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

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

机译：广义Nelson-Siegel项结构模型：第二个斜率和曲率因子是否会改善样本内拟合和样本外预测？

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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.

机译：动态的Nelson-Siegel（DNS）模型，甚至该模型的Svensson推广都难以拟合短期到期收益率，并且无法掌握日本国债收益率曲线的特征，该曲线在短期内是平坦的并且具有多个拐点点。因此，考虑了具有两个斜率和曲率的密切相关的广义动态Nelson-Siegel（GDNS）模型，并在样本内拟合和样本外预测方面与传统DNS进行了经验比较。此外，还考虑了具有时变波动成分的GDNS（以标准EGARCH流程建模）来评估其相对于GDNS的性能。就样本内拟合和样本外预测而言，GDNS模型在整体上胜过DNS。此外，解释时变波动率的扩展模型优于其他模型以拟合收益率曲线，并产生相对更准确的6个月和12个月提前预测，而GDNS模型则提供了针对非常短的预测范围的更精确的预测。

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来源
《Journal of applied statistics》 |2015年第4期|876-904|共29页
作者
Wali Ullah; Yasumasa Matsuda; Yoshihiko Tsukuda;
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作者单位

Graduate School of Economics and Management, Tohoku University, Sendai 980-8577, Japan,Institute of Business Administration (IBA), Karachi;

Graduate School of Economics and Management, Tohoku University, Sendai 980-8577, Japan;

Graduate School of Economics and Management, Tohoku University, Sendai 980-8577, Japan;

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正文语种 eng
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term structure of interest rates; latent factors model; state-space model; Kalman filter; EGARCH; forecasting; bond market;

机译：利率期限结构;潜在因素模型状态空间模型卡尔曼滤波器EGARCH;预测;债券市场;

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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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