 Generalized Nelson-Siegel Term Structure Model: Do the second slope and curvature factors improve the in-sample fit and out-of-sample forecast?Wali Ullah and Yasumasa Matsuda No 312, TERG Discussion Papers from Graduate School of Economics and Management, Tohoku University 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 (JGBs) yield curve, which is flat at the short end and have multiple inflection points. Therefore, a closely related generalized Nelson-Siegel model (GDNS) with 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, modelled as standard EGARCH process, is also considered to evaluate its performance in relation to the GDNS. The GDNS models 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. Pages: 39 pages Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:toh:tergaa:312 Access Statistics for this paper More papers in TERG Discussion Papers from Graduate School of Economics and Management, Tohoku University Contact information at EDIRC. |
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