Generalized Nelson–Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts?
Generalized Nelson–Siegel term structure model: do the second slope and curvature factors improve the in-sample fit and out-of-sample forecasts?
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广义尼尔森-西格尔期限结构模型：第二个斜率和曲率因子是否改善了样本内拟合和样本外预测？
DOI：
10.1080/02664763.2014.993363
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发表时间：
2015
影响因子：
1.5
通讯作者：
Yoshihiko Tsukuda
中科院分区：
文献类型：
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作者：
Wali Ullah;Y. Matsuda;Yoshihiko Tsukuda
动态Nelson-Siegel（DNS）模型乃至Svensson模型的推广，在拟合短期收益率时存在困难，未能把握日本国债收益率曲线短端平坦、拐点多的特点。因此，一个密切相关的广义动态纳尔逊-西格尔（GDNS）模型，有两个斜率和曲率被认为是经验比较传统的DNS在样本拟合以及样本预测。此外，GDNS随时间变化的波动性组件，建模为标准EGDSPOT过程，也被认为是评估其性能的GDNS。GDNS模型在样本内拟合和样本外预测方面一致优于DNS。此外，考虑到随时间变化的波动性的扩展模型超过了拟合收益率曲线的其他模型，并产生了相对更准确的6个月和12个月的预测，而GDNS模型则对非常短的预测范围进行了更精确的预测。
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.
政府债券收益率的期限结构建模与预测
影响因子：
3.4
作者：
Wali Ullah;Yasumasa Matsuda and Yoshihiko Tsukuda
通讯作者：
Yasumasa Matsuda and Yoshihiko Tsukuda
考虑潜在因素和宏观经济因素的政府债券收益率期限结构预测：宏观经济因素是否意味着更好的样本外预测？
DOI：
--
发表时间：
2013
期刊：
Journal of Forecasting (forthcoming)
影响因子：
--
作者：
Walli Ullah;Yoshihiko Tsukuda and Yasumasa Matsuda
通讯作者：
Yoshihiko Tsukuda and Yasumasa Matsuda