The yield curve of U.S. Treasury securities is one of the most fundamental economic quantities and critical datasets for researchers and practitioners. The yield curve or, equivalently, discount curve is a key factor for economists, traders, asset managers, central banks, and financial-markets regulators. Precise and robust yield estimates are needed for trading and making investment decisions, studying the term structure, predicting bond returns, analyzing monetary policy, and pricing assets, derivatives and liabilities. We introduce a robust, flexible and easy-to-implement method for estimating the yield curve from the sparse set of noisy Treasury securities. Our non-parametric estimator can explain complex yield curve shapes. We trade off pricing errors against an economically motivated smoothness reward of the discount curve. This uniquely determines the optimal basis functions that span the discount curve in a reproducing kernel Hilbert space. We show that most existing models for estimating the discount curve are nested within our general framework by imposing additional ad-hoc assumptions. We provide a closed-form solution of our machine learning estimator as a simple kernel ridge regression, which is straightforward to implement. We show in an extensive empirical study on U.S. Treasury securities, that our method strongly dominates all parametric and non-parametric benchmarks. It 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. We provide a publicly available and regularly updated new benchmark dataset for daily zero-coupon Treasury yields based on our estimates. Our benchmark dataset provides the most precise zero-coupon Treasury yield estimates for all maturity ranges, while being robust to data selection choices.

Bio: Markus Pelger is an Assistant Professor of Management Science & Engineering at Stanford University and a Reid and Polly Anderson Faculty Fellow.

His research focuses on understanding and managing financial risk. He develops mathematical financial models and statistical methods, analyzes financial data and engineers computational techniques. His research is divided into three streams: statistical learning in high-dimensional financial data sets, stochastic financial modeling, and high-frequency statistics. His most recent work focuses on developing machine learning solutions to big-data problems in empirical asset pricing.

Markus’ work has appeared in the Journal of Finance, Review of Financial Studies, Management Science, Journal of Econometrics and Journal of Applied Probability. He is an Associate Editor of Management Science, Digital Finance and Data Science in Science. His research has been recognized with several awards, including the Utah Winter Finance Conference Best Paper Award, the Best Paper in Asset Pricing Award at the SFS Cavalcade, the Dennis Aigner Award of the Journal of Econometrics, the International Center for Pension Management Research Award, the CAFM Best Paper Award and the IQAM Research Award. He has been invited to speak at hundreds of world-renowned universities, conferences and investment and technology firms.

Markus received his Ph.D. in Economics from the University of California, Berkeley. He has two Diplomas in Mathematics and in Economics, both with highest distinction, from the University of Bonn in Germany. He is a scholar of the German National Merit Foundation and he was awarded a Fulbright Scholarship, the Institute for New Economic Thinking Prize, the Eliot J. Swan Prize and the Graduate Teaching Award at Stanford University. Markus is a founding organizer of the AI & Big Data in Finance Research Forum and the Advanced Financial Technology Laboratories.