Events / Upcoming
Financial Engineers Give a Personal View of Their Careers in Quantitative Finance
A Series of Panel Discussions for Students Interested in a Career in Quantitative Finance
Wednesday, February 28th
Program: 6:30 p.m.
Reception: 8:30 p.m.
2201 G Street,
Funger Hall Auditorium 103
Washington, DC 20052
In Partnership with
The George Washington University
School of Business
Mahmut Sen, Worldbank
Ryan Henning, Freddie Mac
Steve Holden, Fannie Mae
Svein Backer, Lockheed Martin Investment Management Company
Stephen Young, Wells Fargo
Registration is Free!
Moderator: Stephen Young
Stephen D. Young works in the Wealth & Investment Management (WIM) division of Wells Fargo and is the Head of Market Risk and Advanced Analytics for WIM’s Investment Risk organization. Prior to this current role, Stephen worked in the WIM division of Wells Fargo as the Head of Investment and Market Risk for Wells Fargo Asset Management (WFAM). At a time when WFAM had several partly or wholly owned affiliates, Stephen served as the Chief Risk Officer for the Affiliated Managers Division of Wells Fargo. Over his tenure at Wachovia and Wells Fargo Stephen has held various positions including the Head of the Option Strategies Group for Evergreen Investments, Head of Wachovia’s Credit & Counterparty Risk Analytics Group, Director of Risk Oversight, and he also worked in Wachovia’s Equity Derivatives Group.
Prior to joining Wachovia, Stephen was with Merrill Lynch and Sterling Investments. Stephen has published in peer-reviewed academic and practitioner journals including Journal of Derivatives, Journal of Fixed Income, Journal of Futures Markets, Review of Quantitative Finance and Accounting, and others. Dr. Young is an adjunct professor at The George Washington University (GWU) (2005 – Present) where he teaches Cases in Financial Modeling and Engineering (GWU, School of Business, MS Finance Program), Stochastic Processes in Engineering, and Uncertainty Analysis in Engineering (GWU, School of Engineering and Applied Science, MS Electrical Engineering Program). Dr. Young has also taught at the Seoul School of Integrated Sciences and Technologies in Seoul, South Korea (2006 – 2013, Cases in Financial Modeling and Engineering) and the University of North Carolina, Charlotte (UNCC) (2015 – 2020, Asset and Portfolio Management, UNCC, Belk College of Business, MS Math Finance Program).
Stephen holds a BA in Economics from LIU, CW Post College, MBA in Finance from The George Washington University, MS in Finance from The George Washington University, MS in Predictive Analytics (Data Science) from Northwestern University, and a Doctorate in Engineering (D.Eng.) from The George Washington University, School of Engineering and Applied Science (SEAS), with coursework focused on systems engineering and methods of operations research. Dr. Young has earned the right to use the Professional Risk Manager (PRM) designation and has earned the Certificate in Quantitative Finance (CQF).
Mahmut Rustem Sen is part of ALM and Policy (CMIAP,) focusing on issues and policies that impact the balance sheet dynamics. Before joining CMIAP, he was the head of the Portfolio Analytics in QSA responsible for i) providing analytics to investment, funding, and ALM desks; ii) capacity building for central banks, sovereign pension and oil funds; iii) risk analysis for multi-asset portfolios; iv) quantitative and financial solutions and frameworks for internal and external partners. His experiences include asset management; market, credit, counterparty, liquidity, and enterprise risks; risk budgeting; ALM; economic capital; capital adequacy; RAROC; structuring; and central bank reserve management.
Prior to joining the WB, he was Financial Analysis Manager and Senior Risk Research Analyst at Freddie Mac, providing analytics on MBS, ALM, hedging, economic capital, and regulatory capital; teaching assistant at the George Washington University; and intern at Salomon Smith Barney.
He holds MS Finance from the George Washington University, BSc in Industrial Engineering from Marmara University, and completed “Leadership for the 21st Century: Chaos, Conflict & Courage” from Harvard Kennedy School Executive Education. He is a PRM charter holder from PRMIA.
Ryan Henning is the Head of Financial Engineering at Freddie Mac, a position he has held for most of his 24 year career. In this role he oversees the implementation of the firm's models and risk systems, as well as model governance and model data engineering for a $3+T balance sheet that helps provide stability and liquidity for our nation's housing stock.
Models and systems produced by Financial Engineering support automated underwriting, distressed borrower and property decisions, capital markets trading, debt issuance and redemption, market and credit risk management, collateral valuation, credit risk transfer, hedge accounting, loan loss reserves, regulatory capital, stress testing, balance sheet forecasting, and corporate strategy. The Enterprise Model Governance function works across all lines of defense to ensure a safe and sound environment for model development, implementation, and use across all business lines, which includes the governance of AI/ML. The Data Engineering function supplies curated data sets for the construction of models and supports the modelers with data quality and model monitoring services.
Mr. Henning has degrees in Computer Science, Finance and Accounting from St. Bonaventure University, and a Masters in Computational Finance from Carnegie Mellon University. He also holds CFA and FRM designations.
Steve Holden is Fannie Mae's Senior Vice President-Single-Family Analytics, reporting to the Executive Vice President – Single Family Business.
Holden leads a group of data science professionals that support loan underwriting, pricing and acquisition, securitization, loss mitigation, and loan liquidation for the company’s multi-trillion-dollar Single-Family mortgage portfolio. He also holds Enterprise-wide accountability for all Generative AI initiatives at Fannie Mae. His team delivers real-time analytic solutions, guiding the thousands of daily business decisions needed to manage a mortgage portfolio of this magnitude. The team includes experts in econometric models, machine learning, data engineering, data visualization, software engineering, and analytic infrastructure design.
Experience: Previously, Holden was Vice President – Credit Portfolio Management Analytics. Prior to joining Fannie Mae in 1999, he served in several analytic leadership roles. Holden spent the early part of his career working on economic issues at the Economic Strategy Institute and the U.S. Bureau of Labor Statistics.
Education: Holden has a Bachelor of Arts in economics from the University of Toronto, a Master of Arts in economics from Queen's University, and a Doctorate in economics from Johns Hopkins University.
Svein is the Managing Director of Global Equities at Lockheed Martin Investment Management Company and Head of Internally Managed Equities (four Global Equity portfolios). The internally managed equity group consists of a team of 3 professionals in Bethesda/MD (PM + 2 analysts) and manage approximately US$ 850 million.
Prior to Lockheed Martin, Svein was the Director of Public Securities for Dow Chemical's Pension Investments group. At Dow he managed its in-house $880 million equity and $1.9Bn fixed income portfolios as well as serving on the pension plan's Investment Policy Committee.
Svein has 30 years of experience in global financial markets as an analyst, portfolio manager and strategist. Over his career he has managed European, EAFE, US and Global equity portfolios. He has served as Chairman of the Northern Trust Corporation's Global Asset Allocation Committee as well as International Portfolio Strategist at Driehaus Capital Management.
Svein is a dual US and Norwegian citizen. He received his Masters of Business Administration in 1997 from the University of Chicago with concentration in Finance, International Business, and Business Economics. He received his Bachelor's of Business Administration Magna Cum Laude in Finance from the Loyola University of Chicago in 1992. In 1987, he graduated from the Norwegian Army Infantry Military Academy and in 1985 he completed the Norwegian Army Officer Candidate School for Field Artillery.
6:00 PM Seminar Begins
7:30 PM Reception
140 West 62nd Street
New York, NY 10023
For Virtual Attendees: Please select Virtual instead of member type upon registration.
This presentation explains the Expectile, Conditional Value-at-Risk (CVaR)/Superquantile, Value-at-Risk (VaR)/Quantile and other risk measures in the framework of the Fundamental Risk Quadrangle (FRQ) theory. According to the FRQ, a Quadrangle includes four stochastic functions: Error, Regret, Risk, and Deviation. These functions are interconnected through a Statistic stochastic function. Risk is used in stochastic optimization, and Error is employed in the statistical estimation of the corresponding Statistic. The Expectile, similar to VaR and CVaR, is used in risk management. Quadrangles based on VaR and CVaR statistics are well-established. The Errors in these Quadrangles are minimized for VaR and CVaR estimation. We focus on the recently proposed Quadrangles based on Expectile. We examine properties of these Expectile Quadrangles, with a particular emphasis on the new Quadrangle, where the Expectile is both Statistic and Risk. This Expectile Quadrangle is based on the new Piecewise Linear Error, which is an alternative to the standard Asymmetric Variance Error. We have demonstrated the equivalence of Expectile regression with these two Errors. Linear regression with the Piecewise Linear Error is reduced to linear programming. Optimization of Expectile is reduced to convex and linear programming using the FRQ Regret Theorem. Moreover, the Kusuoka representation of Expectile, together with the FRQ Regression Theorem implies the equivalence of Expectile and Quantile regressions. Theoretical findings are validated with several case studies.
Stan Uryasev is Professor and Frey Family Endowed Chair of Quantitative Finance at the Stony Brook University.
He received his M.S. in Applied Mathematics from the Moscow Institute of Physics and Technology (MIPT), Russia, in 1979 and Ph.D. in Applied Mathematics from the Glushkov Institute of Cybernetics, Kiev, Ukraine in 1983. From 1979 to 1987 he held a research position at the Glushkov Institute. From 1988 to 1992 he was a Research Scholar at the International Institute for Applied System Analysis, Luxenburg, Austria. From 1992 to 1998 he held the Scientist position at the Risk and Reliability Group, Brookhaven National Laboratory, Upton, NY. From 1998 to 2019 he was the George and Rolande Willis Endowed Professor at the University of Florida, and the director of the Risk Management and Financial Engineering Lab.
His research is focused on efficient computer modeling and optimization techniques and their applications in finance and DOD projects. He published four books (two monographs and two edited volumes) and more than 130 research papers. He is a co-inventor of the Conditional Value-at-Risk and the Conditional Drawdown-at-Risk optimization methodologies. He developed optimization software in risk management area, including Drawdown and Credit Risk minimization.
His joint paper with Prof. Rockafellar on Optimization of Conditional Value-At-Risk in The Journal of Risk, Vol. 2, No. 3, 2000 is among the 100 most cited papers in Finance. Many risk management/optimization packages implemented the approach suggested in this paper (MATLAB implemented a toolbox).
Stan Uryasev is a frequent speaker at academic and professional conferences. He has delivered seminars on the topics of risk management and stochastic optimization. He is on the editorial board of a number of research journals and is Editor Emeritus and Chairman of the Editorial Board of the Journal of Risk.
We extract contextualized representations of news text to predict returns using the state-of-the-art large language models in natural language processing. Unlike the traditional word-based methods, e.g., bag-of-words or word vectors, the contextualized representation captures both the syntax and semantics of text, thus providing a more comprehensive understanding of its meaning. Notably, word-based approaches are more susceptible to errors when negation words are present in news articles. Our study includes data from 16 international equity markets and news articles in 13 different languages, providing polyglot evidence of news-induced return predictability. We observe that information in newswires is incorporated into prices with an inefficient delay that aligns with the limits-to-arbitrage, yet can still be exploited in real-time trading strategies. Additionally, we find that a trading strategy that capitalizes on fresh news alerts results in even higher Sharpe ratios.
Dacheng Xiu is Professor of Econometrics and Statistics at the University of Chicago Booth School of Business. His current research focuses on developing machine learning solutions to big-data problems in empirical finance. Xiu’s work has appeared in the Journal of Finance, Review of Financial Studies, Econometrica, Journal of Political Economy, the Journal of the American Statistical Association, and the Annals of Statistics. He has served as Co-Editor for the Journal of Financial Econometrics and has been on the editorial board as an Associate Editor for many prestigious journals, including the Review of Financial Studies, Journal of the American Statistical Association, Journal of Econometrics, and Management Science. He has received several recognitions for his research, including the Fellow of the Society for Financial Econometrics, Fellow of the Journal of Econometrics, AQR Insight Award, EFA Best Paper Prize, and Swiss Finance Institute Outstanding Paper Award. He has been recognized as one of Poets & Quants’ Best 40-under-40 Business School Professors of 2023. At Booth, he teaches a variety of courses related to FinTech, Big Data, and Statistical Inference to MBA, college, and PhD students. Xiu earned his PhD and MA in applied mathematics from Princeton University.
Topological Data Analysis (TDA) has emerged as a powerful methodology in time-series analysis and signal processing. TDA is able to provide detailed descriptions of complex data which complements statistical methods. Recent applications include detection of critical transitions in financial time series, particularly of financial bubbles. The methodology relies on time-delay coordinate embedding, which is used to construct, from the time-series, a point-cloud in some space. The dynamics on the point-cloud unveils patterns in the time-series. Most of the evidence so far on the adeptness of TDA to detect financial bubbles has been empirical. We present, for the first time, a heuristic argument for why TDA can detect financial bubbles. We use models from economics that assert that the time series exhibit certain oscillatory patterns when approaching a tipping point. These oscillations determine holes in the point-clouds, which can be quantified by TDA. When approaching the tipping point of a bubble, there are significant changes in the nature of the oscillations, and consequently in the TDA output. These changes can be captured via persistence homology and yield early warning signals. As an application, we illustrate this approach on a sample of positive and negative bubbles in the Bitcoin price.
Marian Gidea is a professor of mathematics at Yeshiva University in New York City. He held previous appointments at the Mathematical Sciences Research Institute in Berkeley, the Institute for Advanced Study in Princeton, Centre de Recerca Matemàtica in Barcelona, Northeastern Illinois University, Northwestern University, and Loyola University Chicago. He also served at the National Science Foundation as a program director in the Mathematical Sciences Division. His research interests include Dynamical Systems, Topological Data Analysis, and Financial Mathematics.
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