence set; risk-return trade-off. (search for similar items in EconPapers)
JEL-codes: G12 G14 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-fmk
Date: 2017-01-10
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