The seminar takes place in the Spring of 2023, Tuesdays and Thursdays, 7:40 pm — 8:55 pm.

Location: 301 Uris Hall. For directions please see Directions to Campus and Morningside Campus Map.

Organizer: Lars Tyge Nielsen

Schedule of Presentations

Click here for the Schedule of Past Presentations.

Thursday, February 2, 2023

Title: Dynamic Portfolio Management and Portfolio Protection
Speaker: Mikhail Smirnov, Columbia University
Abstract

We discuss different methods of dynamic portfolio management, especially deleveraging and re-leveraging in falling and rising markets including the classical portfolio insurance strategy of Black-Jones-Perold and its modifications including drawdown portfolio insurance and constant dynamic leverage portfolio insurance. We discuss the advantages and disadvantages of different approaches. For an investment fund with dynamically controlled risk and certain risk inertia we demonstrate the existence of a critical NAV level below which the efficacy of de-leveraging is compromised. We introduce a concept of dynamic leverage, which is a risk measure generalizing traditional value-at-risk type measures.

Tuesday, February 7, 2023

Title: Identifying and Estimating Retail Flows
Speaker: Boris Lerner, Morgan Stanley
Abstract
Thursday, February 9, 2023

Title:
Speaker: Amal Moussa, Goldman Sachs and Columbia University

Dr. Amal Moussa is a Managing Director at Goldman Sachs, where she leads the Single Stocks Exotic Derivatives Trading desk. Prior to that, Amal held senior-level positions in equity derivatives trading at other leading financial institutions such as J.P. Morgan, UBS, and Citigroup. In addition to her work in Markets, Amal is an Adjunct Professor at Columbia University, where she teaches a graduate course on Modeling and Trading Derivatives in the Mathematics of Finance Masters program. Amal has a Ph.D. in Statistics, obtained with distinction, from Columbia University. Her thesis “Contagion and Systemic Risk in Financial Networks” shed light on the importance of the network structure in identifying systemic financial institutions and formulating regulatory policies and has been cited by several scholars and industry professionals, including former Federal Reserve president Janet Yellen. She was also awarded the Minghui Yu Teaching Award at Columbia University. Prior to her Ph.D., Amal graduated with a Masters in Mathematical Finance from Sorbonne University (former Paris VI) and a Grande Ecole engineering degree from Télécom Paris. Amal is a board member of Teach for Lebanon, an NGO working to ensure that all children in Lebanon have access to education regardless of socioeconomic background, and she is an active member of the Women in Trading network at Goldman Sachs.

Abstract

Tuesday, February 14, 2023

Title:
Speaker: Ilya Zhokhov
Abstract
Thursday, February 16, 2023

Title:
Speaker: Daniel Fernholz
Abstract
Tuesday February 21 through Thursday March 9, 2023

Lecture Series by Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Tuesday, February 21, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Thursday, February 23, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Tuesday, February 28, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Thursday, March 2, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Tuesday, March 7, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Thursday, March 9, 2023

Title:
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB (tentative)
Abstract
Tuesday, March 14, 2021 — Spring Recess, no seminar
Thursday, March 16, 2021 — Spring Recess, no seminar
Tuesday, March 21, 2023

Title: Real estate investing in Asset Allocation – the intersection between Fixed Income and Value Equity
Speaker: Rosanna Pezzo-Brizio, Tulip Tree Capital and Columbia University
Abstract
Thursday, March 23, 2023

Title:
Speaker: David-Antoine Fournié
Abstract
Tuesday March 28 through Thursday April 6, 2023

Lecture Series by Julien Guyon, Ecole des Ponts ParisTech
Tuesday, March 28, 2023

Title:
Speaker: Julien Guyon, Ecole des Ponts ParisTech
Abstract
Thursday, March 30, 2023

Title:
Speaker: Julien Guyon, Ecole des Ponts ParisTech
Abstract
Tuesday, April 4, 2023

Title:
Speaker: Julien Guyon, Ecole des Ponts ParisTech
Abstract
Thursday, April 6, 2023

Title:
Speaker: Julien Guyon, Ecole des Ponts ParisTech
Abstract
Tuesday, April 11, 2023

Title: Climate Risk at Major Financial Institutions
Speaker: Peter Cai, Data, Analytics, Reporting and Technology (DART)
Abstract
Thursday, April 13, 2023

Title:
Speaker: Laura S. Leal, Goldman Sachs
Abstract
Tuesday, April 18, 2023

Speaker: Edward Tricker, Graham Capital
Title:
Abstract
Thursday, April 20, 2023

Title:
Speaker: Fabio Mercurio (Tentative)
Abstract
Tuesday, April 25, 2023

Title:
Speaker: Kevin Webster
Abstract
Thursday, April 27, 2023
Title:
Speaker: Natalia Zvereva, JP Morgan Asset Management
Natalia Zvereva, Executive Director, is a portfolio manager in the Quantitative Solutions group at JP Morgan Asset Management, based in New York. An employee since 2011, prior to joining Quantitative Solutions team, Natalia was an Investment Director within Multi-Asset Solutions, overseeing the group’s investment portfolios, as well as spending over 5 years as a risk manager covering Strategic Beta Strategies and the Alternatives businesses. Previously, she worked in Credit Portfolio Solutions Derivatives and Derivatives Clearing risk management within the Investment Bank, where she performed quantitative analysis of cross-asset derivatives portfolios. She was a training champion for Analyst and Associate risk training program globally, where she helped to develop training curriculum and events for junior talent. Natalia holds a Master’s Degree in Financial Mathematics from Columbia University, BBA in Finance & Investments from Baruch College, and is a CAIA charterholder.
Abstract

Past Presentations

Tuesday, January 17, 2023

Title: ADJOINT ALGORITHMIC DIFFERENTIATION (AAD): How to better hedge financial risks, crack some of the puzzles of condensed matter and much more with upside-down derivatives

Speaker: Luca Capriotti, Credit Suisse and Columbia University

Luca works in the Quantitative Analysis and Technology (QAT) department in New York where he is the Global Head of Quantitative Strategies Credit, and he is responsible for both front office and capital models. Previous to this role, he was the global head of Quantitative Strategies for Credit and Structured Notes; he was the EMEA head and the US head of Quantitative Strategies Global Credit Products; he worked in Commodities in New York and London, and he was part of the cross-asset modeling R&D group of QS in the London office.

Luca is also visiting professor at the Department of Mathematics at University College London, and Adjunct Professor at Columbia University, at the Departments of Mathematics and Industrial Engineering and Operations Research. His current research interests are in Credit Models, Computational Finance, and Machine Learning, with a focus on efficient numerical techniques for Derivatives Pricing and Risk Management, and applications of Adjoint Algorithmic Differentiation (AAD), which he has helped introduce to Finance and Physics, and for which he holds a US Patent. Luca has published over 70 scientific papers, with the top 3 papers collecting to date over 1000 citations (h factor 26, i10 factor 48).

Prior to working in Finance, Luca was a researcher at the Kavli Institute for Theoretical Physics, Santa Barbara, California, working in High-Temperature Superconductivity and Quantum Monte Carlo methods for Condensed Matter systems. He has been awarded the Director’s fellowship at Los Alamos National Laboratory, and the Wigner Fellowship at Oak Ridge National Laboratory.

Luca holds an M.S. cum laude in Physics from the University of Florence, and an M.Phil. and a Ph.D. cum laude in Condensed Matter Theory, from the International School for Advanced Studies, Trieste.

Abstract

Adjoint Algorithmic Differentiation (AAD) is a computational technique that, despite being known in its modern form since at least the 1960’s, has become mainstream only in the last decade or so when it was “re-discovered” in Finance about 15 years ago. In this talk, I will describe what makes AAD one of the most important innovations in financial risk management and how the same ideas can be applied in other fields whenever computing accurately and efficiently a large number of derivatives is beneficial.

Thursday, January 19, 2023

Title: Optimal Turnover, Liquidity, and Autocorrelation

Speaker: Gordon Ritter, Ritter Alpha LP and Columbia University

Gordon Ritter is an Adjunct Professor in the MAFN program and founder and CIO of Ritter Alpha LP, a registered investment adviser running systematic absolute-return trading strategies across multiple asset classes and geographical regions. Before Ritter Alpha, he was a senior portfolio manager at GSA Capital and a Vice President in the Statistical Arbitrage Group at Highbridge Capital Management (HCM). Gordon completed his PhD in mathematical physics at Harvard University and his Bachelors’ degree with honors in Mathematics from the University of Chicago. While at Harvard, he published several papers in the areas of quantum field theory, differential geometry, quantum computation and abstract algebra. His current research is on portfolio optimization and statistical machine learning. Notable publications include “Optimal turnover, liquidity, and autocorrelation,” with @Bastien Baldacci of @ Université Paris Dauphine – PSL and @Jerome Benveniste of @New York University, Risk, 2022, and “Machine learning for trading,” Risk, 2017. In recognition of the latter publication, Professor Ritter was named Buy-Side Quant of the Year in 2019.

Abstract

The steady-state turnover of a trading strategy is of clear interest to practitioners and portfolio managers, as is the steady-state Sharpe ratio. In this article, we show that in a convenient Gaussian process model, the steady-state turnover can be computed explicitly, and obeys a clear relation to the liquidity of the asset and to the autocorrelation of the alpha forecast signals. Indeed, we find that steady-state optimal turnover is given by gamma * sqrt(n+1) where gamma is a liquidity-adjusted notion of risk-aversion, and n is the ratio of mean-reversion speed to gamma. The steady-state portfolio size and information ratio can also be given in closed form, and thereare also explicit formulas available in the multi-asset case with nontrivial correlation structure.

Tuesday, January 24, 2023

Title: Semi-Systematic Macro Investment

Speaker: Andres Jaime Martinez

Andrés Jaime is an Associate Portfolio Manager at Capstone, based in New York. His main responsibility is to assess macro trends in Emerging Markets by relying heavily on a systematic and quantitative approach in order to come up with actionable investment ideas.

Previously, Andres was the head of Global Macro Quant & FXEM Volatility Strategy for Morgan Stanley. In addition, he led the LatAm Macro Strategy team for over four years, and was ranked Analyst of the year (1st place) by Institutional Investor in LatAm FX and Rates strategy in 2020, the last year he led the team.

Andrés joined Morgan Stanley in 2017 from Barclays, where he focused on G10 and LatAm local markets research. Prior to that, he worked for Bank of Mexico (Banxico), where he held several managerial positions in strategic and tactical asset allocation, and FX and commodities trading.

He is a regular contributor to the Reforma and El Financiero newspaper in Mexico and has lectured in finance and econometrics in graduate and undergraduate courses at ITAM.

Andrés holds an M.A. in mathematics of finance from Columbia University in New York, a Graduate Certificate in Machine Learning from Cornell University, and a B.A. in economics from ITAM University in Mexico City.

Abstract

Quantitative models are not only useful as inputs into systematic strategies, but can heavily improve the performance of a discretionary portfolio. I will go through a few examples on the applications of machine learning and more classical econometric techniques into macro trading.

Thursday, January 26, 2023

Title: VOLATILITY AND ARBITRAGE

Speaker: Ioannis Karatzas, Columbia University

Ioannis Karatzas is the Eugene Higgins Professor of Applied Probability at Columbia. He was the driving force in the founding of the Mathematics of Finance MA (MAFN) program back in 1996/97 and has been a strong supporter of the program ever since. He earned his PhD from Columbia University in the City of New York in 1980, did his post-doc at Brown University, then returned to Columbia as a faculty member. He works, publishes, and advises Ph.D. students in probability, stochastic control, mathematical economics and finance. His book “Brownian Motion and Stochastic Calculus”, co-authored with Steven Shreve of Carnegie Mellon University and first published in 1987, is the standard reference in the field of Stochastic Analysis and has helped educate several generations of students and researchers. His recent book “Portfolio Theory and Arbitrage: A Course in Mathematical Finance”, published in 2021 and co-authored with Constantinos Kardaras of The London School of Economics and Political Science (LSE), may represent the ultimate formulation of the theoretical underpinnings of arbitrage pricing. Complementing his academic work, Professor Karatzas has had a long-standing association with INTECH Investment Management in Princeton, NJ

Abstract

Can the market portfolio be outperformed? If not, why? If yes, under what structural conditions? over which time-horizons? by what portfolios? Can said conditions and portfolios be described in terms only of observables, without resorting to any model assumptions? Questions such as these lead to some pretty interesting mathematics — both in probability theory and in differential geometry, the flow of curves by their curvature (as in the seminal work of Richard Hamilton). We shall discuss some results in this vein, and suggest open problems. (Joint work with E.R. Fernholz and J. Ruf.)