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.

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.

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.

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
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.

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

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

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.)

Thursday, February 2, 2023

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

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
Boris Lerner, Morgan Stanley

Boris Lerner is the Global Head of Quantitative Equity Research at Morgan Stanley, based in New York. Boris joined Morgan Stanley in 2003 and has worked on a broad range of projects over the years, including derivative research, quantitative modeling, data analytics, and structuring. In his current role, Boris is focused on the quantitative analysis of the equity markets, developing alpha models, evaluating alternative data sources, and applying quantitative techniques to traditional fundamental research. Prior to joining the Morgan Stanley Research department, Boris co-headed the Morgan Stanley Quantitative Investment Strategies (QIS) structuring team in North America, where he developed cross-asset, rules-based hedging and risk premia investment strategies for use in diversified institutional portfolios. Boris holds a Master’s degree in Financial Mathematics from Columbia University, and a Bachelor’s degree in Finance and Information Technology from the New York University Stern School of Business.

The “Game Stop frenzy” and the broader “meme stock” phenomenon that has developed over the last year have prompted many questions from clients on how to effectively estimate retail activity. To address these questions, we have developed a model using publicly available data. Our model seeks to estimate both the level and direction of retail trading activity (i.e., how much of the daily trading volume in each stock is attributable to retail traders, and whether they were net buyers or sellers of that volume).

We find that insights into retail activity provided by our model can be used to generate alpha. We see a positive relationship between our estimate of the retail order imbalance and the subsequent stock returns. Stocks with a high buy imbalance tend to outperform stocks with a high sell imbalance over the subsequent one month. The signal can be further amplified by limiting the universe of stocks to those with high retail activity.

Thursday, February 9, 2023

Title: Introduction to Equity Exotic Derivatives Trading
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.


Tuesday, February 14, 2023

Title: Introduction into MBS market, MBS instruments, Structured MBS, and MBS Derivatives
Ilya Zhokhov

Ilya Zhokhov is currently Managing Director in ALM portfolio management group at PNC Bank focusing on mortgage servicing rights portfolio management. Prior to this role, Ilya was Executive Director at JP Morgan where he was revenue CFO in mortgage bank most recently and head of MSR risk management in Chief Investment Office prior to that. Before JP Morgan 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.

Introduction and overview of MBS market, key MBS investors, the structure of MBS market and key investment risk associated with investing in MBS as well as overview of MBS modeling process and key assumptions.

Thursday, February 16, 2023

Title: Covariance Estimation
Daniel Fernholz

Dan Fernholz is head of quantitative modeling at AEA Investors LP. Previously, he was founder and fund manager at Numeraire Technologies, a systematic quantitative hedge fund. Dan has a PhD in Computer Science from the University of Texas and a BA in Mathematics from Harvard University, and has academic publications in computer science, mathematics and finance.

An accurate measurement of the covariance matrix of stock returns is essential for effective portfolio optimization. We give an introduction to the problem of covariance estimation, exploring both theoretical aspects and practical considerations.

Tuesday February 21 through Thursday March 9, 2023

Lecture Series by Paul-Guillaume Fournié, BNP Paribas CIB

Title: Introduction to Interest Rates models

Paul-Guillaume Fournié has been working as an options rates quant at BNP Paribas’ New York office since 2019, heading the team locally since 2022. Covering both Vanilla and Exotic options, his most recent focus has been on the creation and adaptation of interest rates models to SOFR. He holds MA in Mathematics and Physics from Ecole Polytechnique and MA in Economics and Public Policy from Corps des Mines. Before switching to finance he occupied several positions in leading French industrial and telecommunications companies and served in France’s Ministry of the Economy.

Summary :
This lecture series is an introduction to Interest Rates models. It will build the universe of interest rates as succesive blocks of increasing complexity.

Starting from the notion of financing and time value for money, we will first define discount factors, forwards and swaps ; and introduce the notions of yield curve, multi-curve framework and collateralization. The next lessons will focus on vanilla options on interest rates (caps, floors and swaptions) and on other liquid products (bonds, futures and their options). The last part will be dedicated to exotic products, ranging from CMS to callable structured notes and variable notional Bermudean swaptions. Besides, a specific lecture will cover the transition from LIBOR to SOFR, which is one of the most significant transformations of the financial industry in the past decades.

Throughout the lecture, a specific focus will be put on the intuition behind each product and concept ; and the most commonly used models will be introduced and analyzed.

Tuesday, February 21, 2023

Title: Interest rates and yield curve
Paul-Guillaume Fournié, BNP Paribas CIB

Abstract: Introduction to basic concept

Thursday, February 23, 2023

Title: Multicurve framework and collateralization
Paul-Guillaume Fournié, BNP Paribas CIB

Abastract: How things became complicated

Tuesday, February 28, 2023

Title: Vanilla options and exchang-traded products
Paul-Guillaume Fournié, BNP Paribas CIB

Caps, Floors and Swaptions- A first jump into the non-linear world
Other rates flow and vanilla products- Eurodollar futures, Treasuries and Treasury futures

Thursday, March 2, 2023

Title: CMS and CMS spread options
Paul-Guillaume Fournié, BNP Paribas CIB

CMS replication- The main building block of rates exotics
CMS spread options- Mixing two underlyings together

Tuesday, March 7, 2023

Title: From LIBOR to SOFR
Paul-Guillaume Fournié, BNP Paribas CIB

From LIBOR to SOFR- The power of compounding

Thursday, March 9, 2023

Title: Exotic products
Speaker: Paul-Guillaume Fournié, BNP Paribas CIB

Midcurves and Rollercoasters- When everything is a basket of co-initial swaps
Complex products, complex models- When Monte Carlo becomes necessary

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
Rosanna Pezzo-Brizio, Tulip Tree Capital and Columbia University

In 1997, Rosanna Pezzo-Brizio formed part of the first class of the Mathematics of Finance Program at Columbia alongside seven other students.

After her graduation in 1998, she joined the Litterman’s group in Goldman Sachs Asset Management as an analyst, where she managed partners’ portfolios using the Black Litterman model. While working at Goldman Sachs, she completed her thesis for her Ph.D. in Mathematics of Finance from the University of Brescia, Italy.

Dr. Pezzo-Brizio later joined Greenwich Capital as a Risk Manager for the Treasury and Derivative Desk, and in 2004 she became an Exotic Trader, focusing on Credit Derivatives. She was the only woman on the trading floor.

In 2006, she joined Intesa Sanpaolo in Turin, Italy, to start a Credit Derivates Desk. A year later, she was promoted to Head of Fixed Income Desk and managed the three-billion-dollar Fixed Income Portfolio of the bank’s New York branch. Dr. Pezzo-Brizio sold the mortgage back security portfolio before the Lehman collapse in 2008, then started a new Fixed Income Liquidity Portfolio. After the European Crisis of 2011, she managed both that portfolio and the Treasury Department of the New York branch.

Dr. Pezzo-Brizio moved to New York Life Investment Management in 2019 to supervise different Portfolio Strategies, covering both the Asset Allocation and Fixed Income sectors. In January 2023, she founded Tulip Tree Capital.

Since 2015, Dr. Pezzo-Brizio has been an Adjunct Professor of Fixed Income Portfolio Management in the Mathematics of Finance Program at Columbia University.


Thursday, March 23, 2023

David-Antoine Fournié

Tuesday March 28 through Thursday April 6, 2023

Lecture Series by Julien Guyon, Ecole des Ponts ParisTech

Title: Volatility modeling: from Black-Scholes to path-dependent and rough volatility
Speaker: Julien Guyon, Ecole des Ponts ParisTech

Julien Guyon is a professor of Applied Mathematics at Ecole des Ponts ParisTech, one of the oldest and one of the most prestigious French Grandes Ecoles, where he holds the BNP Paribas Chair Futures of Quantitative Finance. Before joining Ecole des Ponts, Julien worked in the financial industry for 16 years, first in the Global Markets Quantitative Research team at Societe Generale in Paris (2006-2012), then as a senior quantitative analyst in the Quantitative Research group at Bloomberg L.P., New York (2012-2022). Julien was also an adjunct professor in the Department of Mathematics at Columbia University and at the Courant Institute of Mathematical Sciences, NYU, from 2015 to 2022; and previously at Universite Paris Diderot and Ecole des Ponts ParisTech. Julien serves as an Associate Editor of Finance & Stochastics, SIAM Journal on Financial Mathematics, and Journal of Dynamics and Games, as well as a Managing Editor of Quantitative Finance. He is also a Louis Bachelier Fellow.

Julien co-authored the book Nonlinear Option Pricing (Chapman & Hall, 2014) with Pierre Henry-Labordere. He has published more than 20 articles in peer-reviewed journals (including Finance and Stochastics, SIAM Journal on Financial Mathematics, Quantitative Finance, Risk, Journal of Computational Finance, Annals of Applied Probability, Stochastic Processes and their Applications) and is a regular speaker at international conferences, both academic and professional. His main research interests include volatility and correlation modeling, option pricing, optimal transport, and numerical probabilistic methods.

A big soccer fan, Julien has also published articles on fairness in sports both in academic journals and in top-tier newspapers including The New York Times, The Times, Le Monde, and El Pais. Some of his suggestions for draws and tournament design have been adopted by FIFA and UEFA, including a new, fairer draw method for the FIFA World Cup; a fairer format for the 2026 FIFA World Cup (in progress); a new knockout bracket for the UEFA Euro; and an optimized schedule of the UEFA Champions League. His paper “Risk of collusion: Will groups of 3 ruin the FIFA World Cup?” won the 2nd prize at the 2021 MIT Sloan Sports Analytics Conference, the biggest sports analytics event in the world.

Time permitting, we will address the following topics:

– The different types of volatility
– The different types of volatility derivatives
– The volatility smile
– Volatility modeling: a brief history
– Static vs dynamic properties of volatility models
– Black-Scholes, P&L analysis
– Local volatility
– Stochastic volatility
– Variance curve models
– The smile of variance curve models
– Local stochastic volatility
– The particle method for smile calibration
– Path-dependent volatility
– Rough volatility

Tuesday, April 11, 2023

Title: Climate Risk at Major Financial Institutions
Speakers: Peter Cai, Data, Analytics, Reporting and Technology (DART) and Cino Robin Castelli, Director at Citi

PETER CAI is a member of the Risk Management Executive Committee, and the global head of Risk Data, Analytics, Reporting & Technology (DART).

Peter has well-rounded experience in risk infrastructure and governance across securities, banking and insurance. Prior to joining Citi, Peter was at Barclays where he oversaw risk in the global asset-liability profile and investments, responsible for the ALM/IRRBB framework, quantitative analytics and regulatory requirements.

Peter has prior experience at Global Atlantic (formerly Goldman Sachs Reinsurance) as the Chief Risk Officer, as well as Morgan Stanley in Enterprise Risk Management and Lehman Brothers in Global Credit Trading/Fixed Income.

As the head of Risk Data, Analytics, Reporting & Technology, Peter oversees risk data aggregation, controls, reporting and analysis across all risk teams. Peter is also the Risk Data Transformation Sponsor accountable for Risk’s overall data efforts, working closely with all risk stripes, Chief Data Office, Finance and Operations & Technology.

Peter has a Ph.D. degree in Materials Science from Pennsylvania State University and a B.S. in Mathematics and Applied Mechanics from Fudan University in China.

CINO ROBIN CASTELLI is Director at Citi, Head of Climate Modeling Analytics supporting Citi’s firmwide agenda on Climate Risk, by leading the team that is developing the models that are required to estimate the stresses to financial institutions, properties or sectors which arise from shifts in policy, consumer and business sentiment, or technologies associated with the required changes necessary to limit climate change. Prior to this position, Robin was the Business Unit Manager for Enterprise Risk Management, the area that covers, amongst other topics, Climate Risk, and the Chief Strategy Officer for Quantitative Risk and Stress Testing, the division of Risk tasked with developing all the quantitative models used for Market Risk, Counterparty Risk, Credit and Obligor Risk Analytics, Risk Capital Analytics, and Stress Testing. Robin is also co-founder and former Executive VP for Business Development at MacroUSA, in the field of Unmanned Ground Vehicles, and prior to that, co-founder, CEO and president of Macroswiss SA. Robin holds a Bachelor’s Degree and a Master’s Degree in Molecular Biology, summa cum laude, from Università degli Studi di Milano-Bicocca, with an evolutionary biology thesis on “Chromosomal rearrangements as speciation mechanisms” and is the co-author of “Quantitative Methods for ESG Finance” by Wiley (ISBN 978-1119903802). Robin also guest lectures at Columbia in the school of Engineering (IEOR 4723:Financial Engineering for ESG Finance, course taught by Prof. Cyril Shmatov), on the use of Agent-Based Modeling for financial applications.

Section 1 – Climate Change and why it matters
Fundamentals of Climate Change – The hard data
The Impact of Climate Change on the Financial Sector
Section 2 – How banks should deal with Climate Risks
Section 3 – Climate Risk Modelling: Approaches and case study

Thursday, April 13, 2023

Title: Optimal Execution with Quadratic Variation Inventories
Speaker: Laura S. Leal, Goldman Sachs

Laura Leal is Vice President at GSAM Quantitative Investment Strategies, working on Systematic Trading. She has completed her PhD in the Operations Research and Financial Engineering department at Princeton University. Her research interests are centered in high-frequency finance, using machine learning, deep neural networks, optimization, statistical and econometric methods to study high-frequency trading data. The main topics she has worked on include optimal execution, market making, identification of institutional activity, and tail risk. During her time in Princeton, Laura was awarded the School of Engineering and Applied Science Award for Excellence, the Princeton Teaching Award and the Gordon Wu Fellowship.

We describe and implement statistical tests arguing for the presence of a Brownian component in the inventories and wealth processes of individual traders. We use intra-day data from the Toronto Stock Exchange to provide empirical evidence of this claim. Results are valid for regularly spaced time intervals, as well as with asynchronously observed data. The tests reveal with high significance the presence of a non-zero Brownian motion component. Furthermore, we analyse trader behaviors throughout the day. We extend the theoretical analysis of an existing optimal execution model to accommodate the presence of Ito inventory processes, and compare empirically the optimal behavior of traders in such fitted models, to the actual behavior we read off the data.

Tuesday, April 18, 2023

Speaker: Thomas Feng, Graham Capital

Tom Feng is a Director of Quantitative Research at Graham Capital Management, where he has worked for 14 years and is responsible for developing and managing systematic trading strategies in global macro asset classes. Prior to joining Graham, Tom was a quantitative portfolio manager at Fortress Investment Group and RBS Greenwich Capital, respectively. Tom started his career in quantitative finance at RBS Greenwich Capital, in research roles culminating in Head of Interest Rate and Mortgage Derivatives Research, before transitioning into quantitative investing where he has accumulated close to 25 years of experience.

Tom holds BS and MS degrees in Mathematics from Yale and a PhD in Mathematics from Princeton.

Title: Options, Real and Imagined

Options play a myriad of roles in the quantitative investment process. In this talk we survey a broad range of usage cases for options. We start with some investment problems that can be cast theoretically as synthetic options and show that option pricing theory can lead to useful solutions. Next we show that data from options markets can be used to forecast underlying asset prices and create trading signals. Finally we discuss various ways options can be traded directly in systematic strategies.

Thursday, April 20, 2023

Title: Crypto Ecosystem and AMM Design
Speaker: Irene Aldridge, AbleMarkets and AbleBlox

Irene Aldridge is an internationally-recognized quantitative Finance and AI researcher, Adjunct Professor at Cornell University and Lecturer at Cambridge University (U.K.). In addition, Irene is President and Managing Director, Research, of AbleMarkets, an AI-for-Finance company, as well as President of AbleBlox, a blockchain startup. Prior to AbleMarkets and AbleBlox, she 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 a software engineer in financial services. Irene is a co-author of “Big Data Science in Finance” (with Marco Avellaneda, Wiley 2021), “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading, Flash Crashes” (co-authored with Steve Krawciw, Wiley, 2017), and the author of “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (2nd edition, translated into Chinese, Wiley 2013), among other work.

Assets on blockchain trade 24×7 with very thin liquidity. This demands new fully automated processes, including Automated Market Making (AMM). We dive into the microstructure of the fully-automated systems,comparing the differences between traditional and modern microstructure implementations.

Tuesday, April 25, 2023

Title: What is the P&L cost of incorrectly estimating your price impact model?
Speaker: Kevin Webster
Professor Webster has ten years of experience building large-scale, systematic, and model-driven frameworks for trading and has deployed algorithms for top-tier financial institutions such as Citadel, Deutsche Bank, and BNP Paribas. Kevin graduated with a Ph.D. from Princeton University’s Operations Research and Financial Engineering Department (ORFE), where he studied mathematical models applied to high-frequency trading, emphasizing price impact and market making. Professor Webster authored the Handbook of Price impact modeling. His research papers include “Do You Really Know Your P&L? The Importance of Impact-Adjusting the P&L,” with Petter Kolm, available on SSRN , “Stochastic Liquidity as a Proxy for Nonlinear Price Impact,” with Johannes Muhle-Karbe and Zexin Wang, available on SSRN, “Getting More for Less – Better A/B Testing via Causal Regularization,” with Nicholas Westray, available on SSRN, “The Self-Financing Equation in High Frequency Markets,” with Rene Carmona, Finance & Stochastics 23 (3) (2019) , and “Information and Inventories in High Frequency Trading,” with Johannes Muhle-Karbe, Market Microstructure and Liquidity, Vol. 03, No. 02 (2017).

Portfolio managers’ orders trade off return and trading cost predictions. Return predictions rely on alpha models, whereas price impact models quantify trading costs. This talk revisits the non-linear price impact proposed by Alfonsi, Fruth, and Schied (AFS) and its optimal trading strategy. Then, it studies the AFS model’s misspecification cost, that is, the cost of incorrectly estimating the price impact model. Finally, the talk presents empirical results to illustrate the AFS model and its misspecification costs.

Thursday, April 27, 2022

Title: Introduction to Factor Investing
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.

In this lecture, we will cover an introduction to factor-based investing, risk management, and industry trends. We will go over various types of factors used in factor models, insights into construction and evaluation of factor strategies, as well as discuss how factor-based investing can be used to enhance returns, manage risk, and achieve diversification.