Adaptive Dynamic Nelson-Siegel Term Structure Model with Applications
Adaptive Dynamic Nelson-Siegel Term Structure Model with Applications
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DOI：
10.2139/ssrn.2025853
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发表时间：
2013-06
期刊：
影响因子：
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通讯作者：
Ying Chen;Linlin Niu
中科院分区：
文献类型：
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作者：
Ying Chen;Linlin Niu
本文提出了一种自适应动态Nelson-Siegel（ADNS）模型来自适应地检测参数变化并预测收益率曲线。该模型是简单而灵活的，可以安全地应用到平稳和非平稳的情况下，不同的参数变化的来源。对于1998年1月至2010年9月美国收益率曲线的3至12个月样本外预测，ADNS模型在流行的简化形式和仿射期限结构模型中均占主导地位;与随机游走预测相比，ADNS稳定地将预测误差测量值降低了20%至60%。当地估计的系数和确定的稳定子样本随着时间的推移与政策变化和最近的金融危机的时间。
We propose an Adaptive Dynamic Nelson–Siegel (ADNS) model to adaptively detect parameter changes and forecast the yield curve. The model is simple yet flexible and can be safely applied to both stationary and nonstationary situations with different sources of parameter changes. For the 3- to 12-months ahead out-of-sample forecasts of the US yield curve from 1998:1 to 2010:9, the ADNS model dominates both the popular reduced-form and affine term structure models; compared to random walk prediction, the ADNS steadily reduces the forecast error measurements by between 20% and 60%. The locally estimated coefficients and the identified stable subsamples over time align with policy changes and the timing of the recent financial crisis.