Forecasting Bond Risk Premia using Stationary Yield Factors

38 Pages Posted: 13 Apr 2021

See all articles by Tobias Hoogteijling

Tobias Hoogteijling

Robeco Asset Management

Martin Martens

Robeco Asset Management

Michel van der Wel

Erasmus University Rotterdam; CREATES; ERIM; Tinbergen Institute

Date Written: April 12, 2021

Abstract

The standard way to summarize the yield curve is to use the first three principal components of the yield curve, resulting in level, slope and curvature factors. Yields, however, are non-stationary. We analyze the first three principal components of yield changes, which correspond to changes in level, slope and curvature. The new factors based on changes in yields have strong predictive power for bond risk premia, in contrast to the factors based on yield levels. We also provide insights into the impact this has on the added value of macro data for bond risk premia predictions and the recent conclusion that machine learning provides better forecasts than linear regression.

Keywords: Yield curve, Bond risk premia, Forecasting, PCA, Machine learning

JEL Classification: G12, G17, C38, E43, C45

Suggested Citation

Hoogteijling, Tobias and Martens, Martin P.E. and van der Wel, Michel, Forecasting Bond Risk Premia using Stationary Yield Factors (April 12, 2021). Available at SSRN: https://ssrn.com/abstract=3824896 or http://dx.doi.org/10.2139/ssrn.3824896

Tobias Hoogteijling (Contact Author)

Robeco Asset Management ( email )

Rotterdam, 3011 AG
Netherlands
0655685700 (Phone)

Martin P.E. Martens

Robeco Asset Management ( email )

Weena 850
Rotterdam, 3014 DA
Netherlands

Michel Van der Wel

Erasmus University Rotterdam ( email )

Burg. Oudlaan 50
Rotterdam, NL 3062 PA
Netherlands

CREATES ( email )

School of Economics and Management
Building 1322, Bartholins Alle 10
DK-8000 Aarhus C
Denmark

ERIM ( email )

P.O. Box 1738
3000 DR Rotterdam
Netherlands

Tinbergen Institute ( email )

Burg. Oudlaan 50
Rotterdam, 3062 PA
Netherlands

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