[Sds-seminars] [Sds-announce] [FDS] Today: Econometrics x FDS: Markus Pelger, "Stripping the Discount Curve – a Robust Machine Learning Approach"

Wed Apr 5 12:02:24 EDT 2023

At 3:30, there will be a pre-talk reception with tea, coffee, and snacks,
on the first floor of 24 Hillhouse.

 --Dan

On Wed, Apr 5, 2023 at 9:17 AM Hau, Emily <emily.hau at yale.edu> wrote:

> 
>
> [image: image008.png]
>
>
>
> FDS & Econometrics Talk
>
> *“Stripping the Discount Curve – a Robust Machine Learning Approach” *
>
> *Speaker:* Markus Pelger, Stanford
>
> *Wednesday,  April 5, 2023, 4:00 pm - 5:00 pm *
>
> *Location:* Mason Lab 211, 9 Hillhouse Avenue with optional remote access
> via Panopto
> <https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=5afc432b-ddd2-4fa9-8587-af93011fd5c0>.
>
>
> *Abstract: *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.
>
> *Speaker 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.
>
> Website: https://mpelger.people.stanford.edu/
>
> 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)
>
> *Add event to calendar*
>
> [image: image001.png] <https://www.addevent.com/event/hp16617364+apple> [image:
> image002.png] <https://www.addevent.com/event/hp16617364+google> [image:
> image003.png] <https://www.addevent.com/event/hp16617364+office365> [image:
> image004.png] <https://www.addevent.com/event/hp16617364+outlook> [image:
> image005.png] <https://www.addevent.com/event/hp16617364+outlookcom> [image:
> image006.png] <https://www.addevent.com/event/hp16617364+yahoo>
>
>
>
> *UPCOMING EVENTS: *
>
>
>
> Apr 24, 4:00pm
>
> FDS Colloquium: Robert Schapire (Microsoft Research) "Convex Analysis at
> Infinity: An Introduction to Astral Space"
> <https://fds.yale.edu/calendar_event/fds-colloquium-schapire/>
>
>
>
> May 01, 9:00am
>
> FDS Workshop: Healthcare for Data Scientists
> <https://fds.yale.edu/calendar_event/data-science-for-healthcare/>
>
>
>
>
>
> Emily E. H. Hau | Associate Director, Data Science @ Yale University
>
> *Yale Institute for Foundations of Data Science (FDS)*
>
> *Yale Institute for Network Science (YINS)*
>
> 17 Hillhouse Avenue | Room 341 | New Haven, CT 06511
>
> *emily.hau at yale.edu <emily.hau at yale.edu> | *P: 203-436-4732
>
> @yaledatascience <https://twitter.com/yaledatascience> @YINSedge
> <https://twitter.com/yinsedge>
>
>
>
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