Generalized Nelson–Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts?

Wali Ullah, Yasumasa Matsuda, Yoshihiko Tsukuda

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

Abstract

The dynamic Nelson–Siegel (DNS) model and even the Svensson generalization of the model have trouble in fitting the short maturity yields and fail to grasp the characteristics of the Japanese government bonds yield curve, which is flat at the short end and has multiple inflection points. Therefore, a closely related generalized dynamic Nelson–Siegel (GDNS) model that has two slopes and curvatures is considered and compared empirically to the traditional DNS in terms of in-sample fit as well as out-of-sample forecasts. Furthermore, the GDNS with time-varying volatility component, modeled as standard EGARCH process, is also considered to evaluate its performance in relation to the GDNS. The GDNS model unanimously outperforms the DNS in terms of in-sample fit as well as out-of-sample forecasts. Moreover, the extended model that accounts for time-varying volatility outpace the other models for fitting the yield curve and produce relatively more accurate 6- and 12-month ahead forecasts, while the GDNS model comes with more precise forecasts for very short forecast horizons.

Original languageEnglish
Pages (from-to)876-904
Number of pages29
JournalJournal of Applied Statistics
Volume42
Issue number4
DOIs
Publication statusPublished - 2015 Apr 3

Keywords

  • EGARCH
  • Kalman filter
  • bond market
  • forecasting
  • latent factors model
  • state-space model
  • term structure of interest rates

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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