This study empirically examines the role of macroeconomic and stock market variables in the dynamic Nelson-Siegel framework with the purpose of fitting and forecasting the term structure of interest rate on the Japanese government bond market. The Nelson-Siegel type models in state-space framework considerably outperform the benchmark simple time series forecast models such as an AR(1) and a random walk. The yields-macro model incorporating macroeconomic factors leads to a better in-sample fit of the term structure than the yields-only model. The out-of-sample predictability of the former for short-horizon forecasts is superior to the latter for all maturities examined in this study, and for longer horizons the former is still compatible to the latter. Inclusion of macroeconomic factors can dramatically reduce the autocorrelation of forecast errors, which has been a common phenomenon of statistical analysis in previous term structure models.
ASJC Scopus subject areas
- Modelling and Simulation
- Computer Science Applications
- Strategy and Management
- Statistics, Probability and Uncertainty
- Management Science and Operations Research