Modelling Consumer Price Index Effect on 10-year US Treasury Bond Yields using Least Square Spline Approach

Julia Widiyanti, Safira Salsabila, Dwika Maya Harsanti, Dita Amelia, Marisa Rifada

Abstract


Inflation measured by the Consumer Price Index (CPI) is a critical indicator in the government bond market that directly affects the yields of long-term securities such as the 10-year US Treasury Bond. This study is an explanatory quantitative study that aims to examine the complex dynamics of this relationship using the nonparametric least square spline method. The analysis uses monthly CPI data from FRED and 10-year US Treasury bond yield data from Investing.com for the period 2013-2025. This method divides the data into simple polynomial segments that are smoothly connected at transition points (knots), enabling the modelling of nonlinear patterns without assuming an initial curve shape. The analysis results indicate that a first-degree polynomial spline model (piecewise linear) with three knots successfully represents the bond yield response to inflation shocks with R^2 = 86.48%. Model segmentation identified four regimes: (1) Post-crisis recovery phase, with a negative relationship driven by Fed monetary stimulus suppresing yields despite initial inflation emergence; (2) Policy normalization phase, with a positive relationship aligned with monetary tightening in response to moderate inflation; (3) During the COVID-19 pandemic, a negative relationship due to a surge in demand for safe-haven bonds despite rising inflation; (4) Post-pandemic, the relationship turned positive again following the Fed’s aggressive monetary tightening in response to high global inflation. These findings highlight the urgency of regime-based monitoring for investors and policymakers, while contributing concretely to SDG 8 (decent work and economic growth) through the facilitation of appropriate interest rate policies for sustainable macroeconomic stability, and supporting SDG 9 (industry, innovation, and infrastructure) through the identification of inflation patterns that strengthen shock-resistant infrastructure investment planning and financial innovation during turbulent economic transitions.

Keywords


Least Square Spline; Consumer Price Index; US 10-year Treasury Bond; Nonparametric Model; Inflation.

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References


Azam, M., & Khan, S. (2022). Threshold effects in the relationship between inflation and economic growth: Further empirical evidence from the developed and developing world. International Journal of Finance and Economics, 27(4), 4224–4243. DOI: https://doi.org/10.1002/ijfe.2368

Bantis, L. E., Tsimikas, J. V., & Georgiou, S. D. (2020). Survival estimation through the cumulative hazard with monotone natural cubic spline using convex optimization-the HCNS approach. Computer Methods and Programs in Biomedicine, 190, Article 105357. https://doi.org/10.1016/j.cmpb.2020.105357

Bekaert, G., & Ermolov, A. (2023). International Yield Comovements. Journal of Finance and Quantitative Analysis, 58(1), 250–288. https://doi.org/10.1017/S0022109022000515

Belke, A., Gros, D., & Osowski, T. (2017). The effectiveness of the Fed’s quantitative easing policy: New evidence based on international interest rate differentials. Journal of International Money and Finance, 73, 335–349. https://doi.org/10.1016/j.jimonfin.2017.02.011

Chamidah, N., & Lestari, B. (2022). Analisis Regresi Nonparametrik dengan Perangkat Lunak R (Z. Abadi, A. Riyanto, & R. Wahyudi, Eds.; pp. 91–103). Airlangga University Press.

Chang, M. S., Ju, P., Liu, Y., & Hsueh, S. C. (2022). Determining hedges and safe havens for stocks using interval analysis. North American Journal of Economics and Finance, 61, 286-293. https://doi.org/10.1016/j.najef.2022.101671

Ghodke, M., & Giri, P. (2023). Consumer Price Index (CPI) – Types & Sources. Indian Journal of Community Health, 35(4), 520–525. https://doi.org/10.47203/IJCH.2023.V35I04.020

Hetzel, R. L. (2017). Indexed bonds as an aid to economic policy. In Handbook of Debt Management (pp. 781–792). Taylor and Francis.

Laumer, S., & Schaffer, M. (20hetzelbelke25). Monetary policy transmission under supply chain pressure. European Economic Review, 172, 410-420. https://doi.org/10.1016/j.euroecorev.2024.104949

Karčiauskas, K. (2023). Quadratic-Attraction Subdivision. Computer Graphics Forum, 42(5), 644-657. https://doi.org/10.1111/cgf.14900

Krivobokova, T., Kauermann, G., & Archontakis, F. (2006). Estimating the term structure of interest rates using penalized splines. Statistical Papers, 47(3), 443–457. https://doi.org/10.1007/s00362-006-0297-8

Maharani, M., & Saputro, D. R. S. (2021). Generalized Cross Validation (GCV) in Smoothing Spline Nonparametric Regression Models. Journal of Phsics:Conference Series, 1808(1). https://doi.org/10.1088/1742-6596/1808/1/012053

Mankowski, M., & Moshkov, M. (2021). Segmented Least Squares. In Studies in Systems, Decision and Control (Vol. 331, pp. 147–156). Springer Science and Business Media Deutschland GmbH.

Marchelina, R., Bakar, N., & Asfa’ani, E. (2023). Karakteristik Solusi Kuadrat Terkecil. MAp (Mathematics and Applications) Journal, 5(2), 127–136. https://doi.org/10.15548/map.v5i2.7045

Mohr, R., Coblenz, M., & Kirst., Peter. (2023). Globally optimal univariate spline approximations. Computational Optimization and Application, 85(2), 409-439. DOI: https://doi.org/10.1007/s10589-023-00462-7

Rogan, K. J. (2025). Fictions of the market: The shelter component of the consumer price index in theory and practice. Journal Human Geography (United Kingdom). 33(1), 1–???. https://doi.org/10.1177/19427786251316868

Shelevytsky, I., Shelevytska, V., Semenva, K., & Bykov, I. (2020). Regression Spline-Model in Machine Learning for Signal Prediction and Parameterization. Advances in Interlligent Systems and Computing, 158–174. https://doi.org/10.1007/978-3-030-26474-1_12

Sifriyani, Sari, A. R. M., Dani, A. T. R., & Jalaluddin, S. (2023). Bi-Response Truncated Spline Nonparametric Regression With Optimal Knot Point Selection Using Generalized Cross-Validation in Diabetes Mellitus Patient’S Blood Sugar Levels. Communications in Mathematical Biology and Neuroscience, 2023, 1–18. https://doi.org/10.28919/cmbn/7903

Suhada, A., Syafriandi, Vionanda, D., & Fitri, F. (2023). Modeling Open Unemployment Rate in West Sumatera Province Using Truncated Spline Regression. UNP Journal of Statistics and Data Science, 1(1), 39–44. https://doi.org/10.24036/ujsds/vol1-iss1/3

Suryavanshi, S. (2023). A study of Fisher Effect in India. Journal Indian Economic Review, 58, 485–503. DOI: https://doi.org/10.1007/s41775-023-00180-1

Sutopo, E., & Slamet, A. (2017). Inferential Statistics. Andi Publisher.

Utami, T. W., Haris, M. A., Prahutama, A., & Purnomo, E. A. (2020). Optimal knot selection in spline regression using unbiased risk and generalized cross validation methods. In D. Nurhadiyanto, Sutopo, B. R. Setiadi, & H. Pratiwi (Eds.), Journal of Physics: Conference Series (pp. 12–49). Institute of Physics Publishing. https://doi.org/10.1088/1742-6596/1446/1/012049




DOI: https://doi.org/10.31764/jtam.v9i4.33020

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