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Practitioners’ Seminar 2019

The seminar takes place in the Spring of 2019, Tuesdays and Thursdays 7:40pm — 8:55 pm.

Location: 312 Mathematics Building. For directions please see Directions to Campus and Morningside Campus Map

Organizer: Lars Tyge Nielsen

Click here for the Schedule of Presentations.

Click here for the Schedule of Past Presentations.

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  • The seminar is open to the public (no registration necessary).
  • The speaker often will not make copies of the presentation available — to protect intellectual property or comply with company rules.
  • No photos or video of the speaker or presentation allowed except with express permission.
  • Only to document the attendance of MAFN students, the audience may be photographed at the beginning of the seminar, and sign-up sheets may be circulated


Schedule of Presentations

Click here for the Schedule of Past Presentations.

Thursday, February 21, 2019

Speaker: Leon Xin, J.P. Morgan
Leon Xin is the Head of Risk and Portfolio Construction and Hedge Fund Strategist for the CIO team of the Endowments and Foundations Group at JP Morgan. Mr. Xin joined J.P. Morgan in 2016 and has 13 years of investment industry experience. Prior to J.P. Morgan, Mr. Xin worked for over 10 years as the Head of Alternative Investment Risk at UBS Asset Management, where he covered UBS O’Connor, an internal multi-strategy hedge fund. Prior to UBS, Mr. Xin worked as an associate in Ping An Insurance of China for two years on strategic planning projects. Mr. Xin receives a M.S. degree on Applied Math from the University of Illinois at Chicago and is a CFA charter holder.

Title: Identify Macro Risk Factors in Equity Long/Short Funds

Commercial factor risk models are powerful in measuring risk of typical long only equity portfolios, but sometimes fail to capture the systematic risk in carefully managed equity long short and especially market neutral portfolios. I propose a framework to identify risk factors in equity long short portfolios, by combining existing risk management process with ad-hoc models.
Tuesday, February 26, 2019

Speaker: David-Antoine Fournie
David Fournie heads the Equity Exotics trading desk at Deutsche Bank NY. Prior to this, he had two stints as a senior Exotics and Dispersion trader at Morgan Stanley, and co-founded the volatility hedge fund manager Deauville Capital Management LLC. He graduated from Ecole Polytechnique and obtained a Ph.D. in Mathematics from Columbia University for his extension of Ito’s formula to non-anticipative functionals of the whole path.

Title: Payoff replication with vanilla options and dynamic delta

We will show how to replicate terminal distribution payoffs, variance swaps and conditional variance swaps, with vanilla
options and delta
Thursday, February 28, 2019

Speaker: Gregory Pelts


Tuesday, March 5, 2019

Speaker: Marco Avellaneda, NYU Courant Institute
Marco Avellaneda (PhD, University of Minnesota, 1985) started his academic career at New York University’s Courant Institute where he has been a member of the faculty since 1988. His research interests include applied mathematics, mathematical finance, econometrics, derivative securities, investment theory and risk-management. He is best known for the Uncertain Volatility Model for option pricing and his contributions to the formulation of quantitative trading strategies, such as statistical arbitrage, correlation trading, and automated market-making. Marco has held positions in Morgan Stanley, Capital Fund Management and Galleon Group. In 2007 he founded a risk-management consulting firm, Finance Concepts.

Title: Statistical Arbitrage: Back to the Future?

Thursday, March 7, 2019



Tuesday, March 12, 2019

Speaker: Ashish Misra, Aperture Investors


Thursday, March 14, 2019

Speaker: Vladimir Markov


Tuesday, March 19, 2019 — Spring Recess, no seminar
Thursday, March 21, 2019 — Spring Recess, no seminar
Tuesday, March 26, 2019

Speaker: Irene Aldridge, AbleMarkets and Cornell University

Title: Big Data Techniques in Finance: Beyond Econometrics and Data Mining

Big Data is often considered an extension of Econometrics. This talk gives a survey of now-mainstream Big Data techniques just gaining the traction in Finance that extend far beyond traditional data analysis. The Big Data techniques discussed do away with rigidity and limitations of Econometrics, provide in-depth inferences often without the need for hypotheses, and lay foundation for the true artificial intelligence models poised to revolutionize the field of Finance in the next 5-10 years.
Thursday, March 28, 2019

Speaker: Wei Lu


Tuesday, April 2, 2019

Speaker: Julien Guyon


Thursday, April 4, 2019

Speaker: Jonathan Assouline


Tuesday, April 9, 2019

Speaker: Natalia Zvereva, JP Morgan


Thursday, April 11, 2019

Speaker: Kelly Ye, Index IQ

Title: The Art and Science of Hedge Fund Replication

Tuesday, April 16, 2019

Speaker: Ilya Zhokhov


Thursday, April 18, 2019

Speaker: Arturo Cifuentes


Tuesday, April 23, 2019

Speaker: Samim Ghamami


Thursday, April 25, 2019

Speaker: Vladimir Sankovich


Tuesday, April 30, 2019

Speaker: Amal Moussa


Thursday, May 2, 2019

Speaker: Aleksandr Veygman




Past Presentations

Tuesday, January 22, 2019

Speaker: Peter Carr, NYU Tandon School of Engingeering, Finance and Risk Engineering
Dr. Peter Carr is the Chair of the Finance and Risk Engineering Department at NYU Tandon School of Engineering. He has headed various quant groups in the financial industry for the last twenty years. He also presently serves as a trustee for the National Museum of Mathematics and WorldQuant University. Prior to joining the financial industry, Dr. Carr was a finance professor for 8 years at Cornell University, after obtaining his Ph.D. from UCLA in 1989. He has over 85 publications in academic and industry-oriented journals and serves as an associate editor for 8 journals related to mathematical finance. He was selected as Quant of the Year by Risk Magazine in 2003 and Financial Engineer of the Year by IAQF/Sungard in 2010. From 2011 to 2014, Dr. Carr was included in Institutional Investor’s Tech 50, an annual listing of the 50 most influential people in financial technology.

Title: What Does an Implied Volatility Mean?

We use a market model of implied volatility to develop an implied volatility smile. The implied variance rate is given a simple probabilistic representation
Thursday, January 24, 2019

Speaker: Walter Schachermayer, University of Vienna and Columbia University
Walter Schachermayer is Professor Emeritus of Mathematics at the University of Vienna and presently visiting Professor at Columbia University. His research is concerned with stochastic processes and their applications in finance.

Walter Schachermayer was the first mathematician to receive the Wittgensteinpreis (1998), the highest scientific distinction in Austria. He is a member of the German National Academy of Science Leopoldina and the European Academy of Science. Professor Schachermayer holds an honorary doctorate from Université Paris Dauphine and Universidad de Murcia. In 2009 he was awarded an ERC Advanced Grant.

Among his achievements is the proof of the “Fundamental Theorem of Asset Pricing”
in its general form, which was done jointly with Freddy Delbaen.

Title: The Amazing Power of Dimensional Analysis in Finance: Market Impact and the Intraday Trading Invariance Hypothesis

A basic problem when trading in financial markets is to analyze the prize movement caused by placing an order. Clearly we expect – ceteris paribus – that placing an order will move the price to the disadvantage of the agent. This price movement is called the market impact.

Following the recent work of A. Kyle and A. Obizhaeva we apply dimensional analysis – a line of arguments wellknown in classical physics – to analyze to which extent the square root law applies. This universal law claims that the market impact is proportional to the square root of the size of the order.

We also analyze the dependence of the trading activity on a stock, i.e. number of trades per unit of time, in dependence of some suitable explanatory variables. Dimensional analysis leads to a 2/3 law: the number of trades is proportional to the power 2/3 of the exchanged risk.

The mathematical tools of this analysis reside on elementary linear algebra.

Joint work with Mathias Pohl, Alexander Ristig and Ludovic Tangpi.

Tuesday, January 29, 2019

Speaker: Leon Tatevossian, NYU Courant Institute and NYU Tandon School of Engineering
Leon Tatevossian is an adjunct instructor in Mathematics in Finance at NYU Courant and in Finance and Risk Engineering at NYU Tandon. From 2009-16 Leon was a director in Group Risk Management at RBC Capital Markets, LLC, where he covered market risk for securitized products in secondary-trading, origination, and proprietary-trading areas. He has thirty years of sell-side experience in the fixed-income markets, including positions as a trader, quantitative strategist, derivatives modeler, and market-risk analyst. Leon’s product background includes US Treasury securities, US agency securities, interest-rate derivatives, MBSs, ABSs, and credit derivatives. He graduated from MIT (SB; mathematics) and was a graduate student in mathematics (algebraic number theory) at Brown University.

Title: Credit Risk Transfer (CRT) Mortgage Bonds: Slicing/Leveraging Homeowner Credit Risk

Within its oversight role (conservatorship) of Fannie Mae and Freddie Mac one of the Federal Housing Finance Agency’s ongoing objectives is restoring greater balance in the ownership of credit risk within the single-family mortgage universe. In 2012 the Agency released guidelines (“Credit Risk Transfer”) for distributing this loss exposure towards private capital (and away from the US taxpayer). Implementation (launched in 2013) has comprised a blend of capital-markets transactions, pool-level reinsurance contracts, and loan-specific mortgage insurance (on higher-LTV loans).

From 2013 through 2017 a portion of the credit risk on $2.1 trillion of outstanding principal on single-family loans has been sold into private hands; capital-markets vehicles (Fannie and Freddie debentures with principal payments linked to reference-pool loss realizations) have been the dominant strategy; these “CRT mortgage bonds” now constitute an actively-traded segment of the “credit MBS” sector.

Relative-value and risk analysis of CRT bonds references fundamental techniques from mortgage modeling as well as the basic underpinnings and rationale of structured finance (tranching, leverage). The talk will describe how mortgage strategists view this asset class, attempt to rationalize spread performance, and examine pricing in line with the unlevered pricing of mortgage default risk (g-fee).

Thursday, January 31, 2019

Speaker: Yury Blyakhman, J.P. Morgan

Title: Mathematical Modeling in Emerging Markets. Practical Aspects
Yury Blyakhman is a Managing Director at JPMorgan Chase in New York where he heads Linear Rates and FX Quantitative Research globally across Developed and Emerging Markets. Team is responsible for development and support of all pricing and risk management models across regions, businesses and asset classes. Yury has been with J.P. Morgan since 2004. Before that, from 2001 to 2004, Yury was part of Fixed Income Research (FIRST) team at BNP Paribas doing Interest Rates modeling. Yury holds Ph.D. in Physics from NYU.

The talk will introduce rarely discussed subject of financial mathematical modeling in Emerging Markets. It will present peculiar and special products, go over few practical modeling aspects and review some latest trends in the field
Tuesday, February 5, 2019

Speaker: Fabio Mercurio, Bloomberg L.P.
Fabio is global head of Quantitative Analytics at Bloomberg LP, New York. His team is responsible for the research on and implementation of cross-asset analytics for derivatives pricing, XVA valuations and credit and market risk. Fabio is also adjunct professor at NYU, and a former CME risk committee member. He has jointly authored the book ‘Interest rate models: theory and practice’ and published extensively in books and international journals, including 18 cutting-edge articles in Risk Magazine. Fabio holds a BSc in Applied Mathematics from the University of Padua, Italy, and a PhD in Mathematical Finance from the Erasmus University of Rotterdam, The Netherlands

Title: SOFR So Far: Modeling the LIBOR Replacement

Even before the 2007 credit crunch, some dealers were manipulating IBORs in an attempt to make profits in the derivatives market. Investigations prompted by regulators found overwhelming evidence of this fraudulent practice, and skepticism around the reliability of IBOR indexes increased across the industry.

In June 2017, with the goal of proposing a new interest rate benchmark, the US Alternative Reference Rates Committee (ARRC) announced that they had identified a Treasuries repo financing rate, which they called Secured Overnight Funding Rate (SOFR), as the best replacement for LIBOR. Starting on April 3, 2018, the New York Fed has published the SOFR on the New York Fed website every business day. Following the publication of this new interest rate benchmark, the market began trading SOFR-based derivatives and cash instruments.

The existing data on SOFR and SOFR-based derivatives leads to the creation of a SOFR interest-rate curve that can be stripped and extrapolated using available quotes. In this talk, we introduce a simple multi-curve interest-rate model that allows for the consistent calculation of the convexity adjustments for both SOFR futures and Eurodollar futures. We also introduce pricing formulas for SOFR-based swaps and show how the valuations of LIBOR-based swaps, as well as LIBOR-SOFR basis swaps, change because of the new LIBOR fallback introduced by ISDA.

Thursday, February 7, 2019

Speaker: Graham L Giller, MA (Oxon.), DPhil., Deutsche Bank
Graham is Head of US Primary Research at Deutsche Bank in New York. He has a doctorate from Oxford University in Experimental Elementary Particle Physics, where his field of research was statistical cosmic ray astronomy which featured large scale computer based data analytics. He joined Morgan Stanley in London in 1994 and was an early member of the now famous Process Driven Trading group run by Peter Muller (now “PDT Partners”). Subsequent to Morgan Stanley, he ran a small “friends and family” investment fund that specialized in systematic trading of financial futures. He was recruited to Bloomberg LP to run the Data Science within Bloomberg’s Global Data division and joined JP Morgan as Chief Data Scientist, New Product Development, in 2015, ultimately becoming Head of Data Science Research. He joined Deutsche Bank’s new Data Innovation Group (“dbDIG”) in March, 2018.

Title: Online Learning of Multicategory Response Probabilities with Autoregressive Dirichlet Distributions

In the 20th Century, much fundamental and quantitative research focused on either reference data, published by companies and government agencies, or market pricing data. In the 21st Century we can directly observe the inputs to companies and the economy by processing data gathered directly from the public. This data possesses more dimensions and higher resolution along those dimensions and can be used to anticipate publically released data. This talk discusses a methodology that can be used to extract macroeconomically informative data from online surveys that ask simple multiple-choice questions.
Tuesday, February 12, 2019

Speaker: Louis Scott, Federal Reserve Bank of New York
Louis Scott is currently at the Federal Reserve Bank of New York, where he is an Officer in the Model Risk Department, Supervision. Prior to joining the Fed in 2014, he was in the investment banking industry for 17 years where he was a Managing Director with Morgan Stanley and UBS. He held a variety of positions with responsibilities in risk management and quantitative research. He was a university professor in Finance at the University of Illinois and the University of Georgia and published academic research, including papers on derivative pricing. He has also served as an adjunct professor at the Courant Institute, New York University, and the Graduate Business School at Fordham University. His undergraduate degree is in Electrical Engineering from Duke University, and he has an MBA in Finance from Tulane University and a Ph.D. in Economics from University of Virginia.

Title: Parallel Processing for Pricing Financial Derivatives, with Applications for Interest Rate Derivatives and Equity Options on GPUs

Recent developments in computing technology coupled with massive parallel processing have produced significant advances in artificial intelligence, machine learning, robotics, and driverless cars. This same technology can be exploited for use in financial models. The presentation begins with an overview of massive parallel processing on general purpose graphical processing units (aka GPU’s) and how this technology can be applied to financial models. The overview is followed by applications with interest rate derivatives and equity options. The interest rate derivative example is a 3 factor version of the Hull-White model. The equity option example is pricing SPX and SPY options with a model that incorporates stochastic volatility and jump processes. Both examples include applications of Monte Carlo simulation and finite difference methods, implemented on GPU’s. The analysis includes performance tests (computing time tests) and a calibration of the model for both SPX and SPY options. The Monte Carlo solutions are applied to options with European style exercise, and the finite difference methods are applied to options with American style exercise. The application of massive parallel processing on GPU’s makes it possible to implement more complex financial models.
Thursday, February 14, 2019

Speaker: Mikhail Smirnov, Columbia University

Title: ETFs, Leveraged ETFs and Market Anomalies

ETFs became one of the fastest growing assets and now give exposure to different market sectors and strategies. We will discuss ETF market, its evolution and access to market anomalies it offers. Then we discuss leveraged ETFs that provide a convenient mechanism to dynamically change portfolio exposure. We show more complex dynamic portfolio strategies that also can be implemented using leveraged ETFs. We will introduce and discuss the notion of Dynamic Leverage as a VAR extending risk measure taking into account investment time horizon.
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