Forecasting bond yields with segmented term structure models

Caio Almeida, Kym Ardison, Daniela Kubudi, Axel Simonsen, José Vicente

Research output: Contribution to journalArticle

2 Scopus citations

Abstract

Inspired by the preferred habitat theory, we propose parametric interest rate models that split the term structure into segments. The proposed models are compared with successful term structure benchmarks based on out-of-sample forecasting exercises using U.S. Treasury data. We show that segmentation can improve long-horizon term structure forecasts when compared with nonsegmentation. Additionally, introducing cointegration in latent factor dynamics of segmented models makes them particularly strong to forecast short-maturity yields. Better forecasting is justified by the segmented models' ability to accommodate idiosyncratic shocks in the cross-section of yields.

Original languageEnglish (US)
Pages (from-to)1-33
Number of pages33
JournalJournal of Financial Econometrics
Volume16
Issue number1
DOIs
StatePublished - Dec 1 2018
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Finance
  • Economics and Econometrics

Keywords

  • Error correction model
  • Exponential splines
  • Local shocks
  • Model selection
  • Preferred habitat theory

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