本研究旨在建構美國公債殖利率的風險結構，並評估其在不同經濟情境下的變化。相較於現有文獻中以 GDP 為核心的 Growth-at-Risk（GaR）架構，創新地提出「殖利率風險結構」（Term Structure of Interest-at-Risk）概念，將不同存續期間的美國公債殖利率視為應變數，試圖找出其上下行風險的來源與傳導機制。 首先本文採用多種機器學習方法進行變數篩選，並以均方根誤差（RMSE）作為輔助依據，選出各期限殖利率下對解釋力最具貢獻的模型，將其挑選出之關鍵變數納入分量迴歸架構中，估計殖利率在不同條件分位下的風險結構，藉此捕捉利率反應於經濟極端情境的潛在異質性。 實證結果指出，短端殖利率對通膨與工業生產等變數較敏感，而長端殖利率則更受市場風險偏好與中長期預期影響。部分變數在不同期限的殖利率中呈現方向相反的效果，顯示市場對相同訊號可能有期限上的不同詮釋。此外，殖利率對特定變數的反應在極端情況下或有不同，顯示風險評估應考慮條件性與非對稱性。 本研究所建立之殖利率風險結構，有助於政策制定者與風險管理者掌握利率變動潛在風險，並可作為利率政策評估與資產配置的量化依據。;This study aims to construct the risk structure of U.S. Treasury yields and evaluate its variation under different economic conditions. In contrast to existing literature that centers on GDP within the Growth-at-Risk (GaR) framework, this paper introduces an innovative concept—the Term Structure of Interest-at-Risk—which treats Treasury yields of different maturities as dependent variables to identify the sources and transmission mechanisms of both upside and downside risks. First, this study applies various machine learning methods for variable selection and uses root mean square error (RMSE) as a supplementary criterion to identify the models with the greatest explanatory power across different maturities. The key variables selected are then incorporated into a quantile regression framework to estimate the conditional risk structure of yields and capture the potential heterogeneity in yield responses under extreme economic scenarios. Empirical results show that short-term yields are more sensitive to variables such as inflation and industrial production, while long-term yields are more influenced by market risk sentiment and medium- to long-term expectations. Some variables exhibit opposite effects across different maturities, suggesting that markets may interpret the same signals differently depending on the term. In addition, yield responses to certain variables may differ under extreme conditions, indicating that risk assessments should consider both conditionality and asymmetry. The risk structure of interest rates developed in this study offers policymakers and risk managers a quantitative basis for understanding potential interest rate risks, and serves as a useful tool for evaluating policy and guiding portfolio allocation.
