对比多种模型预测美债收益率曲线，发现经典方法仍更优，但部分机器学习模型表现不俗。
Yield Curve Forecasting using Machine Learning and Econometrics: A Comparative Analysis
- 用47年日频数据比较计量、传统机器学习与深度学习模型
- ARIMA和朴素模型整体最优，TimeGPT、LGBM、RNN表现最佳的机器学习模型
- 验证非平稳数据输入对深度学习的影响，为金融时序建模提供参考
尽管机器学习在自然语言处理和计算机视觉等领域取得突破，其在时间序列预测中的作用仍存在争议，尤其在金融领域。本文基于47年日频数据，对比了计量经济学、经典机器学习与深度学习方法在美债收益率曲线预测中的表现。收益率曲线是债券市场参与者广泛使用的工具，其规模超过股票市场。研究涵盖自回归积分滑动平均（ARIMA）及其扩展、基准模型、集成方法、循环神经网络（RNNs）及多个用于预测的Transformer模型。结果表明，总体上ARIMA和简单经济模型优于其他方法，仅在某一时间段例外。在机器学习模型中，TimeGPT、LGBM和RNN表现最佳。此外，论文探讨了深度学习模型使用平稳或非平稳数据的适用性。
原文摘要 · Abstract (English)
While machine learning has revolutionized many fields such as natural language processing (NLP) and computer vision, its impact on time-series forecasting is still widely disputed, especially in the finance domain. This paper compares forecasting performance on U.S. Treasury yield curve data across econometrics/time-series analysis, classical machine learning, and deep learning methods, using daily data over 47 years. The Treasury yield curve is important because it is widely used by every participant in the bond markets, which are larger than equity markets. We examine a variety of methods that have not been tested on yield curve forecasting, especially deep learning algorithms. The algorithms include the Autoregressive Integrated Moving Average (ARIMA) model and its extensions, naive benchmarks, ensemble methods, Recurrent Neural Networks (RNNs), and multiple transformers built for forecasting. ARIMA and naive econometric models outperform other models overall, except in one time block. Of the machine learning methods, TimeGPT, LGBM and RNNs perform the best. Furthermore, the paper explores whether stationary or nonstationary data are more appropriate as input to deep learning models.
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