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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.


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.
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, Wells Fargo

Title: Conformal Symmetry and Projective Geometry in Application to Stochastic Rates and Multi-Currency Models

Volatility is called unspanned if it can be dynamically separated from analytical representation of the underlying observables, such as swap or market rates. This quality is paramount for ecient calibration and pricing. Conformal symmetries provide a powerful tool for building parsimonious models of this kind. However, in this family of models, only common scale of volatility is unspanned. This limits the model calibration exibility, particularly, in the low rate regime. Here, we demonstrate how to overcome these restrictions. This is achieved via application of projective geometry and abstract algebra.
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

Speaker: Douglas W. Dwyer, Moody’s Analytics
Douglas W. Dwyer, Managing Director, heads the Single Obligor Research Group in Moody’s Analytics Quantitative Research Group. This group produces credit risk measures of corporations and financial institutions worldwide. Doug has been with Moody’s Analytics for 17 years, and he has worked to develop the company’s public and private expected loss models (e.g., PD, LGD and EAD). The group also helps lenders make effective use of these models. A recent focus of the group is the application of risk models to the stress testing of bank portfolios. He and his team are currently working on understanding how behavioral and other non-financial information improves risk assessment when combined with traditional financial data. This work can have useful application in the context of assessing credit risk for small businesses, stress testing, and computing ALLL.

Prior to working at Moody’s Analytics, Doug was a Principal at William M. Mercer, Inc., in their Human Capital Strategy practice. He earned a Ph.D. in Economics at Columbia University and a B.A. in Economics from Oberlin College.

Title: Validating and Understanding a Highly Non-Linear Machine Learning Model

This presentation goes through the exercise of asking: How much better would you do if you used a “highly nonlinear model” to predict defaults instead of a “traditional nonlinear model” while holding the risk drivers and sample fixed. Also, how do you assess better? And how do you achieve transparency? Finally, it shows how one can combine the two approaches.
Tuesday, March 12, 2019

Speaker: Ashish Misra, Aperture Investors
Ashish Misra is Chief Risk Officer at Aperture Investors. For over 20 years, Ashish has specialized in mathematical modeling of corporate and structured credit and other asset classes. Most recently, he served as a Managing Director at DW Partners, where he was the Head of the Analytics Group and a member of the Management Committee. Prior to DW Partners, Ashish worked at BlueMountain Capital where he developed systematic credit versus equity volatility strategies. He started his career at Morgan Stanley, where he was part of the Global High Yield and Emerging Markets desk and later the credit products quantitative group

Ashish earned his Ph.D. in Aeronautics from Caltech and his M.E. in Mechanical Engineering from Indian Institute of Science.

Title: Examination of excess returns of a small stock tracking portfolio

When a fund manager is paid on outperformance with respect to a benchmark, the excess returns are a measure of the managers P&L and also a measure of its active risk. We look at the distribution of excess returns of a small sized portfolio of stocks which aims to track a benchmark. We look at balanced portfolios where the percentage of market value allocated to each stock is held constant. Low pairwise correlation across the stocks and a low-to-moderate average correlation of single names to the benchmark could still yields a fairly high correlation of the portfolio to the benchmark. Furthermore, despite high correlation, tracking and performance, as de ned by the drawdown for instance, is diminished when the portfolio volatility deviates from the benchmark volatility on either side. The distribution of the excess returns shifts left with increasing portfolio volatility. Therefore tail risk increases for two reasons: (a) increase in portfolio volatility impacts the drift terms by shifting it to the left, and (b) the deviation of the portfolio volatility from the benchmark widens the excess return distribution.
Thursday, March 14, 2019

Speaker: Vladimir Markov, Bloomberg LP

Title: Design and performance analysis of execution trading strategies.

In this talk we discuss a set of practical problems and solutions relevant for designing and implementing sell-side equity trading strategies.

We present a practical design of schedule-based trading strategies (VWAP, Participation, and Implementation Shortfall) in uncertainty bands framework. The framework allows to cleanly separate high-level scheduling from low-level execution tactics.

The critical piece of any algorithm is limit order tactics. We quantify a general limit order tactics with the expected fill price, adverse price selection, and opportunity costs.

We show that liquidity seeking algorithms can be effectively modeled as multi-armed bandit problem.

In the last part of the talk, we discuss application of Bayesian methods for implementation shortfall and VWAP density and expected value estimation with the goal to build a generative model that takes into account fat-tails, heteroscedasticity, and skewness. It allows measuring the expected value of trading benchmarks from a small data sample and doing probabilistic ranking of broker algorithms.

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

Speaker: Irene Aldridge, ABLE Alpha Trading and
Irene Aldridge is President and Managing Director, Research, of AbleMarkets, a Big Data for Capital Markets company. Prior to AbleMarkets, Aldridge designed and ran high-frequency trading strategies in a $20-million cross-asset portfolio. Still previously, Aldridge was, in reverse order, a quant on a trading floor; in charge of risk quantification of commercial loans; Basel regulation team lead; technology equities researcher; lead systems architect on large integration projects, including web security and trading floor globalization. Aldridge started her career as software engineer in financial services. Aldridge is the author of multiple academic papers and several books. Most notable titles include “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (Wiley, 2017), “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013) and “Big Data Science in Finance: Mathematics and Applications” (forthcoming).

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, Federal Reserve Bank of New York
Wei Lu is a capital market risk manager in the Supervision Group at the Federal Reserve Bank of New York. He leads a team of both market risk and model risk examiners that critically evaluate trading and market risk measurement and management frameworks of certain large, complex financial institutions regulated by federal reserve system. Before joining New York Fed in March 2011, he had 10 years of experience in the financial industry with a focus on quantitative risk analytics and management.

Title: Market Risk Regulatory Capital Modeling Framework: A Supervisory Perspective

The presentation will provide an overview of regulatory modeling framework in trading book, which includes historical evolution, advancements since 2008 crisis, key challenges in market risk capture, Basel 2.5 market risk rule, and the latest developments in BCBS Trading Book Fundamental Review and certain implementation perspectives.
Tuesday, April 2, 2019

Speaker: Julien Guyon, Bloomberg L.P. and Columbia University
Julien Guyon is a senior quantitative analyst in the Quantitative Research group at Bloomberg L.P., New York. He is also an adjunct professor in the Department of Mathematics at Columbia University and at the Courant Institute of Mathematical Sciences, NYU. Before joining Bloomberg, Julien worked in the Global Markets Quantitative Research team at Societe Generale in Paris for six years (2006-2012), and was an adjunct professor at Universite Paris 7 and Ecole des ponts. He co-authored the book Nonlinear Option Pricing (Chapman & Hall, CRC Financial Mathematics Series, 2014) with Pierre Henry-Labordere. His main research interests include nonlinear option pricing, volatility and correlation modeling, and numerical probabilistic methods. Julien holds a Ph.D. in Probability Theory and Statistics from Ecole des ponts. He graduated from Ecole Polytechnique (Paris), Universite Paris 6, and Ecole des ponts. A big soccer fan, Julien has also developed a strong interest in sports analytics, and has published several articles on the FIFA World Cup, the UEFA Champions League, and the UEFA Euro in top-tier newspapers such as The New York Times, Le Monde, and El Pais, including a new, fairer draw method for the FIFA World Cup.

Title: On the Joint Calibration of SPX and VIX Options

Since VIX options started trading in 2006, the joint calibration of SPX and VIX option markets has been a very challenging problem. In particular, the very large negative skew of short-term SPX options, which in continuous models implies a very large volatility of volatility, seems inconsistent with the comparatively low levels of VIX implied volatilities.

In this talk we investigate the following question: Does there exist a continuous model on the SPX that jointly calibrates to SPX options, VIX futures, and VIX options? We present a novel approach based on the SPX smile calibration condition. In the limiting case of instantaneous VIX, a novel application of martingale transport to finance shows that such model exists if and only if, for each time t, the local variance and the instantaneous variance are in convex order. The real case of a 30 day VIX is more involved, as averaging over 30 days and projecting onto a filtration can undo convex ordering.

We show that, in usual market conditions, the distribution of the squared VIX in the local volatility model and the market-implied distribution of the squared VIX are in reversed convex order for short VIX maturities, not rankable for intermediate maturities, and in convex order for longer maturities. We numerically show that (a) stochastic volatility models with large mean reversion and large volatility of volatility, and (b) rough volatility models with small Hurst exponent, can reproduce this term-structure of convex ordering of the local and stochastic squared VIX. In particular they satisfy what we call the inversion of convex ordering property at short maturities. We also mathematically prove that inversion of convex ordering can be produced by continuous stochastic volatility models.

We suggest two numerical approaches for fitting both the SPX and VIX smiles with continuous models, one where we first fit the VIX smile and futures and then try to fit the SPX smile, the other where we first fit the SPX smile thanks to a leverage function. We test them using the skewed two-factor Bergomi model, as well as a new model, the skewed rough Bergomi model. It turns out that it is extremely difficult to fit both SPX skew and VIX implied volatilities with continuous models, which seems to indicate either the need for more complicated / less constraining models. Our recent expansion of VIX futures in Bergomi models sheds light on the structural constraints that these models impose. Inspired by the recent work of De March and Henry-Labordere (2019), we use a dispersion-constrained martingale transport approach and the Sinkhorn algorithm to build a discrete-time model consistent with SPX/VIX option market data.

Thursday, April 4, 2019

Title: Introduction to Dispersion and Correlation Trading

Speaker: Jonathan Assouline, J.P. Morgan
Jonathan Assouline is an Executive Director, Equity Derivatives Trader at JPMorgan Chase in New York where he is responsible for the US Volatility Relative Value including the Dispersion Trading. Before this, he was working at Société Générale for 10 years leading and developing the Corporate Trading, the Dispersion Trading and the Single Names Exotics. He graduated from Ecole des Ponts et Chaussees in 2006 and obtained a Master of Mathematics of Finance from Columbia University in the same year.

The equity options market allows the assessment of an implied correlation in a region or a sector. We will clarify what this parameter represents and how one can benefit from its implied moves or its realization.
Tuesday, April 9, 2019

Speaker: Natalia Zvereva, JP Morgan Asset Management
Natalia Zvereva is a Vice President in the Investment Risk team at JP Morgan Asset Management. Natalia and her team focus on risk management and oversight of the Liquid Alternatives Funds, Hedge Funds, and Beta strategies, including ETF business. Natalia has been working in risk management since 2009, and held a number of roles in JP Morgan within market risk and credit risk space. Prior to her current position, she covered counterparty risk, credit and funding pricing of cross-asset derivatives portfolios across Rates, FX, Equities, Credit and Commodities. Prior to that, Natalia was a market risk manager in OTC Derivatives Clearing for 4 years, where she helped to facilitate the launch of the OTC Clearing business. Before joining JP Morgan is 2011, Natalia was a market risk manager at MF Global. Natalia is an analyst and associates champion for JPM Asset Management Risk in North America. She created a training curriculum and organized global technical training and networking events for analysts, associates, and interns in Asset and Wealth Management throughout the year. Natalia holds a Master’s Degree in Financial Mathematics from Columbia University (2014) and BBA in Finance & Investments from Baruch College (2009).

Title: Introduction to Factor-Based Investing

The concept of factors, identified in the ‘60s by W. Sharpe, became increasingly popular in recent years as systematic approaches have been developed to capture factors with Exchange Traded Funds (ETFs) using rules-based transparent strategies. In this lecture, we will cover an introduction to factor-based investing including factors modeling, practical considerations and limitations, factors beyond equities and how factor-based approach can be implemented with ETFs.
Thursday, April 11, 2019

Speaker: Kelly Ye, Index IQ
Kelly Ye, CFA, is Director of Research at IndexIQ, the ETF platform for New York Life Investments. She oversees research and new product development across IndexIQ ETF family.

Before joining IndexIQ, Kelly is Director and head of Quantitative Investment Strategy in New York Life Investment Management’s Strategic Asset Allocation Team. Kelly joined NYLIM in 2015 from Goldman Sachs Asset Management where she was the head quant on the credit investment team. She was responsible for all bottom up and top down research models as well as portfolio construction tools. Prior to that role, Kelly was a senior Fixed Income Quant Strategist in Goldman Sachs credit trading desk covering various Fixed Income instruments including Bank Loans, Invest Grade and High Yield corporate bonds, Credit Default Swaps and Municipal bonds. Kelly began her career as a Credit Derivative Structurer at BNP Paribas, designing structured credit solutions for institutional and private bank clients.

Kelly has a dual master’s degree in Operations Research and Financial Engineering from Massachusetts Institute of Technology and a B.S. in Applied Math from Peking University in China. She is a CFA charter holder and serves on the board of CFA Society New York.

Title: The Art and Science of Hedge Fund Replication

This presentation gives an overview of the hedge fund replication problem, its history, validity and the science behind hedge fund replication algorithms. Starting from the economic rationale of why hedge fund replication is needed, from the challenge asset owners face when allocating to hedge funds; then the theory established 20+ years ago showing the factor drivers of hedge fund returns, followed by a deeper dive of the algorithms practitioners use to replicate hedge funds. There will also be a discussion at the end about the advanced techniques that could be used to improve the replication results.
Tuesday, April 16, 2019

Speaker: Ilya Zhokhov, JP Morgan
Ilya Zhokhov is currently Executive Director at Chief Investment Office in JP Morgan focusing on risk management of mortgage servicing rights portfolio. Prior to the current role Ilya spent a number of years at Blackrock and was responsible for managing relationships with banks and financial institutions. His team provided risk management and strategic advisory services to many of the nation’s largest banks and financial institutions.

Title: Introduction into MBS markets and instruments

Thursday, April 18, 2019

Speaker: Arturo Cifuentes, Columbia Business School and CLAPES-UC

Arturo divides his time between New York and Chile. In New York, he is an Adjunct Professor at the Division of Finance & Economics of Columbia University. In Chile, he is a Research Associate at CLAPES-UC, a research/public policy center affiliated with the Catholic University (PUC). He also advises the Chilean Association of Insurance Companies on financial regulation issues, and, a US-based alternative assets investment fund.

Previously, he served three years as a member and President of the Chilean Sovereign Fund investment committee (US$ 25 billion); and four years as a member of the Advisory Board of the Division of Humanities and Social Sciences of the California Institute of Technology (Caltech). He also participated in the Financial Regulation Reform Commission that was appointed by the Chilean Minister of Finance (2010-2011).

As a result of the subprime financial crisis, he was invited twice to testify, as expert witness, by the U.S. Senate. He has also been consulted by the U.S. Congress, the U.S. Treasury, and the Connecticut State Insurance Commissioner.

Arturo holds a Ph.D. in applied mechanics and a M. S. in civil engineering from the California Institute of Technology (Caltech); an MBA in finance from New York University (Stern scholar award); and a civil engineering degree from the University of Chile.

Title: Pension Funds and Portfolio Management: Some Interesting Lessons from Chile & Mexico

Mexico and Chile implemented a few years ago pension systems based on a similar Defined Contribution (DC) concept. The idea was to offer the affiliates several investment choices (funds) based on their risk profile. The thought was that younger workers should select the riskiest fund while workers close to their retirement age should go with the most conservative fund. These countries, however, chose a different type of regulation to implement this idea.

The empirical evidence is that the Chilean regulation was a failure while the Mexican regulation was a success. In this talk we will examine the reasons behind this situation, and we will discuss some broader implications for pension funds and long-term investment vehicles in general.

Tuesday, April 23, 2019

Speaker: Samim Ghamami, Goldman Sachs
Samim Ghamami is a Senior Financial Economist at Goldman Sachs, an Adjunct Professor of Finance at New York University, and an Adjunct Associate Professor of Economics at Columbia University. He received his Ph.D. in Mathematical Finance and Operations Research from the University of Southern California in 2009.

Ghamami has been an Associate Director (Acting) and a Senior Research Economist at the U.S. Department of the Treasury, Office of Financial Research, an Economist at the Board of Governors of the Federal Reserve System and an Advisor to the Basel Committee on Banking Supervision. Ghamami has worked as an expert with the Financial Stability Board on the review of OTC derivatives market reforms in 2016 and 2017. He has also served on the National Science Foundation panel on Financial Mathematics in 2017 and 2018.

Ghamami has also been a Visiting Scholar at the Department of Economics at UC Berkeley and a Post-Doctoral Researcher at CREATE Homeland Security Center. His publications have appeared in different journals including Management Science, Journal of Financial Intermediation, Journal of Applied Probability, Mathematics of Operations Research, Journal of Derivatives, and Quantitative Finance.

Title: Collateralized Networks

This paper studies the spread of losses and defaults through financial networks focusing on two important elements of regulatory reforms: collateral requirements and bankruptcy stay rules in over-the-counter (OTC) markets. Under \segregated” collateral requirements, one firm can benefit from the failure of another, the failure frees the committed collateral of the surviving firm giving it additional resources to make other payments. In OTC derivatives markets, similarly, one firm may obtain additional resources upon the failure of another if it terminates its in the money derivatives with the failed entity. Studying contagion in the presence of this real world phenomenon becomes challenging, our proposed model deviates from the existing network models to capture collateral and accelerated contract termination payments. The model also incorporates fire sales externalities when collateral is held in illiquid assets. We show that asset fire sales increase the risk of contagion if illiquid collateral is seized and sold immediately upon defaults. We also analyze the impact of different stay rules on contagion. Some of our results contrast with the post-crisis stay rules. For instance, we show that when banks are not highly leveraged in terms of their OTC derivatives transactions, symmetric contract termination in the absence of automatic stays can reduce the risk of contagion.
Thursday, April 25, 2019

Speaker: Vladimir Sankovich, DRW
Dr. Vladimir Sankovich is a quantitative analyst with over 20 years of experience in modeling of interest rates derivatives and fixed income products. He currently works at DRW – a private capital trading company – where he leads a team of quantitative researchers and software engineers responsible for the development of a new global analytics framework. Prior to joining DRW Dr. Sankovich worked at several major financial institutions, most recently as a managing director and head of quantitative modeling and analytics at TD Securities, and before that as a Global Head of FICC Quants at RBC Capital Markets. Before starting his career in Finance, Dr. Sankovich has earned a Ph.D. degree in Theoretical Physics from New York University.

Title: Designing a modern analytics library: elements of software- and financial engineering

The presentation will discuss some of the challenges and decisions involved in the design and implementation of a large-scale analytics library. We will analyze modeling choices as well as architectural/engineering aspects of the design based on real life examples and requirements. We will discuss all phases of the project from its initial design to modeling choices, to testing and interfacing the library with other systems and libraries and point out the pros and the cons of various potential approaches.
Tuesday, April 30, 2019

Speaker: Amal Moussa, Director, Equity Derivatives Trading at Citi
Amal Moussa is a derivatives trader at Citi where she is in charge of the North America Stocks Exotics and Dispersion business. She has a Ph.D. in Statistics from Columbia University. Prior to her Ph.D., Amal graduated with a Masters in Mathematical Finance from Paris VI University and a Grande Ecole engineering degree from Ecole Nationale Supérieure des Télécommunications.

Title: A Quick Peek into the Equity Vol Market
Thursday, May 2, 2019

Speaker: Aleksandr Veygman
Alexander Veygman has been a leading fixed income desk quantitative analyst at HSBC for the past 12 years working on valuation of various kinds of vanilla and exotic interest rates and credit hybrids derivatives. His scope of interest includes researching numerical methods to simultaneously incorporate multiple market observables to develop practical models ready to be used by fixed income desks. He holds an MS degree from NYU in Statistics & Operations Research/Math in Finance.

Title: The Fall of Numéraire Probability Measure Search for Financial Models in the Post-Crisis World

Many financial engineering articles or books suggest solving financial engineering problems or building models by selecting an appropriate probability measure associated with an asset that is viewed as a kind of a new currency (numéraire). While in the per-crisis world with its uniform funding assumptions it worked sufficiently well, applying this approach in the current world is becoming less and less straightforward. Presently, even in theory a global noarbitrage assumption does not always work anymore because of emergence of all
kinds of basis (e.g. cross-currency, tenor, etc.). The funding costs assumptions became more versatile as well and they are no longer uniform across financial instruments.

Therefore we found that a useful and practical approach would be to directly consider the cashows from the instrument we price and its hedges (and these cashows vary depending on the location of the market). This will prompt us to build a valuation PDE (Partial Dierential Equation). Only then, based on what its solution looks like we can start choosing probability measure if needed.

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