The NYU Tandon Department of Finance and Risk Engineering welcomes Liuren Wu to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, October 13th at 6 pm ET on Zoom*.
“Common Pricing of Decentralized Risk:
A New Linear Option Pricing Model”
*NYU Students are highly encouraged to attend in person.
All other non-NYU guests are invited to attend virtually.
The NYU Tandon Department of Finance and Risk Engineering welcomes Nassim Nicholas Taleb to the Peter Carr Brooklyn Quant Experience (BQE) Seminar Series on
Thursday, October 6th at 6 pm ET on Zoom*.
“Covid: A Lesson in Risk Management”
Nassim Nicholas Taleb
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell Financial Data Science Seminars. Through hybrid talks in Fall 2022, we are excited to collaborate with various guest speakers in highlighting machine learning applications in finance.
All webinars are from 5:30 pm to 6:30 pm ET.
This webinar is free and open to all guests. Registration is required (please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from no-reply@zoom.us).
| Date | Tuesday, September 13th, 2022 |
| Time | 5:30 pm to 6:30 pm ET |
| Speaker | Ciamac Moallemi (Columbia) |
| Title | Liquidity Provision and Automated Market Making |
Abstract:
In recent years, automated market makers (AMMs) and, more specifically, constant function market makers (CFMMs) such as Uniswap, have emerged as the dominant mechanism for trading risky assets on blockchains. On the Ethereum blockchain, for example, such decentralized exchanges are the largest single “application category” implemented through smart contracts, as ranked by resource consumption. Compared to electronic limit order books (LOBs), which are the dominant market structure for traditional, centralized exchange-based electronic markets, CFMMs offer some advantages. First of all, they are efficient computationally. Thus, CFMMs are uniquely suited to the severely computation- and storage-constrained environment of the blockchain. Second, LOBs are not well-suited to a “long-tail” of illiquid assets. This is because they require the participation of active market markers. In contrast, CFMMs mainly rely on passive liquidity providers (LPs).
We consider the market microstructure of CFMMs from the economic perspective of the liquidity providers. In a frictionless, continuous-time setting and in the absence of trading fees, we decompose the return of an LP into a instantaneous market risk component and a non-negative, non-decreasing, and predictable component which we call “loss-versus-rebalancing” (LVR, pronounced “lever”). Market risk can be fully hedged, but once eliminated, LVR remains as a running cost that must be offset by trading fee income in order for liquidity provision to be profitable. We show how LVR can be interpreted in many ways: as the cost of commitment, as the time value for giving up future optionality, as the compensator in a Doob-Meyer decomposition, as an adverse selection cost in the form the profits of arbitrageurs trading against the pool, and as an information cost because the pool does not have access to accurate market prices. LVR is distinct from the more commonly known metric of “impermanent loss” or “divergence loss”; this latter metric is more fundamentally described as “loss-versus-holding” and is not a true running cost. We express LVR simply and in closed-form: instantaneously, it is the scaled product of the variance of prices and the marginal liquidity available in the pool, i.e., LVR is the floating leg of a generalized variance swap. As such, LVR is easily calibrated to market data and specific CFMM structure. LVR provides tradeable insight in both the ex ante and ex post assessment of CFMM LP investment decisions, and can also inform the design of CFMM protocols.
This talk is joint work with Jason Milionis (Columbia CS), Tim Roughgarden (Columbia CS/a16z Crypto), and Anthony Lee Zhang (Chicago Booth).
The paper is available here: https://moallemi.com/ciamac/papers/lvr-2022.pdf
Speaker Bio:
Ciamac C. Moallemi is the William von Mueffling Professor of Business in the Decision, Risk, and Operations Division of the Graduate School of Business at Columbia University, where he has been since 2007. A high school dropout, he received S.B. degrees in Electrical Engineering & Computer Science and in Mathematics from the Massachusetts Institute of Technology (1996). He studied at the University of Cambridge, where he earned a Master of Advanced Study degree in Mathematics (Part III of the Mathematical Tripos), with distinction (1997). He received a Ph.D. in Electrical Engineering from Stanford University (2007). Prior to his doctoral studies, he developed quantitative methods in a number of entrepreneurial ventures: as a partner in a $200 million fixed-income arbitrage hedge fund and as the director of scientific computing at an early-stage drug discovery start-up. He holds editorial positions at the journals Operations Research and Management Science. He is a past recipient of the British Marshall Scholarship (1996), the Benchmark Stanford Graduate Fellowship (2003), first place in the INFORMS Junior Faculty Paper Competition (2011), and the Best Simulation Publication Award of the INFORMS Simulation Society (2014). Aside from his academic work, he regularly consults for fintech companies. His research interests are in the development of mathematical and computational tools for optimal decision making under uncertainty, with a focus on applications areas including market microstructure, quantitative and algorithmic trading, and blockchain technology.
We hope to see you online!
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Upcoming Events
September 16, 2022
Future of Finance Conference
October 25, 2022
Speaker: Yuyu Fan (Alliance Bernstein)
Title of Presentation: TBD
November 8, 2022
Speaker: Kevin Webster
Title of Presentation: TBD
November 29, 2022
Speaker: Chakri Cherukuri (Bloomberg)
Title of Presentation: TBD
The Peter Carr Memorial Conference will honor the life and career of Peter Carr, our beloved teacher, scholar, and colleague.
Through the sharing of research that spans various domains and disciplines, this conference aims to memorialize Peter, his contributions to financial engineering, and the legacy he’s left behind for generations of professionals and academics to extend and follow.
This conference will be co-hosted at NYU’s Brooklyn Campus by the Tandon School of Engineering and the Society of Quantitative Analysts (SQA), where Peter served as chair and director, respectively.
For general conference inquiries, contact the conference planning committee at carr-memorial-conference@nyu.edu.
The deadline to register is Friday, May 20th. You must register by this date to guarantee access to the venue.
Join us for the Brooklyn Quant Experience (BQE) Lecture Series today, Thursday, May 5th at 6 pm ET on Zoom.
“Simulacrum or Shenanigan: Deep Generative Models and Simulators for Financial Markets”
Derek Snow
Visiting Industry Assistant Professor
Department of Finance and Risk Engineering
NYU Tandon
*Please note a meeting password is required for this event.
Meeting ID: 995 3136 9451
Password: BQEDS55
Abstract
Deep generative models are synthetic data generators that use deep learning algorithms to generate data that preserves the original data’s statistical features while producing entirely new data points. Deep generative models are not dynamic or reactive, whereas other data-generating techniques like multi-agent market simulators are. This presentation will identify the differences between these methods and discuss a new third-way approach that combines deep learning and agent-based models.
Bio
Derek is an assistant professor at NYU and an associate member at Oxford University’s Man Institute of Quantitative Finance and The Alan Turing Institute, the UK’s national institute for Artificial Intelligence. He was previously a visiting Doctoral scholar at the University of Cambridge and NYU’s School of Engineering. He received his Ph.D. and Honours degree with distinction from the University of Auckland, studying topics in Machine Learning for Finance. Derek has worked with some of the world’s largest quantitative research firms, and his software receives thousands of monthly downloads.
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell Financial Data Science Webinars. Through online talks in Spring 2022, we are excited to collaborate with various guest speakers in highlighting machine learning applications in finance.
All webinars are from 5:00 pm to 6:00 pm ET.
This webinar is free and open to all guests. Registration is required (please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from no-reply@zoom.us).
| Date | Tuesday, May 10th, 2022 |
| Time | 5:00pm – 6:00pm ET |
| Speaker | Felix Prenzel (University of Oxford) |
| Title | Analysis and Modeling of Client Order Flow in Limit Order Markets |
Abstract:
Orders in major electronic stock markets are executed through centralised limit order books (LOBs). The availability of historical data have led to extensive research modelling LOBs. Better understanding the dynamics of LOBs and building simulators as a framework for controlled experiments, when testing trading algorithms or execution strategies are among the aims in this area. Most work in the literature models the aggregate view of the limit order book, which focuses on the volume of orders at a given price level using a point process. In addition to this aggregate view, brokers and exchanges also have information on the identity of the agents submitting the order to them. This leads to a more complicated representation of limit order book dynamics, which we attempt to model using a heterogeneous model of order flow.
We present a granular representation of the limit order book, that allows to account for the origins of different orders. Using client order flow from a large broker, we analyze the properties of variables in this representation. The heterogeneity of the order flow is modeled by segmenting clients into different clusters, for which we identify representative prototypes. This segmentation appears to be stable both over time, as well as over different stocks. Our findings can be leveraged to build more realistic order flow models that account for the diversity of market participants.
Speaker Bio:
Felix Prenzel is a Ph.D. student part of the Centre of Doctoral Training in Mathematics of Random Systems at the University of Oxford. He is supervised by Prof. Rama Cont and Prof. Mihai Cucuringu. His research primarily concerns data-driven modeling of limit order books with the aim to build realistic training environments for trading applications.
We hope to see you online!
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Past Events
February 15, 2022
Speaker: Kevin Webster
Title of Presentation: “How Price Impact Distorts Accounting P&L – Revisiting Caccioli, Bouchaud and Farmer’s Impact-Adjusted Valuation”
March 22, 2022
Speaker: Maarten Scholl (Oxford)
Title of Presentation: “Studying Market Ecology Using Agent-Based Models”
April 26, 2022
Speaker: Andreea Minca (Cornell ORIE)
Title of Presentation: “Clustering Heterogeneous Financial Networks”
Join us for the Brooklyn Quant Experience (BQE) Lecture Series today, Thursday, April 28th at 6 pm ET on Zoom.
“Bridging P-Q Modeling Divide with Factor HJM Modeling Framework”
Andrei Lyashenko
Head of Market Risk and Pricing Model
Quantitative Risk Management
*Please note a meeting password is required for this event.
Meeting ID: 924 1231 8499
Password: BQEAL428
Abstract
We show how the factor modeling approach widely used to model yield curve evolution in real-world applications can be adapted to pricing applications using the Musiela HJM modeling framework. The resulting risk-neutral modeling framework combines the intuitiveness and computational efficiency of the factor modeling approach with the rigor of risk-neutral term structure pricing models.
Bio
Andrei Lyashenko is the head of Market Risk and Pricing Models at the Quantitative Risk Management (QRM), Inc. in Chicago. His team is responsible for research, implementation, and support of pricing and risk models across multiple asset classes. In November 2019, he was awarded the prestigious Quant of the Year award, jointly with Fabio Mercurio from Bloomberg, L.P., for their Risk Magazine paper on modeling backward-looking rates.
Andrei is also an adjunct professor at the Illinois Institute of Technology. Before joining the QRM in 1997, Andrei was on the mathematical faculty at the University of Illinois at Chicago and Iowa State University. Prior to coming to the US, he conducted academic research in applied math in Russia, Japan, and Italy and published numerous research papers in the area of fluid stability in major mathematical journals. He holds a BSc in Mathematics from the Novosibirsk State University, Russia, and a Ph.D. in Mathematics from the Russian Academy of Science.
Join us for the Brooklyn Quant Experience (BQE) Lecture Series today, Thursday, April 21st at 6 pm ET on Zoom.
“Risk and Reward in the Fixed-Income Market: Where are We Now?”
This is joint work with Andrew Brenner, senior partner and head of international fixed income at NatAlliance Securities LLC .
Leon Tatevossian
Adjunct Professor
Department of Finance and Risk Engineering
NYU Tandon School of Engineering
*Please note a meeting password is required for this event.
Meeting ID: 975 0103 9278
Passcode: BQELT421
Abstract
Dislocations caused by the pandemic extended across all parts of the economy (corporate, consumer, and the public sector) and the capital markets. In response aggressive fiscal intervention was implemented, beginning with the $2.2 trillion CARES Act (signed by President Trump in March 2020) and President Biden’s $1.9 trillion American Rescue Plan (signed in March 2021). Steps taken on the monetary front included the Fed’s multifaceted bond-buying and liquidity programs. This combination of spending and liquidity actions to: [1] underpin the consumer, business, and corporate sectors; and [2] stabilize financial-asset valuations, was unprecedented in its size.
Economic developments getting the most attention now by the rates Markets are the dramatic increase in inflation and expectations of the reversal of monetary accommodation (which will include the sell-down of the Fed’s $9 trillion Treasury and agency mortgage bond portfolio).
This seminar talk (in a question-and-answer form with supporting slides) will explain some of the basic valuation parameters in the fixed-income markets, how these metrics got upended by the swift arrival of the pandemic, and how market sentiment evolved as the Treasury and Fed actions worked through the system.
Of particular interest: How reliably have the “usual suspects” guided the valuation of “risk assets,” “structure,” and “credit”? The investment performance of these sectors has always exhibited connections to the “real economy” (consumer and business) and to the decision-making and risk appetites of institutional investors. Which valuation relationships and economic metrics do we interrogate for a clear perspective on where we’ve been … and where we might be headed?
Bio
Leon Tatevossian is an adjunct instructor in quantitative finance in the Finance and Risk Engineering Dept. and at the Courant Institute of Mathematical Sciences. From 2009-to 16 he was a director in Group Risk Management at RBC Capital Markets, LLC where he focused on securitized-products market risk in secondary trading, origination, and proprietary-trading areas. Leon has twenty-eight years of experience in the fixed-income capital markets (trader, quantitative strategist, derivatives modeler, and market-risk analyst), with a product background covering US Treasury securities, US agency securities, interest-rate derivatives, mortgage-backed securities, asset-backed securities, and credit derivatives. Prior to RBC, he worked at several large sell-side firms (Banc of America Securities, Goldman Sachs, Citicorp Securities, and Morgan Stanley).
Leon graduated from MIT (SB; mathematics) and was a Ph.D. student in mathematics at Brown University. He has also taught courses in quantitative finance at Columbia University (Department of Industrial Engineering and Operations Research) and at Baruch College–The City University of New York (Department of Mathematics).
Featuring Machine Learning experts from Cornell, Citi, and more…
You and your colleagues are invited to attend the Cornell Financial Data Science Webinars. Through online talks in Spring 2022, we are excited to collaborate with various guest speakers in highlighting machine learning applications in finance.
All webinars are from 5:00 pm to 6:00 pm ET.
This webinar is free and open to all guests. Registration is required (please RSVP here). You will receive the webinar link and dial-in info upon registration (the confirmation email will come from no-reply@zoom.us).
| Date | Tuesday, April 26th, 2022 |
| Time | 5:00pm – 6:00pm ET |
| Speaker | Andreea Minca (Cornell ORIE) |
| Title | Clustering Heterogeneous Financial Networks |
Abstract:
For the degree corrected stochastic block model in the presence of arbitrary or even adversarial outliers, we develop a convex-optimization-based clustering algorithm. We test the performance of the algorithm on semi-synthetic heterogenous networks reconstructed to match aggregate data on the Korean financial sector. Our method allows for recovery of sub-sectors with significantly lower error rates compared to existing algorithms. Our second application is to overlapping portfolio networks, for which we uncover a clustering structure.
Speaker Bio:
Andreea Minca is an Associate Professor in the School of Operations Research and Information Engineering at Cornell University. She holds degrees from Sorbonne University (PhD in Applied Mathematics) and Ecole Polytechnique (Diplome de l’Ecole Polytechnique).
In recognition of “her fundamental research contributions to the understanding of financial instability, quantifying and managing systemic risk, and the control of interbank contagion,” Andreea received the 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize. This award distinguishes contributions to the mathematical modeling of financial markets and is the highest early-career distinction in the field of financial engineering and mathematics. Andreea is also a recipient of the NSF CAREER Award (2017), a Research Fellow of the Global Association of Risk Professionals (GARP) (2014), and an AXA Research Fund Awardee (2020). She serves on the editorial board of the SIAM Journal on Financial Mathematics.
We hope to see you online!
**Please excuse any duplication of this announcement
If you are interested in our past seminars, you are welcome to subscribe to our YouTube Channel and watch our videos!
Past Events
February 15, 2022
Speaker: Kevin Webster
Title of Presentation: “How Price Impact Distorts Accounting P&L – Revisiting Caccioli, Bouchaud and Farmer’s Impact-Adjusted Valuation”
March 22, 2022
Speaker: Maarten Scholl (Oxford)
Title of Presentation: “Studying Market Ecology Using Agent-Based Models”
Upcoming Events
April 2022
Speaker: Felix Prenzel (Oxford)
Title of Presentation: TBD
Join us for the Brooklyn Quant Experience (BQE) Lecture Series today, Thursday, March 31st at 6 pm ET on Zoom.
“When to Sell an Asset? – A Distribution Builder Approach”
(This is joint work with Peter Carr.)
Stephan Sturm
Associate Professor
Mathematical Sciences
Worcester Polytechnic Institute
*Please note a meeting password is required for this event.
Meeting ID: 947 7419 9649
Password: BQESS331
Abstract
We revisit the question of the optimal time of an asset sale from the point of view of Sharpe’s “Distribution Builder” approach: Instead of assuming the investor’s risk preferences in form of a utility function, the investor provides themself a distribution that should be attained when selling the asset at a stopping time (specified a priori). This obviously begs the question of which distributions are attainable for an investor. We connect this problem to the Skorokhod embedding problem for one-dimensional diffusions and provide explicit representation for optimal stopping times as well as their expected values. In the case that the target distribution is specified from a parametrized family (e.g., log-normal distributions), we show that optimality involves a mean-variance trade-off similar to the efficient frontier in Markowitz’s approach to portfolio optimization. This is joint work with Peter Carr.
Bio
Stephan Sturm is an Associate Professor of Mathematical Sciences at Worcester Polytechnic Institute (WPI) in Massachusetts. After obtaining his Ph.D. in Mathematics from TU Berlin (Germany), he became a Postdoctoral Research Associate and Lecturer at ORFE before joining WPI as a faculty member. Sturm’s research covers mainly different areas of financial mathematics, but he is interested in stochastic modeling in general, such as applications to climate science. In finance, his work is devoted in particular to questions of value adjustments for derivative securities (XVAs), optimal portfolio selection, and systemic risk in financial markets.
