| 摘要(英) |
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. |
| 參考文獻 |
英文文獻
1. Ali, Z. H., and Burhan, A. M. (2023) “Hybrid Machine Learning Approach for Construction Cost Estimation: An Evaluation of Extreme Gradient Boosting Model”, Asian Journal of Civil Engineering, 24, 2427–2442.
2. Breiman, L. (1996), “Bagging Predictors Machine Learning”, 24, 123–140.
3. --------------- (2001), “Random Forests Machine Learning”, 45, 5–32.
4. Cox, J.C., Ingersoll, J.E. and Ross, S.A. (1985), “A Theory of the Term Structure
of Interest Rates”, In Theory of valuation (pp. 129-164).
5. Dunn, K. (2021), “Avoid R-squared to judge regression model performance”, Towards Data Science.
6. Fisher, I(1896), “Appreciation and Interest: A Study of the Influence of Monetary Appreciation and Depreciation on the Rate of Interest with Applications to the Bimetallic Controversy and the Theory of Interest”, American economic association, Vol. 11, No. 4.
7. Gu, S., Kelly, B., and Xiu, D. (2020),“Empirical Asset Pricing via Machine Learning” The Review of Financial Studies, 33(5), 2223-2273.
8. Harvey, C. R. (1989), “Forecasts of Economic Growth from the Bond and Stock Markets”, Financial Analysts Journal, 45(5), 38-45.
9. Hastie, T., Tibshirani, R., and Friedman, J. (2001), “The Elements of Statistical Learning: Data Mining, Inference and Prediction”, New York: Springer.
10. Hoerl, A. E., and Kennard, R. W. (1970), “Ridge Regression: Biased Estimation for Nonorthogonal Problems”, Technometrics, 12(1), 55–67.
11. Luque Raya, I.M. and Luque Raya, P. (2023), "Machine learning algorithms applied to the estimation of liquidity: the 10-year United States treasury bond", European Journal of Management and Business Economics.
12. Malkiel, B. G.(1973), “A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing”, W. W. Norton & Company.
13. Medeiros, M. C., Vasconcelos, G. F. R., Veiga, Á ., and Zilberman, E. (2019).,
“Forecasting Inflation in a Data-Rich Environment: The Benefits of Machine
Learning Methods”, Journal of Business & Economic Statistics, 39(1), 98–119.
- 33 -
14. Medeiros, M. C., and Mendes, E. (2016), “l1-Regularization of High-Dimensional Time-Series Models With Non-Gaussian and Heteroskedastic Errors,” Journal of Econometrics, 191, 255–271.
15. Schwarz, G. E.(1978), “Estimating the dimension of a model”, Annals of Statistics 6(2): 461–464.
16. Shiller, R. J., and McCulloch, J. H. (1990). “The term structure of interest rates”, Handbook of monetary economics, 1, 627-722.
17. Tibshirani, R. (1996), “Regression Shrinkage and Selection via the LASSO,” Journal of the Royal Statistical Society, Series B, 58, 267–288.
18. Zou, H., and Hastie, T. (2005), “Regularization and Variable Selection via the Elastic Net,” Journal of the Royal Statistical Society, Series B, 67, 301–320.
19. Zou, H. (2006), “The Adaptive LASSO and Its Oracle Properties,” Journal of the American Statistical Association, 101, 1418–1429.
中文文獻
1. 李怡庭(2021)，「貨幣銀行學(四版)」，雙葉書廊
2. 延任(2022)，「美國公債殖利率與市場指數關聯性實證研究」，國立臺灣
大學經濟系在學專班碩士論文
3. 葉憲之(2023)，「參加美國紐約聯邦準備銀行「貨幣政策執行」研習課程
出國報告」，中央銀行
4. 簡嘉瑛(2009)，「美國公債殖利率與景氣循環指標間關聯性之探討」，國
立中央大學財務金融學系碩士論文 |
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