Abstract: We develop a robust, flexible and easy-to-implement approach for estimating the yield curve from a large set of treasury securities. Our non-parametric method optimally learns basis functions in reproducing Hilbert spaces with an economically motivated smoothness penalty. We provide the theoretical foundation for our machine learning estimator and an analytical solution as a simple kernel ridge regression, which is straightforward and fast to implement. We show in an extensive empirical study on daily U.S. Treasury securities, that our approach strongly dominates all parametric and non-parametric benchmark methods. Our method achieves substantially smaller out-of-sample yield and pricing errors, while being robust to outliers and data selection choices. We attribute the superior performance to the optimal trade-off between flexibility and smoothness, which positions our method as the new standard for yield curve estimation.

Bio: Damir Filipović holds the Swissquote Chair in Quantitative Finance at the Ecole Polytechnique Fédérale de Lausanne (EPFL) and a Swiss Finance Institute Senior Chair. He also acts as head of the Swiss Finance Institute @ EPFL.

He holds a Ph.D. in mathematics from ETH Zurich and has been a faculty member of the University of Vienna, the University of Munich and Princeton University. He also worked for the Swiss Federal Office of Private Insurance as co-developer of the Swiss Solvency Test.

He is on the editorial board of several academic journals. His research focus is in quantitative finance and risk management. His papers have been published in a variety of academic journals including the Journal of Finance, Journal of Financial Economics, Mathematical Finance, Finance and Stochastics, and the Annals of Applied Probability. He is the author of a textbook titled Term-Structure Models.

Meeting Recording: https://ucsb.zoom.us/rec/share/fLlgQstSjlBqo7vyDnYB4BwOpU2y8m5qGTzAiAtsonG7210xE5XLW3ohu61oiX-4.9z4sJ3GasN4oJ8rI

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