基于动态四形因子模型的利率期限结构预测

Li Shan
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引用次数: 0

摘要

本文分析了曲率期限结构变动对利率期限结构拟合和预测的重要性。基于形状因子框架,提出了指数型三因子动态Nelson-Siegel模型的扩展-动态四形状因子模型,其中第四个因子捕获第二类曲率。新因子增强了模型产生波动的能力和捕获收益率曲线非线性的能力,从而显著提高了模型的拟合和预测能力。该模型针对原始的动态尼尔森-西格尔模型和其他一些基准进行了测试。通过对上海证券交易所固定收益数据的拟合和预测实验,得到三种不同预测水平下的均方根误差显著降低。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Forecasting Interest Rates Term Structure Based on Dynamic Four Shape Factors Model
In this paper, the importance of curvature term structure movements on fitting and forecasting of interest rates term structure is analyzed. An extension of the exponential three-factor Dynamic Nelson-Siegel model-Dynamic Four Shape Factors model is proposed based on the Shape Factors Framework, where a fourth factor captures a second type of curvature. The new factor enhances model ability to generate volatility and to capture nonlinearities in the yield curve, which leads to a significant improvement of fitting and forecasting ability. The model is tested against the original Dynamic Nelson-Siegel model and some other benchmarks. Based on a fitting and forecasting experiment with Shanghai Security Exchange's fixed income data, it obtains significantly lower root mean square errors under three different forecasting horizons.
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