Affine Term Structure Model with Macroeconomic Factors: Do No‐Arbitrage Restriction and Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?

Affine Term Structure Model with Macroeconomic Factors: Do No‐Arbitrage Restriction and Macroeconomic Factors Imply Better Out‐of‐Sample Forecasts?
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具有宏观经济因素的仿射期限结构模型:无套利限制和宏观经济因素是否意味着更好的样本外预测?

DOI:
10.1002/for.2378
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发表时间:
2016
影响因子:
3.4
通讯作者:
Wali Ullah
Wali Ullah
中科院分区:
经济学4区
文献类型:
--
作者:
Wali Ullah

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本研究扩展了包含宏观经济变量的仿射动态Nelson-Siegel模型。在无套利约束下导出的仿射期限结构模型中包含五个宏观经济变量,以评估它们在期限结构的样本内拟合和样本外预测中的作用。我们发现宏观经济因素与收益率数据之间的关系具有直观的解释,并且收益率与宏观经济因素之间存在相互依赖关系。此外,宏观经济因素显著提高了模型的预测性能。仿射Nelson-Siegel型模型优于基准简单时间序列预测模型。在短期内,具有宏观经济因素的仿射尼尔森-西格尔模型的样本外可预测性优于所有期限的简单仿射收益率模型,而在较长期限内,前者仍然与后者兼容,特别是中长期。版权所有©2015 John Wiley & Sons, Ltd
This study extends the affine dynamic Nelson–Siegel model for the inclusion of macroeconomic variables. Five macroeconomic variables are included in affine term structure model, derived under the arbitrage‐free restriction, to evaluate their role in the in‐sample fitting and out‐of‐sample forecasting of the term structure. We show that the relationship between the macroeconomic factors and yield data has an intuitive interpretation, and that there is interdependence between the yield and macroeconomic factors. Moreover, the macroeconomic factors significantly improve the forecast performance of the model. The affine Nelson–Siegel type models outperform the benchmark simple time series forecast models. The out‐of‐sample predictability of the affine Nelson–Siegel model with macroeconomic factors for the short horizon is superior to the simple affine yield model for all maturities, and for longer horizons the former is still compatible to the latter, particularly for medium and long maturities. Copyright © 2015 John Wiley & Sons, Ltd.
政府债券收益率的期限结构建模与预测
DOI: --
发表时间: 2013
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