确定内尔松-西格尔模型时变衰减参数的另一种方法

IF 1.9 4区 经济学 Q2 ECONOMICS Computational Economics Pub Date : 2024-07-03 DOI:10.1007/s10614-024-10653-x
Sang-Heon Lee
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引用次数: 0

摘要

本文针对 Nelson-Siegel 收益曲线模型提出了一种替代性的、简单明了的两步估算方法。其目的是为时变衰减参数生成平滑时间序列,并建立稳定的收益率曲线因子。为了纠正过高的参数估计,如跳跃或尖峰,衰减参数将使用闭式表达式向其长期平均值调整。利用美国国债数据进行的实证研究表明,这种方法能产生稳定且易于解释的结果,同时还能有效缓解混杂效应,混杂效应的特点是参数之间的幅度较大且符号相反。在样本外预测练习中,与包括随机漫步模型在内的其他竞争模型相比,所提出的模型表现出相当或适中的性能。特别是,端点移动技术提高了整体预测能力。最后,即使应用于其他国家,所提出的模型也能稳健地显示出有效的平滑效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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An Alternative Approach for Determining the Time-Varying Decay Parameter of the Nelson-Siegel Model

This paper presents an alternative and straightforward two-step estimation method for the Nelson–Siegel yield curve model. The goal is to generate smoothed time series for the time-varying decay parameter and establish stable yield curve factors. To rectify excessive parameter estimates such as jumps or spikes, the decay parameter is adjusted towards its long-run mean using a closed-form expression. Empirical studies conducted with U.S. Treasury data reveal that this method generates stable and easily interpretable outcomes while the confounding effect, which is characterized by large magnitudes with opposite signs among parameters, is effectively mitigated. In out-of-sample forecasting exercises, the proposed model demonstrates comparable or modest performance compared to other competing models, including the random walk model. In particular, the shifting endpoints technique enhances the overall forecasting ability. Finally, the proposed model demonstrates an effective smoothing effect robustly even when applied to other countries.

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来源期刊
Computational Economics
Computational Economics MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-
CiteScore
4.00
自引率
15.00%
发文量
119
审稿时长
12 months
期刊介绍: Computational Economics, the official journal of the Society for Computational Economics, presents new research in a rapidly growing multidisciplinary field that uses advanced computing capabilities to understand and solve complex problems from all branches in economics. The topics of Computational Economics include computational methods in econometrics like filtering, bayesian and non-parametric approaches, markov processes and monte carlo simulation; agent based methods, machine learning, evolutionary algorithms, (neural) network modeling; computational aspects of dynamic systems, optimization, optimal control, games, equilibrium modeling; hardware and software developments, modeling languages, interfaces, symbolic processing, distributed and parallel processing
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