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Practitioners’ Seminar 2017

The seminar takes place in the Spring of 2017, Tuesdays and Thursdays 7:40pm — 8:55 pm.

Location: For now, 207 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

The Spring 2017 Practitioners’ Seminar is now closed. A big Thank You to all the speakers. We will return in the Spring of 2018.

Click here for the Schedule of Past Presentations.


Past Presentations

Tuesday, January 17, 2017

Title: Vol, Skew, and Smile Trading

Speaker: Peter Carr, NYU Tandon School of 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.

In general, an option’s fair value depends crucially on the volatility of its underlying asset. In a stochastic volatility (SV) setting, an at-the-money straddle can be dynamically traded to profit on average from the difference between its underlying’s instantaneous variance rate and its Black Merton Scholes (BMS) implied variance rate. In SV models, an option’s fair value also depends on the covariation rate between returns and volatility. We show that a pair of out-of-the-money options can be dynamically traded to profit on average from the difference between this instantaneous covariation rate and half the slope of a BMS implied variance curve. Finally, in SV models, an option’s fair value also depends on the variance rate of volatility. We show that an option triple can be dynamically traded to profit on average from the difference between this instantaneous variance rate and a convexity measure of the BMS implied variance curve. Our results yield precise financial interpretations of particular measures of the level, slope, and curvature of a BMS implied variance curve. These interpretations help explain standard quotation conventions found in the over-the counter market for options written on precious metals and on foreign exchange.
Thursday, January 19, 2017

Title: Rank-Based Portfolios, the “Size Effect”, and an Identity for the Exponential Distribution

Speaker: Ioannis Karatzas, Columbia University
Ioannis Karatzas obtained his Ph.D. degree in Mathematical Statistics at Columbia University, where he is the Eugene Higgins Professor of Applied Probability in the Department of Mathematics. He has had a long-standing association with the research group of the investment firm INTECH at Princeton, NJ.

He works and publishes in Probability, Stochastic Control, Sequential Analysis, Mathematical Economics and Finance. He has co-authored with Steven E. Shreve the book “Brownian Motion and Stochastic Calculus” and the monograph “Methods of Mathematical Finance”, both published by Springer-Verlag and both standard references in their respective fields. His 30 PhD students are on the faculties of Universities all over the world, or in various industrial positions. He started a very successful Master’s program on the Mathematics of Finance at Columbia, now in its twentieth year.

In an equity market with stable capital distribution, a capitalization-weighted index of small stocks tends to outperform a capitalization-weighted index of large stocks. This is a somewhat careful statement of the so-called “size effect”, which has been documented empirically and for which several explanations have been advanced over the years. We shall review the analysis of this phenomenon by Fernholz (2001) who showed that, in the presence of (a suitably defined) stability for the capital structure, this phenomenon can be attributed entirely to portfolio rebalancing effects and will occur regardless of whether or not small stocks are riskier than their larger brethren. Collision local times play a critical role in this analysis, as they capture the turnover at the various ranks on the capitalization ladder. We shall provide a rather complete study of this phenomenon in the context of a simple model with stable capital distribution. As a corollary we shall obtain an intriguing identity for the exponential distribution.
Tuesday, January 24, 2017

Title: Introduction into MBS Market and MBS Instruments

Speaker: Ilya Zhokhov, JP Morgan
Ilya Zhokhov is currently Vice President 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.

Mortgage Market and Mortgage Origination Process.
Mortgage market participants (the role of GSE, banks, real money and hedge funds).
Definition and types of MBS and pass-through securities.
Prepayment and other major risks associated with investing in MBS.
Thursday, January 26, 2017

Title: Structured MBS and MBS derivatives

Speaker: Ilya Zhokhov, JP Morgan

CMO and other types of structured MBS
MBS derivatives such as IO and PO strips
Main risks associated with structured MBS
Tuesday, January 31, 2017

Title: Bounds for VIX Futures Given S&P 500 Smiles

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.

We derive sharp bounds for the prices of VIX futures using the full information of S&P 500 smiles. To that end, we formulate the model-free sub/superreplication of the VIX by trading in the S&P 500 and its vanilla options as well as the forward-starting log-contracts. A dual problem of minimizing/maximizing certain risk-neutral expectations is introduced and shown to yield the same value. The classical bounds for VIX futures given the smiles only use a calendar spread of log-contracts on the S&P 500. We analyze for which smiles the classical bounds are sharp and how they can be improved when they are not. In particular, we introduce a tractable family of functionally generated portfolios which often improves the classical spread while still being tractable, more precisely, determined by a single concave/convex function on the line. Numerical experiments on market data and SABR smiles show that the classical lower bound can be improved dramatically, whereas the upper bound is often close to optimal.
Thursday, February 2, 2017

Title: Mathematical Modeling in Emerging Markets. Practical Aspects

Speaker: Yury Blyakhman, JP Morgan Chase
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. His 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 a few practical modeling aspects and review some latest trends in changing regulatory environment.
Tuesday, February 14, 2017

Title: The present of futures

Speaker: Fabio Mercurio, Bloomberg LLP
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 risk management. 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 16 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.

– The convexity conundrum in the old single-curve world
– The convexity conundrum in the new multi-curve world
– A multi-curve hybrid Cheyette-LMM model
– Solving two specific tractable cases
– Numerical examples
Thursday, February 16, 2017

Title: What can we learn from option market? Brexit, US Election, Earnings, FDA Approvals and More

Speaker: Bryan Liang, Bloomberg L.P. and Columbia University
Bryan Liang is a senior quant researcher at Bloomberg L.P. and adjunct assistant professor at Columbia University. He joined the Bloomberg quant research team in 2011 and has been working extensively on various aspects of derivatives modelling, including pricing, hedging, risk management, structuring, market making, trading strategies and parallel computing. He is also an adjunct professor at the Courant Institute NYU. Prior to joining Bloomberg, He worked for derivatives analysis group at Goldman Sachs, covering interest rate derivatives modelling. Bryan received his Ph.D. in mathematics from University of Michigan and taught math at Northwestern University and UC Davis before he moved to finance.

Political events such as Brexit and US election typically bring a great deal of turbulence and uncertainty to global financial markets whereas company specific events such as earnings announcement or FDA approvals may have big impact on the company’s stock price. Option markets therefore often experience dramatic changes in the face of these upcoming market-changing events. We shall try to understand the behaviors of option markets during these tumultuous periods, and discuss various topics such as option market making, trading strategies, volatility surface and dynamic model calibrations. In particular, by exploiting the term structure of volatilities and change in volatility skews for maturities around the event, we may infer the market perception of the probability distribution of market movements on the event day.
Tuesday February 21 through Thursday March 9, 2015

Lecture Series by Amal Moussa, UBS

Amal Moussa is an Equity Exotic Derivatives trader at UBS. Prior to that, Amal was a derivatives trader at Deutsche Bank and JP Morgan also covering equity exotic options and structured notes, and before that she was a quantitative associate in the Global Emerging Markets group at JP Morgan. Amal holds a Ph.D. in Statistics from Columbia University (New York, 2011), a Masters in Probability and Finance from the Paris 6 University (Paris, 2006) and an Engineering degree in Computer Science from the Ecole Nationale Superieure des Telecommunications (Paris, 2006).

Concepts in Volatility Modeling and Trading


Exotic products are broadly defined as any derivative which economics are “more complicated” than calls and puts. Trading these products require more advanced understanding and modeling of the implied volatility surface than Black-Scholes, because hedging them will involve trading calls and puts at multiple strikes and maturities. We will therefore spend the first part of this mini-series on volatility dynamics and modeling, to prepare for the second part which will cover the most familiar Exotic derivatives in Equity markets. The third and final part will cover a very important difference between the Mathematical Finance standard literature, which references a “risk-free rate”, and real market economics of listed and collateralized trades, in which the abstract idea of “time value of money” actually arises from a very tangible “cost of carry” of the value of the positions.


  1. Volatility Modeling
    1. Estimation of Historical Volatility
    2. Implied Volatility and Smile Dynamics
    3. Local Volatility and Stochastic Volatility
    4. Volatility Swaps and Variance Swaps
  2. Equity Exotic Options: Pricing and Hedging
    1. Barrier Options and Digitals
    2. Autocallables
    3. Correlation Products: Worst-Of Puts, Outperformance Options, Quanto Options
  3. Funding and CSA Discounting
    1. The rate market post 2008: OIS-Libor basis, tenor basis, counterparty risk and collateral
    2. Projection and Discount Curves Construction
    3. Bond, FRA, Swap and Option Pricing using CSA Discounting
Tuesday February 21, 2016

Title: Volatility Modeling I

Speaker: Amal Moussa, UBS
Thursday February 23, 2016

Title: Volatility Modeling II

Speaker: Amal Moussa, UBS
Tuesday February 28, 2016

Title: Equity Exotic Options: Pricing and Hedging II

Speaker: Amal Moussa, UBS
Thursday March 2, 2016

Title: Equity Exotic Options: Pricing and Hedging II

Speaker: Amal Moussa, UBS
Tuesday March 7, 2016

Title: Funding and CSA Discounting I

Speaker: Amal Moussa, UBS
Thursday March 9, 2016

Title: Funding and CSA Discounting II

Speaker: Amal Moussa, UBS
Tuesday, March 14, 2017

Spring Recess — No Seminar
Thursday, March 16, 2017

Spring Recess — No Seminar
Tuesday, March 21, 2017

Title: Factor-Based Investing

Speaker: Natalia Zvereva, J.P. 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 Alternatives funds and Beta strategies. 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. Before joining JP Morgan is 2011, Natalia was a market risk manager at MF Global. Natalia holds a Master’s Degree in Financial Mathematics from Columbia University (2014) and BBA in Finance & Investments from Baruch College (2009).

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, and how factor-based approach can be implemented with ETFs.
Thursday, March 23, 2017

Title: Parallel Processing for the Pricing of Financial Derivatives

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.

Recent developments in computing technology coupled with massive parallel processing have opened the way forward for significant advances in artificial intelligence, robotics, and driverless cars. This same technology can be exploited for use in financial models. In this seminar, I will present first an overview of massive parallel processing on general purpose graphical processing units (aka GPU’s) and how this technology is being applied for financial models. This overview will be followed by a specific application to SPX and SPY options using a model with stochastic volatility and jump processes. The model for the stock price index and its stochastic volatility is based on an empirical analysis of the data, and does not lead to convenient closed form or quasi closed form solutions. Two standard solution techniques will be presented: a Monte Carlo solution and a finite difference solution to the partial differential integral equation, both implemented on GPU’s. I will include some performance tests (computing time tests) and a calibration of the model for both SPX and SPY options. The Monte Carlo solution is applied to the SPX options, which have European style exercise. The SPY options are options on the SPY ETF which have American style exercise and should be solved with a grid based method.
Tuesday, March 28, 2017

Title: What Quants Really Do On Wall Street

Speaker: Mark Higgins, Beacon
Dr Mark Higgins is the COO and co-founder of Beacon, a financial technology company that builds institutional quant platforms and trading and risk management systems. Dr Higgins spent eight years at JPMorgan where he launched the Athena project, a Python-based risk management system currently in use across many of JPMorgan’s trading desks; co-headed the Quantitative Research group for all the market making desks in the investment bank; and ran the franchise making electronic markets on currency options. He spent eight years at Goldman Sachs as a quant on the Foreign Exchange and NY Interest Rate derivatives market making desks. Dr Higgins has a PhD in theoretical astrophysics from Queen’s University in Canada.

What do quants spend their days really working on? What skills are in demand? Dr Higgins will draw on his twenty years experience as a working quant to discuss what businesses quants are involved in on the “buy side” and the “sell side”; what problems they work on; and what techniques they use to solve those problems.
Thursday, March 30, 2017

Title: Building Diversified Portfolios that Outperform Out-Of-Sample

Speaker: Marcos López de Prado, Guggenheim Partners
Marcos López de Prado is Senior Managing Director at Guggenheim Partners, where he manages several multibillion-dollar internal funds. Over the past 18 years, his work has combined advanced mathematics and supercomputing technologies to deliver billions of dollars in net profits for his investors and firms. A proponent of research by collaboration, Marcos has published with over 30 leading academics, resulting in some of the most read papers in Finance (SSRN), multiple international patent applications on Algorithmic Trading, three textbooks, numerous articles in the top Mathematical Finance journals, etc. He serves on the editorial board of 5 academic journals, including the Journal of Portfolio Management (IIJ), and is the managing editor of, the first online community dedicated to financial quantum computing.

Since the year 2010, Marcos has also been a Research Fellow at Lawrence Berkeley National Laboratory (U.S. Department of Energy’s Office of Science), where he conducts unclassified research in the mathematics of large-scale financial problems and HPC at the Computational Research Department. For the past 6 years he has lectured at Cornell University, where he currently teaches a graduate course in Financial Big Data and Machine Learning at the Operations Research Department.

Marcos is a recipient of the 1999 National Award for Academic Excellence, which the Government of Spain bestows once a year to the best graduate student nationally. He earned a Ph.D. in Financial Economics (2003), and a second Ph.D. in Mathematical Finance (2011) from Universidad Complutense, both “summa cum laude.” Between his two doctorates, Marcos was a Postdoctoral Research Fellow of RCC at Harvard University for 3 years, during which he published dozens of articles in JCR-indexed scientific journals. In 2006, he was admitted into American Mensa with a perfect test score. Marcos has an Erdös #2 and an Einstein #4 according to the American Mathematical Society.

Mean-Variance portfolios are optimal in-sample, however they tend to perform poorly out-of-sample (even worse than the 1/N naïve portfolio!) We introduce a new portfolio construction method that substantially improves the Out-Of-Sample performance of diversified portfolios. The full paper is available at: SSRN
Tuesday, April 4, 2017

Title: A pricing model for oil well production and drilling rights. Authors: Xin Liu, Ashish Misra

Speaker: Ashish Misra, Co-Head Analytics & Technology, DW Partners
Mr. Misra joined DW at inception in 2009. As co-head of the Analytics & Technology team, Mr. Misra oversees DW’s core analytical and quantitative modelling. He started his career at Morgan Stanley in the high yield and emerging market fixed income group, followed by the credit derivatives group where Mr. Misra was part of the analytical team for seven years, working under David Warren, developing and implementing risk management and trading strategies for Morgan Stanley’s bank loan portfolio using credit derivatives, structured credit derivatives, loan trades. In 2004, Mr. Misra joined hedge fund BlueMountain Capital Management where he developed their credit pricing models and worked on quantitative trading strategies. He joined the fundamental credit team at Brevan Howard in 2008 and moved to DW in 2009. Mr. Misra holds a BE/ME in Mechanical Engineering from the Indian Institute of Science and a PhD in Aeronautics from the California Institute of Technology.

Valuation of an oil well investment requires modeling both the amount of oil that the well is expected to produce as well as the price at which that oil can be sold. The oil output of a producing well declines over time, and engineers model this production stream using an exponential or hyperbolic decline curve. The decline rate parameters are calibrated to fit the observed oil production of other wells with similar characteristics. The precise details of how this estimation is performed are beyond the scope of this talk. A type curve is a hypothetical decline curve that is used to forecast oil production from land where wells have not yet been drilled. This type curve is a function of the topology and other geophysical attributes. The decline curves for existing wells and type curves for drilling rights are key inputs for valuing energy investments.

Determining the valuation of these barrels of oil in the future requires knowledge of the oil forward curve. Oil futures contracts are fairly liquid and trade in the market place. Among other factors, the evolution of these futures contracts determines whether an operator will drill a well at a given point in time. Furthermore oil curves typically display backwardation when oil is high and are in contango when oil prices are depressed. Given these assets are long dated cash flow streams the curve shape and its evolution take on increased relevance. We present a risk-neutral two-factor model for the evolution of the oil curve. We choose the volatility term-structure based on a principal component analysis of historical oil futures contracts and calibrate it to price option contracts on the underlying oil futures. We then present a monte carlo based valuation framework for oil well drilling rights.

Thursday, April 6, 2017

Title: Biases in Dupire’s Local Volatility Model

Speaker: David Fournie, Morgan Stanley
David-Antoine Fournie is an equity derivatives trader at Morgan Stanley where he is responsible for Single Names Exotics and Dispersion. Before this, he co-founded Deauville Capital Management LP, a hedge-fund manager specialized in relative value volatility trades using over-the-counter derivatives, after having been responsible for Index Exotics and Quantitative Algorithmic Strategies at Morgan Stanley. He graduated from Ecole Polytechnique in 2006, and obtained a Ph.D. in Mathematics from Columbia University in 2010 for his work extending Ito’s formula to functional spaces.

We will explain why a unique local volatility is implied by the volatility surface and how the model works. We will then examine the different biases that arise when pricing more complex products with the model: forward skew, gaps, equity/rates correlation.
Tuesday, April 11, 2017

Title: Radically Elementary Derivative Pricing

Speaker: Keith A. Lewis

A mathematically rigorous foundation for pricing, hedging, and valuing the risk of derivative securities can be established using only martingales. There is no need for change of measure, the Ito formula, partial differential equations, and using the Hahn-Banach theorem to prove the existence of arbitrage free models.

It is time to move past the mathematical fiction of continuous time re-hedging and perfect replication. This theory exposits some of the problems faced by practitioners that the classical theory has failed to address such as when to hedge and how effective a hedge is.

Thursday, April 13, 2017

Title: ETFs, ETNs and Their Statistical Anomalies

Speaker: Mikhail Smirnov, Columbia University

In this talk we overview different classes of exchange-traded funds and notes: long, short, unleveraged, leveraged and describe different statistical anomalies and dynamics in this universe.
Tuesday, April 18, 2017

Title: Of Data Relevance and Sufficiency

Speaker: Douglas 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 15 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.

Credit Risk Modelers come from different backgrounds including finance, economics, statistics, Bayesian statistics, business and machine learning. Consequently there are different approaches to some basic modeling questions like: what is a conceptually sound model? what data to include in a model development sample? and how much data is enough to draw a conclusion? This talk will discuss the treatment of some of these modeling questions, utilizing an extremely simple application for modeling Loss Given Default (LGD). My intent is to facilitate constructive discussion of these issues.
Thursday, April 20, 2017

Title: Factor Investing in Equity Markets

Speaker: Boris Lerner, Morgan Stanley
Boris Lerner is the head of the North American Quantitative and Derivative Strategies (QDS) team at Morgan Stanley, based in New York. Boris has joined Morgan Stanley in 2003, and has worked on a broad range of projects over the years, including technology development, analytics, derivative research, and quantitative modeling. For the last ten years Boris has focused on topics related to volatility trading, quantitative analysis, portfolio construction, risk management, and has been working on developing systematic hedging, and alternative risk premia capture strategies for use in diversified, multi-asset portfolios. Boris holds a Master’s degree in Financial Mathematics from the Columbia University, and a Bachelor’s degree in Finance and Information Technology from the New York University Stern School of Business.

The presentation will be focused on the Equity Risk Premia investing:

  • Identifying equity factors with strong and persistent explanatory power of future stock returns
  • Constructing long-short portfolios to capture the factor premium
  • Testing and adjusting the long-short portfolio for un-intended risks
Tuesday, April 25, 2017

Title: Trends in Quantitative Investment Strategies

Speaker: Susan Palmer, CIBC
Sue Palmer, Executive Director at CIBC, has over 16 years of Structuring, Sales, Asset Management and Risk management experience. She currently focuses on systematic strategies for real-money institutional clients (pensions, asset managers, endowments) through custom, bespoke and benchmark indices. In the past 10 years her primary focus has been in Commodities. Sue previously worked at Gresham Investment Management and at Société Générale. She holds an MA in Mathematical Finance from Columbia University and BS in Mathematics from Villanova University.

Systematic investing in commodities and beyond. A discussion about investing in commodity futures and how the asset management industry is using systematic strategies for market exposure.
Thursday, April 27, 2017

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.
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