<div dir="ltr">At 3:30, there will be a pre-talk reception with tea, coffee, and snacks, on the first floor of 24 Hillhouse.<div><br></div><div> --Dan</div><div><br></div></div><div dir="ltr"><br></div><br><div class="gmail_quote"><div dir="ltr" class="gmail_attr">On Wed, Apr 5, 2023 at 9:17 AM Hau, Emily <<a href="mailto:emily.hau@yale.edu" target="_blank">emily.hau@yale.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">
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<span style="color:black">FDS & Econometrics Talk<u></u><u></u></span></p>
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<b><span style="font-size:14pt;color:black">“Stripping the Discount Curve – a Robust Machine Learning Approach”
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<b><span style="color:black">Speaker:</span></b><span style="color:black"> Markus Pelger, Stanford
<u></u><u></u></span></p>
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<b><span style="color:black">Wednesday, April 5, 2023, 4:00 pm - 5:00 pm <u></u><u></u></span></b></p>
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<b><span style="color:black">Location:</span></b><span style="color:black"> Mason Lab 211, 9 Hillhouse Avenue with optional remote access via
<a href="https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5afc432b-ddd2-4fa9-8587-af93011fd5c0" target="_blank">
Panopto</a>. <u></u><u></u></span></p>
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<b><span style="color:black">Abstract: </span></b><span style="color:black">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.
<u></u><u></u></span></p>
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<b><span style="color:black">Speaker bio:</span></b><span style="color:black"> 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.
<u></u><u></u></span></p>
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<span style="color:black">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.
<u></u><u></u></span></p>
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<span style="color:black">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. <u></u>
<u></u></span></p>
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<span style="color:black">Website: <a href="https://mpelger.people.stanford.edu/" target="_blank">
https://mpelger.people.stanford.edu/</a> <u></u><u></u></span></p>
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<span style="color:black">Joint talk hosted with Xiaohong Chen and Edward Vytlacil from the Department of Economics and Daniel Spielman from the Departments of Computer Science, Statistics & Data Science, Mathematics and the Yale Institute for Foundations of
Data Science (FDS)<u></u><u></u></span></p>
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<p class="MsoNormal"><b>UPCOMING EVENTS: <u></u><u></u></b></p>
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<p class="MsoNormal">Apr 24, 4:00pm </p>
<p class="MsoNormal"><a href="https://fds.yale.edu/calendar_event/fds-colloquium-schapire/" target="_blank">FDS Colloquium: Robert Schapire (Microsoft Research) "Convex Analysis at Infinity: An Introduction to Astral Space"</a>
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<p class="MsoNormal">May 01, 9:00am </p>
<p class="MsoNormal"><a href="https://fds.yale.edu/calendar_event/data-science-for-healthcare/" target="_blank">FDS Workshop: Healthcare for Data Scientists</a></p>
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<p class="MsoNormal"><span style="font-size:10pt;color:black">Emily E. H. Hau | Associate Director, Data Science @ Yale University<u></u><u></u></span></p>
<p class="MsoNormal"><b><i><span style="font-size:10pt;color:rgb(68,114,196)">Yale Institute for Foundations of Data Science (FDS)</span></i></b><span style="font-size:10pt;color:black"><u></u><u></u></span></p>
<p class="MsoNormal"><b><i><span style="font-size:10pt;color:rgb(68,114,196)">Yale Institute for Network Science (YINS)</span></i></b><span style="font-size:10pt;color:black"><u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:10pt;color:black">17 Hillhouse Avenue | Room 341 | New Haven, CT 06511<u></u><u></u></span></p>
<p class="MsoNormal"><u><span lang="PT-BR" style="font-size:10pt;color:rgb(5,99,193)"><a href="mailto:emily.hau@yale.edu" title="mailto:emily.hau@yale.edu" target="_blank">emily.hau@yale.edu</a> | </span></u><span lang="PT-BR" style="font-size:10pt;color:black">P: 203-436-4732 </span><span style="font-size:10pt;color:black"><u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:10pt;color:black"><a href="https://twitter.com/yaledatascience" target="_blank">@yaledatascience</a>
<a href="https://twitter.com/yinsedge" target="_blank">@YINSedge</a><u></u><u></u></span></p>
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