Abstract
In this paper, we analyze the importance of curvature term structure movements on forecasts of interest rates. An extension of the exponential three-factor Diebold and Li (2006) model is proposed, where a fourth factor captures a second type of curvature. The new factor increases model ability to generate volatility and to capture nonlinearities in the yield curve, leading to a significant improvement of forecasting ability. The model is tested against the original Diebold and Li model and some other benchmarks. Based on a forecasting experiment with Brazilian fixed income data, it obtains significantly lower bias and root mean square errors for most examined maturities, and under three different forecasting horizons. Robustness tests based on two sub-sample analyses partially confirm the favorable results.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 1171-1196 |
| Number of pages | 26 |
| Journal | International Journal of Theoretical and Applied Finance |
| Volume | 12 |
| Issue number | 8 |
| DOIs | |
| State | Published - Dec 2009 |
| Externally published | Yes |
All Science Journal Classification (ASJC) codes
- General Economics, Econometrics and Finance
- Finance
Keywords
- Interest rate mean forecasting
- Parametric term structure models
- Principal components
- Vector auto-regressive models
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