Predicting bond betas using macro-finance variables

被引：7

作者：

Aslanidis, Nektarios ^{[1
]}

Christiansen, Charlotte ^{[2
,3
]}

Cipollini, Andrea ^{[4
]}

机构：

[1] Univ Rovira & Virgili, Dept Econ, CREIP, Avinguda Univ 1, Reus 43204, Spain

[2] Aarhus Univ, Sch Business & Social Sci, Dept Econ & Business Econ, CREATES, Fuglesangs Alle 4, DK-8210 Aarhus V, Denmark

[3] Lund Univ, Lund, Sweden

[4] Univ Palermo, Dept Econ Management & Stat, Viale Sci, Palermo, Italy

来源：

FINANCE RESEARCH LETTERS | 2019年 / 29卷

基金：

新加坡国家研究基金会;

关键词：

Bond betas; Complete subset regressions; Corporate bonds; Government bonds; Macro-finance variables; Model confidence set; STOCK; RISK; RETURNS; LINKS;

D O I：

10.1016/j.frl.2018.07.007

中图分类号：

F8 [财政、金融];

学科分类号：

0202 ;

摘要：

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.

引用

页码：193 / 199

页数：7

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