Predicting bond betas using macro-finance variables

Aslanidis, N.; Christiansen, C.

Risultato della ricerca: Article

Abstract

We predict bond betas conditioning on a number of macro-finance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high yield corporate bonds. We conduct out-of-sample forecasting using the new approach of combining predictor variables through complete subset regressions (CSR). We consider the robustness of CSR forecasts across the 1-month, 3-month, and 12-month forecasting horizons. The CSR method performs well in predicting bond betas.
Lingua originaleEnglish
pagine (da-a)-
Numero di pagine12
RivistaFinance Research Letters
Stato di pubblicazionePublished - 2018

Cita questo

Predicting bond betas using macro-finance variables. / Aslanidis, N.; Christiansen, C.

In: Finance Research Letters, 2018, pag. -.

Risultato della ricerca: Article

Aslanidis, N.; Christiansen, C. 2018, 'Predicting bond betas using macro-finance variables', Finance Research Letters, pagg. -.
Aslanidis, N.; Christiansen, C. / Predicting bond betas using macro-finance variables. In: Finance Research Letters. 2018 ; pagg. -.
@article{8bbadb0a2965403f8307b1dd86dcd846,
title = "Predicting bond betas using macro-finance variables",
abstract = "We predict bond betas conditioning on a number of macro-finance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high yield corporate bonds. We conduct out-of-sample forecasting using the new approach of combining predictor variables through complete subset regressions (CSR). We consider the robustness of CSR forecasts across the 1-month, 3-month, and 12-month forecasting horizons. The CSR method performs well in predicting bond betas.",
author = "{Aslanidis, N.; Christiansen, C.} and Andrea Cipollini",
year = "2018",
language = "English",
pages = "--",
journal = "Finance Research Letters",
issn = "1544-6123",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Predicting bond betas using macro-finance variables

AU - Aslanidis, N.; Christiansen, C.

AU - Cipollini, Andrea

PY - 2018

Y1 - 2018

N2 - We predict bond betas conditioning on a number of macro-finance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high yield corporate bonds. We conduct out-of-sample forecasting using the new approach of combining predictor variables through complete subset regressions (CSR). We consider the robustness of CSR forecasts across the 1-month, 3-month, and 12-month forecasting horizons. The CSR method performs well in predicting bond betas.

AB - We predict bond betas conditioning on a number of macro-finance variables. We explore differences across long-term government bonds, investment grade corporate bonds, and high yield corporate bonds. We conduct out-of-sample forecasting using the new approach of combining predictor variables through complete subset regressions (CSR). We consider the robustness of CSR forecasts across the 1-month, 3-month, and 12-month forecasting horizons. The CSR method performs well in predicting bond betas.

UR - http://hdl.handle.net/10447/358861

M3 - Article

SP - -

JO - Finance Research Letters

JF - Finance Research Letters

SN - 1544-6123

ER -