美國短中長期公債殖利率預測

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Title:	美國短中長期公債殖利率預測
Authors:	林玉智;Lin, Yu-Chih
Contributors:	經濟學系
Keywords:	美國公債殖利率;機器學習預測;利率期限結構;US Treasury Yield;Machine Learning Prediction;Yield Curve Structure
Date:	2024-07-04
Issue Date:	2024-10-09 16:45:12 (UTC+8)
Publisher:	國立中央大學
Abstract:	美國公債殖利率對經濟影響重大，利率期限結構和殖利率曲線變化常被用來預測景氣循環的變化。一般而言，美國公債殖利率與聯邦基金利率關係緊密，市場依據聯準會的公開信息預測殖利率。然而，近年來因疫情、烏俄戰爭和能源危機，通膨問題嚴峻，聯準會為穩住經濟而頻繁調整貨幣政策，導致預測難度增加。本研究旨在利用更多元的總體經濟、貨幣政策和金融變數，構建模型來預測美國公債殖利率。傳統線性預測方法容易因過度擬合而影響準確性，因此引入機器學習方法解決非線性問題和高維度數據處理，通過比較不同的機器學習預測模型與傳統迴歸模型，尋找短中長期公債殖利率的最佳預測方法。研究結果顯示，傳統基準模型適合極短期預測，但隨預測期延長，預測能力下降。機器學習模型在處理高維度非線性數據上表現出色，特別是在兩期以上的預測中更準確。其中，隨機森林模型在短中長期美國公債殖利率預測中表現最佳且穩定。深度神經網絡模型在八期以上的預測中表現良好，但在面對緊急政策調整時誤差較大。總體而言，隨機森林模型在短中長期美國公債殖利率預測中均展現出高度穩定性和準確性。 ;U.S. Treasury yields have a significant impact on the economy, and the term structure of interest rates and changes in the yield curve are often used to predict economic cycles. Generally, U.S. Treasury yields are closely related to the federal funds rate, with the market predicting yields based on information released by the Federal Reserve. However, in recent years, the severe inflation problems caused by the pandemic, the Russia-Ukraine war, and the energy crisis have led to frequent adjustments in monetary policy by the Federal Reserve to stabilize the economy, increasing the difficulty of making accurate predictions. This study aims to construct a model to predict U.S. Treasury yields using a more diverse set of macroeconomic, monetary policy, and financial variables. Traditional linear prediction methods often suffer from overfitting, affecting accuracy. Therefore, machine learning methods are introduced to address nonlinear issues and handle high-dimensional data. By comparing different machine learning prediction models with traditional regression models, the study seeks to identify the best prediction method for short, medium, and long-term Treasury yields. The research results indicate that traditional benchmark models are suitable for extremely short-term predictions, but their predictive ability declines as the forecast period extends. Machine learning models excel in handling high-dimensional nonlinear data, particularly showing greater accuracy in predictions beyond two periods. Among them, the random forest model performs best and remains stable in predicting short, medium, and long-term U.S. Treasury yields. The deep neural network model performs well in predictions beyond eight periods but shows larger errors when faced with emergency policy adjustments. Overall, the random forest model demonstrates high stability and accuracy in predicting short, medium, and long-term U.S. Treasury yields.
Appears in Collections:	[Graduate Institute of Economics] Electronic Thesis & Dissertation

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