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1 SIAG/FME virtual seminars series

[edit] SIAG/FME virtual seminars series

The series of virtual talks, started by the SIAM Activity Group on Financial Mathematics and Engineering (SIAG/FME), aims at keeping the mathematical finance community connected worldwide beyond traditional formats. The goal is to host a diverse, across all dimensions, lineup of prominent speakers that will present the latest developments in the area of financial mathematics and engineering.

The talks will be once a month, usually on the second Thursday of the month.
The talks will alternate with those set up by the Bachelier Finance Society
All talks will be delivered remotely using Zoom.
The talks are open to the public. Due to security reasons, all attendees have to register.
The registration link will be posted on this web-site, next to the each seminar date below. The detailed information about each talk, and the registration link will be also distributed via SIAM-Engage platform.
The registration is quick (asks only for your name and email), and once registered, you will receive an email with the link to the meeting(s), which is unique to you, so please do not share that email. The registration is usually valid for multiple future talks.

SIAG/FME Seminar Series Committee:

Chair: Samuel Cohen,
Vice Chair: Christa Cuchiero,
Program Director: Luitgard A. M. Veraart,
Secretary: Ibrahim Ekren

The committee is in charge of the scientific component of the seminar, including selecting the speakers and the format of the events. Suggestions from the public on potential speakers, covered topics as well as general recommendation on how to improve the series are welcome and can be addressed to any committee member.

[edit] Forthcoming Talks

We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is

June 11, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Sergio Pulido, ensIIE

Title: Boundary attainment conditions for stochastic Volterra equations

Abstract: In this presentation, I will discuss boundary attainment conditions for one-dimensional stochastic Volterra equations (SVEs) of convolution type. In the first part of the talk, I will present an Osgood-type test for explosion to infinity of SVEs driven by additive noise, featuring kernels from a family that includes the fractional kernel. I will also investigate stability results for explosion times with respect to the kernels, including the case of an Euler-Maruyama approximation scheme. In the second part, I will present a Feller-type test that establishes, on a general open interval of the real line, necessary and sufficient conditions for boundary attainment of solutions to SVEs with possibly multiplicative noise. Here, I will consider dynamics governed by nonsingular kernels, which preserve the semimartingale property of the processes while introducing memory effects through a path-dependent drift. I will also show an application of these results to the Volterra square-root diffusion. The talk is based on joint works with Alessandro Bondi.

Bio: Sergio Pulido is an Associate Professor (Maître de conférences HDR) at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ensIIE) and a permanent member of the Laboratoire de Mathématiques et Modélisation d'Évry (LaMME), a joint research unit of the Centre National de la Recherche Scientifique (CNRS), Université Évry Paris-Saclay, and the ensIIE.

He currently serves as Head of the International Relations Office at the ensIIE and co-manages the M1 in Applied Mathematics (Évry site) at Université Paris-Saclay. He is also an Associate Researcher in the Mathematical Finance group at the Centre de Mathématiques Appliquées (CMAP) of École Polytechnique.

Before joining ensIIE, he was a Postdoctoral Researcher at the Swissquote Chair in Quantitative Finance at the École Polytechnique Fédérale de Lausanne (EPFL), and a Postdoctoral Associate in Applied Probability and Mathematical Finance at Carnegie Mellon University. He received a PhD in Mathematics from Cornell University, an M.S. in Mathematics from Universidad de los Andes, and a B.S. in Mathematics from Universidad Nacional de Colombia.

Sergio Pulido’s recent research focuses on stochastic models with rough trajectories and their applications in finance. From a more theoretical perspective, he has studied stochastic processes solving stochastic convolution equations, namely Stochastic Volterra Equations (SVEs).

[edit] Past Talks

May 14, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Ruimeng Hu, University of California, Santa Barbara

Title: Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning

Abstract: Machine learning has become an increasingly useful tool for solving high-dimensional stochastic control and game problems that are difficult to handle with classical numerical methods. In this talk, I will begin with a general overview of several learning-based approaches for stochastic control and games, including direct policy parameterization, PDE-based methods, and BSDE-based methods, and discuss how these ideas extend to multi-agent and mean-field settings. I will then focus on recent joint work on a new learning framework for mean-field games, called mean-field actor-critic flow. The method combines actor-critic ideas from reinforcement learning with an optimal transport-based update of the population distribution, leading to a coupled learning dynamic for the value function, policy, and mean-field law. I will describe the main algorithmic ideas, discuss a global exponential convergence result under suitable time-scale separation, and present numerical examples illustrating the method.

Bio: Ruimeng Hu is an Associate Professor in the Department of Mathematics and the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research interests include stochastic control, mean-field games, machine learning, and their applications in finance, economics, and multi-agent systems. Before joining UCSB, she was a Term Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University. Her research is supported by grants from the National Science Foundation and the Office of Naval Research. She also serves as an Associate Editor for SIAM Journal on Financial Mathematics and Digital Finance.

April 9, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Yufei Zhang, Imperial College London

Title: An alpha-potential game framework for dynamic N-player games

Abstract: Game theory has a long history, yet identifying Nash equilibria in dynamic non-cooperative games remains a fundamental challenge with significant computational and conceptual complexity. Over the past decade, mean field game theory has emerged as a pivotal framework, offering important theoretical insights and computational advances for the analysis of large-scale stochastic games. However, mean field games require homogeneity and weak interactions among players and focus only on the limiting behavior when the number of players goes to infinity.

In this talk, we present a new paradigm for dynamic N-player games, called alpha-potential games, where the change of a player's objective function resulting from a unilateral deviation of her strategy is equal to the change of an alpha-potential function up to an error alpha. Within this framework, the problem of computing approximate Nash equilibria reduces to a stochastic control problem for the alpha-potential function, significantly simplifying both analysis and computation. The parameter alpha also reveals important structural properties of the game, such as the population size, the intensity of player interactions, and the degree of heterogeneity across players. We will discuss through simple examples some recent theoretical and algorithmic developments, along with a few open problems for this new game framework.

Bio: Yufei Zhang is an Associate Professor in Mathematical Finance and Machine Learning in the Department of Mathematics at Imperial College London, where he also serves as Co-Director of the MSc in Mathematics and Finance program. Before joining Imperial, he was an Assistant Professor in the Department of Statistics at the London School of Economics and Political Science. He earned his PhD in Mathematics from the University of Oxford in 2021. Yufei was awarded the J.P. MorganChase Faculty Research Award in 2025 for his work on the mathematics of artificial intelligence.

Yufei’s research lies at the intersection of stochastic control, game theory, machine learning, and mathematical finance, with a particular emphasis on developing theoretical foundations and algorithmic frameworks for complex decision-making in dynamic and uncertain environments.

March 12, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Eduardo Abi Jaber, Ecole Polytechnique

Title: Path-Signatures: Memory and Stationarity

Abstract: We explore the interplay between path-signatures, memory, and stationarity, highlighting their implications for machine learning, representation of stochastic processes and applications in mathematical finance. In a first part, we provide explicit series expansions to certain stochastic path-dependent integral equations in terms of the path signature of the time augmented driving Brownian motion. Our framework encompasses a large class of stochastic linear Volterra and delay equations and in particular the fractional Brownian motion with a Hurst index H in (0, 1). Our expressions allow to disentangle an infinite dimensional Markovian structure. In addition they open the door to: (i) straightforward and simple approximation schemes that we illustrate numerically, (ii) representations of certain Fourier-Laplace transforms in terms of a non-standard infinite dimensional Riccati equation with important applications for pricing and hedging in quantitative finance. In a second part, we introduce a time-invariant version of the signature: the fading-memory signature, with powerful algebraic, analytic and probabilistic properties and applications to learning stationary relationships in time series. This is based on joint works with Paul Gassiat, Louis-Amand Gérard, Yuxing Huang, Dimitri Sotnikov.

Bio: Eduardo Abi Jaber is a Professor of Applied Mathematics at Ecole Polytechnique. He defended his Habilitation à Diriger des Recherches in 2024 and his PhD in 2018.

His research investigates the role of memory in quantitative finance, advancing the mathematical foundations of sophisticated tools such as Volterra processes and path signatures. Beyond theory, his work translates into practical solutions to key challenges in the field, including volatility modeling and portfolio optimization. Positioned at the crossroads of mathematics and finance, his research combines rigorous analysis, advanced modeling, bespoke numerical methods, and systematic validation against real-world data.

Author of more than 40 papers, with publications in leading journals in applied probability and quantitative finance, Eduardo’s contributions have been recognized with several prestigious awards, including the Amies Prize for the best CIFRE PhD thesis in applied mathematics (2019) and the Junior Scholar Award of the Bachelier Finance Society (2018). He has delivered over 100 invited talks worldwide. He serves as an Associate Editor for Mathematical Finance and the International Journal of Theoretical and Applied Finance, and co-organizes the internationally recognized Bachelier Seminar in Paris. Over the years, he has led a research group comprising more than 10 PhD students and postdoctoral researchers.

February 12, 2026, 1PM-2.30PM (EST) Registration link:

Speaker: Christian Bayer, WIAS Berlin

Title: Global and local regression: a signature approach with applications

Abstract: The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process -- or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.

Bio: Christian Bayer obtained his PhD at the TU Vienna on numerical methods for stochastic differential equations. He is working as a Senior Researcher at the Weierstrass Institute of Applied Analysis and Stochastics in Berlin. His research interests are in rough volatility, computational finance, stochastic numerics, and stochastic optimal control.

December 11, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Erhan Bayraktar, University of Michigan

Title: A Mean-Field Approach to DeFi Currency Exchanges

Abstract: We investigate the behavior of liquidity providers (LPs) by modeling a decentralized cryptocurrency exchange (DEX) based on Uniswap v3. LPs with heterogeneous characteristics choose optimal liquidity positions subject to uncertainty regarding the size of exogenous incoming transactions and the prices of assets in the wider market. They engage in a game among themselves, and the resulting liquidity distribution determines the exchange rate dynamics and potential arbitrage opportunities of the pool. We calibrate the distribution of LP characteristics based on Uniswap data and the equilibrium strategy resulting from this mean-field game produces pool exchange rate dynamics and liquidity evolution consistent with observed pool behavior. We subsequently introduce Maximal Extractable Value bots who perform Just-In-Time liquidity attacks, and develop a Stackelberg game between LPs and bots. This addition results in more accurate simulated pool exchange rate dynamics and stronger predictive power regarding the evolution of the pool liquidity distribution.

Bio: Erhan Bayraktar, the Susan Smith Chair holder, is a full professor of Mathematics at the University of Michigan, where he has taught since 2004. His research spans stochastic analysis, control, applied probability, mean field games, machine learning, and mathematical finance, with applications in financial risk management. He serves as a corresponding editor for the SIAM Journal on Control and Optimization and sits on the editorial boards of Applied Mathematics and Optimization, Frontiers in Mathematical Finance, Mathematics of Operations Research, and Mathematical Finance. Bayraktar has secured continuous funding from the National Science Foundation, including a prestigious CAREER grant. Since 2015, he has directed the Risk Management and Quantitative Finance Masters program, shaping its development. He has mentored 17 Ph.D. students and over 40 post-docs, many now leading in academia and industry.

November 13, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Luciano Campi, University of Milan

Title: Optimal coarse correlated equilibria in mean field games

Abstract: We will consider coarse correlated equilibria (CCE) in continuous time mean field games. CCEs are generalizations of Nash equilibria, when a moderator (correlation device) recommend strategies to the players that are not convenient to unilaterally reject. We will first address existence and approximations results when the number of players goes to infinity. Second, we will provide a linear programming approach through the notion of relaxed strategies in the same spirit as the works by Kurtz and Stockbridge, which have been recently extended to mean field games in several papers by Bouveret, Dumitrescu, Leutscher and Tankov. Within such a linear programming setting and under some regularity assumptions, we will show existence of an optimal CCE with respect to a fixed criterion for the moderator. Finally, we will propose an equivalent Lagrangian formulation and a primal-dual algorithm to compute an optimal CCE numerically. This talk is based on joint papers with F. Cannerozzi, F. Cartellier, M. Fischer and I. Tzouanas.

Bio: Luciano Campi is currently Full Professor of Probability and Mathematical Statistics at the Department of Mathematics "Federigo Enriques", University of Milan. His recent research focuses on stochastic control, stochastic differential games, mean field games, and their applications to energy markets. Before joining the University of Milan, he held academic positions at the London School of Economics, University Paris 13, and University Paris Dauphine. He earned his PhD in Mathematics from the University of Paris 6 and in Computational Mathematics from the University of Padua. Luciano Campi is an Associate Editor for the IMA Journal of Applied Mathematics and Decisions in Economics and Finance.

October 9, 2025, 1PM-2.30PM (EST) Registration link:

Speaker: Nicole Bäuerle, Karlsruhe Institute of Technology

Title: Competitive portfolio optimization

Abstract: Within a common arbitrage-free semimartingale financial market we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their relative wealth. The utility function can be rather general here. Exploiting the linearity of the stochastic integral and making use of the classical pricing theory we are able to express all Nash equilibrium investment strategies in terms of the optimal strategies for the classical one agent expected utility problems. We give applications to specific financial markets and compare our results with those given in the literature. A more specific model with price impacts is also discussed. Moreover, we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their wealth under the constraint that with certain probability the own wealth exceeds a linear combination of the others. We compare the investment strategy to the optimal one without competition. (Joint work with T. Göll)

Bio: Nicole Bäuerle received the Ph.D. degree in mathematics from Ulm University, Ulm, Germany, in 1996. Since 2005, she has been a Professor of probability with the Karlsruhe Institute of Technology, Karlsruhe, Germany. From 2002 to 2005, she was a Professor of insurance mathematics with the University of Hannover, Hannover, Germany. She has authored or coauthored more than 80 papers and a book jointly with Ulrich Rieder on Markov Decision Processes with Applications to Finance. Her research interests include stochastic processes and control with applications to finance, insurance, and stochastic networks. Dr. Bäuerle has served on the editorial board of many journals and is currently Deputy Editor in Chief of the Journal of Applied Probability and an Associate Editor of Statistics and Risk Modeling.

September 11, 2025, 1PM-2.30PM (EST) Registration link and this edition will feature two speakers:

Speaker: Chiara Amorino, Universitat Pompeu Fabra

Title: Minimax rate for multivariate data under componentwise local differential privacy constraints

Abstract: Our research analyses the trade-off between maintaining privacy and preserving statistical accuracy when dealing with multivariate data subject to componentwise local differential privacy (CLDP). Under CLDP, each component of the private data is released through a separate privacy channel. This allows for varying levels of privacy protection for different components or for the privatization of each component by different entities, each with their own distinct privacy policies. It also covers practical situations where it is impossible to privatize all components of the raw data jointly.

We develop general techniques for establishing minimax bounds that quantify the statistical cost of privacy as a function of the privacy levels \alpha_1,…,\alpha_d of the d components. The versatility and efficiency of these techniques are demonstrated through various statistical applications. Specifically, we examine nonparametric density estimation and joint moments estimation under CLDP, providing upper and lower bounds that match up to constant factors, along with an associated data-driven adaptive procedure. We also conduct a detailed analysis of the effective privacy level, exploring how information about a private characteristic of an individual may be inferred from the publicly visible characteristics of the same individual.

Bio: Chiara Amorino is currently an Assistant Professor in the Statistics Group at Universitat Pompeu Fabra in Barcelona, a position she has held since April 2024. In 2025, she was awarded the prestigious Ramón y Cajal Fellowship in Mathematics, a five-year individual grant from the Spanish Ministry of Economy, Industry and Competitiveness. Since 2024, she has also served as Chair of the Bernoulli Young Researchers Committee for Europe.

Before joining UPF, she was a postdoctoral researcher at the University of Luxembourg in the group of Prof. Mark Podolskij. She earned her PhD in Mathematics from Université Paris-Saclay (LaMME) under the supervision of Prof. Arnaud Gloter, defending her thesis in July 2020.

Her research interests include statistical inference for stochastic differential equations, interacting particle systems, Hawkes processes, fractional processes, and local differential privacy.

Speaker: Fayçal Drissi, University of Oxford

Title: Equilibrium Liquidity Provision in Concentrated Liquidity Automated Market Makers

Abstract: Automated market makers (AMMs) with concentrated liquidity (CL) are the most widely used decentralised exchanges, with daily trading volumes around $4 billion. In CL markets, liquidity providers (LPs) strategically choose price ranges to balance fee revenues against adverse selection losses. We develop a model of competition among LPs and characterise the equilibrium distribution of liquidity across ranges. The analysis shows how equilibrium outcomes depend on the number of competing LPs, the ratio of informed to uninformed trading flow, and wealth heterogeneity among liquidity providers. Finally, we examine the role of “noise” liquidity provision and show how it affects equilibrium allocations and execution costs.

Bio: Fayçal Drissi is currently a postdoctoral researcher at the Oxford-Man Institute, University of Oxford. He obtained a Ph.D. in Mathematics from Université Paris 1 Panthéon-Sorbonne in 2023. His thesis focused on the microstructure of traditional electronic markets and decentralised exchanges that use Automated Market Makers (AMMs). Prior to his doctoral studies, he spent five years in the hedge fund industry doing research and development related to systematic trading and global macro.

June 12, 2025, 1PM-2.30PM (EST) Registration link and this edition will feature two speakers:

Speaker: Valentin Tissot-Daguette, Bloomberg

Title: Pathwise Superhedging of Asian Claims

Abstract: The talk unveils pathwise superhedging strategies for convex Asian claims using a dynamic hedge in the underlying and a static position in vanilla options. For an Asian call, where the seller is long the matching vanilla contract, the dynamic hedge may involve the time spent by the asset - or its running average - above the strike. The validity of average-based strategies stems from a mysterious identity relating the Asian call payoff to a strip of binary options across maturities.

The strategies are then tested on synthetic data, where we compare the variance of their P&Ls and hedging turnover. We finally connect these findings with Martingale Optimal Transport and derive robust price bounds for forward start (convex) Asian claims.

Special thanks to Bruno Dupire, Hélyette Geman, Julien Guyon, Bryan Liang, Marcel Nutz, and Nizar Touzi.

Bio: Valentin Tissot-Daguette is a quantitative researcher at Bloomberg. He recently obtained his PhD degree from Princeton University, under the co-supervision of Prof. Mete Soner and Bruno Dupire. Previously, Valentin studied at EPFL and ETH Zurich where he completed a Bachelor's degree in Mathematics and a Master's degree in Financial Engineering. His research interests include exotic derivatives, free boundary problems, and stochastic control.

Speaker: Purba Das, King's College London

Title: Invariance of Stochastic integral with respect to the choice of partitions

Abstract: We study the concept of quadratic variation of a continuous path along a sequence of partitions and its dependence with respect to the choice of the partition sequence. We introduce the concept of quadratic roughness of a path along a partition sequence and show that for Hölder-continuous paths satisfying this roughness condition, the quadratic variation along balanced partitions is invariant with respect to the choice of the partition sequence. We further present several constructions of paths and processes with finite quadratic variation along a refining sequence of partitions and, in particular, study the dependence of quadratic variation with respect to the sequence of partitions for these constructions.

Bio: Purba Das is a Lecturer (Assistant Professor) in Financial Mathematics in the Department of Mathematics at King’s College London. Before KCL, she was a Byrne Research Assistant Professor of Mathematics at the University of Michigan for one year. She completed her DPhil in Mathematics at the University of Oxford under the supervision of Professor Rama Cont.

May 8, 2025, 1PM-2.30PM (EST) Registration link

Speaker: Julio Backhoff, University of Vienna

Title: Of ‘most exciting’ games and the specific relative entropy between martingales

Abstract: The laws of two continuous martingales will typically be singular to each other and hence have infinite relative entropy. But this does not need to happen in discrete time. This suggests defining a new object, the specific relative entropy, as a scaled limit of the relative entropy between the discretized laws of the martingales. This definition goes back to Nina Gantert’s PhD thesis, and in recent time Hans Foellmer has rekindled the study of this object. Independently, this object has made sporadic appearances in finance over the years, for instance in works by Avellaneda et al. and more recently Dolinsky and Cohen.

In this talk we will first discuss the existence of a closed-form formula for the specific relative entropy, depending on the quadratic variation of the involved martingales. Next we will describe an application of this object to prediction markets. Concretely, David Aldous asked in an open question to determine the ‘most exciting’ game, i.e. the prediction market with the highest entropy. With M. Beiglböck we give an answer to this question by solving a stochastic control problem whose cost criterion is the specific relative entropy. Finally we will discuss an application to the ‘most exciting’ game with multiple outcomes, based on joint work with Wang and Zhang, highlighting a novel connection to the field of Monge-Ampere equations.

Bio: Julio Backhoff obtained his PhD in Mathematics from Humboldt-Universität zu Berlin, in 2015, under the supervision of Prof. Ulrich Horst. From 2015 to 2019, he held various postdoc and university assistant positions in the group of Prof. Mathias Beiglböck at the University of Vienna and the Vienna University of Technology. In 2020, he became an assistant professor at the University of Twente. Since 2021, he has been an assistant professor at the University of Vienna (Mathematics Faculty, Mathematical Finance and Probability group). His research lies at the intersection of Mathematical Finance, Stochastic control / analysis, and optimal transport theory. For the most part, he is interested in problems coming from model-free finance and model calibration, with an emphasis on adapted and martingale transport.

April 10, 2025, 1PM-2.30PM (EST) Registration link

Speaker: Sara Svaluto-Ferro, University of Verona

Title: Signature-based models: theory, calibration, and expansions

Abstract: Signature methods provide a non-parametric approach to extracting features from trajectories, offering versatile applications in finance. The structure of signature components enables the use of advanced mathematical tools and the construction of highly general models capable of capturing diverse behaviors.

In this talk, we introduce the concept of the signature and its key properties, illustrating its potential through two financial applications.

The first application focuses on a stochastic volatility model where volatility dynamics are described by linear functions of the (time-extended) signature of a primary process. When this process is of polynomial type, its truncated signature retains this structure, allowing for closed-form expressions of the squared VIX. By incorporating the Brownian motion driving the stock price, both the log-price and the squared VIX can be expressed linearly in terms of the signature of the augmented process, achieving highly accurate calibration results for SPX and VIX options.

The second application examines the local-in-time expansion of a continuous-time process and its conditional moments, including the characteristic function. By leveraging the time-extended Itô signature—composed of iterated integrals of deterministic and stochastic signals (time, multiple correlated Brownian motions, and compound Poisson processes)—we derive automated expansions to any order with explicit coefficients. This provides stochastic representations suitable for asymptotic analysis in the short-time limit.

Bio: Sara Svaluto-Ferro is an Associate Professor at the University of Verona, Department of Economics, specializing in mathematical methods for economics and finance. Previously, she was an Assistant Professor (RTDB) at the same department and a postdoctoral researcher at the University of Vienna in the research group of Prof. C. Cuchiero. She earned her PhD in Mathematics from ETH Zurich under the supervision of Prof. M. Larsson.

Her research focuses on stochastic processes, including affine and polynomial models, stochastic representations of PDEs, and optimization in infinite-dimensional spaces, with applications in financial mathematics, systemic risk, and rough volatility modeling. In the last years she dedicated particular attention to applications of signature processes in finance.

March 20, 2025, 1PM-2.30PM (EST) Registration link

and this edition will feature two speakers:

Speaker: Terry Lyons, University of Oxford

Title: The Mathematics of Complex Streamed Data

Abstract: Multimodal streamed data is essentially different to unimodal streamed data. Consider this:

‘A commuter arrives at a bus stop before the bus’ – they catch it;

‘The bus arrives first’ – they miss it.

These are the same two events, but the order changes everything. Yet most models treat these as identical: ‘A bus and a person arrived’ They ignore timing and relationships.

This simplification isn’t harmless. A timed series gives no information about order within sampling intervals. As a result, the sampling rate has to come from the bottom up if it is to preserve this order information. Rough path theory makes a radical change and describes the stream over an interval using a group element. According to the choice of group it is possible to capture order information and to allow a top down description of the data stream without using essential information about the order of events.

This approach to describing streamed data is important to data science because it reduces the dimension needed for descriptive feature sets and so reduces the size of the data set needed to train. There are numerous prize winning illustrations of the methodology in use and the impact can be measured in the hundreds of millions of US dollars.

Bio: Terry Lyons FLSW FRSE FRS is the Wallis Professor Emeritus and Professor of Mathematics at the University of Oxford, a fellow of St Anne's College, Oxford and a Faculty Fellow at The Alan Turing Institute. He is currently PI of the DataSıg and of the complementary research programme CIMDA-Oxford. He was the President of the London Mathematical Society (2013-2015), the Director (2011-2015) of the Oxford Man Institute of Quantitative Finance and the Director of the Wales Institute of Mathematical and Computational Sciences (2008-2011). He came to Oxford in 2000 having previously been Professor of Mathematics at Imperial College London (1993-2000), and before that he held the Colin Maclaurin Chair at Edinburgh (1985-93).

Professor Lyons’s long-term research interests are all focused on Rough Paths, Stochastic Analysis, and applications – particularly to Finance and more generally to the summarising of large complex data. More specifically, his interests are in developing mathematical tools that can be used to effectively model and describe high dimensional systems that exhibit randomness as well as the complex multimodal data streams that arise in human activity. Professor Lyons is involved in a wide range of problems from pure mathematical ones to questions of efficient numerical calculation. He is, in particular, recognised for developing what is now known as the theory of rough paths.

Speaker: Luhao Zhang, Johns Hopkins University

Title: A Class of Interpretable and Decomposable Multi-period Convex Risk Measures

Abstract: Multi-period risk measures evaluate the risk of a stochastic process by assigning it a scalar value. A desirable property of these measures is dynamic decomposition, which allows the risk evaluation to be expressed as a dynamic program. However, many widely used risk measures, such as Conditional Value-at-Risk, do not possess this property. In this work, we introduce a novel class of multi-period convex risk measures that do admit dynamic decomposition.

Our proposed risk measure evaluates the worst-case expectation of a random outcome across all possible stochastic processes, penalized by their deviations from a nominal process in terms of both the likelihood ratio and the outcome. We show that this risk measure can be reformulated as a dynamic program, where, at each time period, it assesses the worst-case expectation of future costs, adjusting by reweighting and relocating the conditional nominal distribution. This recursive structure enables more efficient computation and clearer interpretation of risk over multiple periods.

Bio: Luhao Zhang is an assistant professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. Before joining JHU, she was a postdoctoral research scientist in the Department of Industrial Engineering and Operations Research at Columbia University from 2023 to 2024. She completed her Ph.D. in Mathematics at the University of Texas at Austin in 2023.

Her research lies on interdisciplinary topics that integrate stochastic analysis, mathematical finance, and robust optimization, with an emphasis on how to exploit information optimally for decision-making in stochastic and uncertain environments from modelling and quantitative aspects. Another recent research interest of hers is the mathematical foundation of generative AI, human-AI interactions, and continuous-time reinforcement learning.

December 12, 2024, 1PM-2PM (EST) Registration link

Speaker:Jose Blanchet, Stanford University

Title: Inference in Stochastic Optimization with Heavy Tailed Input

Abstract: We will start the talk by discussing empirical evidence from a wide range of areas (including insurance, health care, machine learning, among others) suggesting that often infinite variance models are well-suited for inference, particularly in online data-driven decision making. We will argue that infinite variance estimators can be considered appropriate depending on easy-to-monitor features of historical data and on the spatial and temporal scales over which an online algorithm will be deployed (even if the underlying dynamics have finite variance gradients “in theory”). We will then discuss inference tools that can be applied to monitor the quality of solutions of infinite-variance stochastic gradient descent (SGD) based on several asymptotic statistics. Our results extend classical finite-variance weak-convergence analysis of SGD and state-of-the-art infinite variance asymptotic statistics derived under homogeneity conditions which limit the applicability of SGD in typical online optimization tasks. Based on joint work with Aleks Mijatovic, Wenhao Yang.

Bio: Jose Blanchet is a faculty member in the Management Science and Engineering Department at Stanford University – where he earned his Ph.D. in 2004. Prior to joining the Stanford faculty, Jose was a professor in the IEOR and Statistics Departments at Columbia University (2008-2017) and before that he was faculty member in the Statistics Department at Harvard University (2004-2008). Jose is a recipient of the 2009 Best Publication Award given by the INFORMS Applied Probability Society and of the 2010 Erlang Prize. He also received a PECASE award given by NSF in 2010. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He serves in the editorial board of ALEA, Advances in Applied Probability, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Mathematics of Operations Research, and Stochastic Systems.

November 14, 2024, 1PM-2PM (EST) Registration link

Speaker:Ben Hambly, University of Oxford

Title: Systemic risk, endogenous contagion and McKean-Vlasov control

Abstract: We consider some particle system models for systemic risk. The particles represent the health of financial institutions and we incorporate common noise and contagion into their dynamics. Defaults within the system reduce the financial health of other institutions, causing contagion. By taking a mean field limit we derive a McKean-Vlasov equation for the financial system as a whole. The task of a central planner, who wishes to control the system to prevent systemic events at minimal cost, leads to a novel McKean-Vlasov control problem. We discuss the mathematical issues and illustrate the results numerically.

Bio: Ben Hambly received his PhD in 1990 from the University of Cambridge and held lectureships at the Universities of Edinburgh and Bristol before moving to Oxford in 2000. He has interests in stochastic PDEs, rough paths, random processes in random and fractal environments, reinforcement learning ,modelling order books, systemic risk and electricity markets.

October 10, 2024, 1PM-2PM (EST)' Registration link

Speaker: Maxim Bichuch, University at Buffalo

Title: A Deep Learning Scheme for Solving Fully Nonlinear Partial Differential Equation

Abstract: We study the convergence of a deep learning algorithm applied to a general class of fully nonlinear second order Partial Differential Equations. By using a suitable finite difference approximation to the loss function of the deep learning scheme we show the convergence of the numerical solution to the unique viscosity solution. We apply our results and illustrate this convergence to the finite horizon optimal investment problem with proportional transaction costs in single and multi-asset settings.

Bio: Maxim Bichuch holds a M.S. from NYU and a Ph.D. from CMU both in Financial Mathematics. He has been a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at WPI and JHU, before joining the department of Mathematics at UB. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal portfolio selection, optimal investment and consumption, optimal control with transaction costs, viscosity solutions, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning, decentralized finance and fintech.

June 13, 2024, 1PM-2PM (EST)

Speaker: Miklós Rásonyi, HUN-REN Alfréd Rényi Institute of Mathematics

Title: Portolio choice for exponential investors when prices are mean-reverting

Abstract: Several asset classes show mean-reverting features, e.g. commodities, commodity futures, long-term safe assets (gold). We investigate the portfolio choice problem for investors with exponential utilities (=high risk aversion) as the investment horizon T tends to infinity. It turns out that the optimal equivalent safe rate grows in a superlinear way, depending on the strength of the mean-reversion effect. We cannot find the exact optimisers but construct a family of simple, explicit strategies that are optimal asymptotically (they generate equivalent safe rates of the optimal order). Interestingly, the presence or absence of a drift leads to entirely different conclusions, the nonzero drift case spectacularly outperforming the driftless one. Time permitting, we also review some

[edit] Past steering committees

2021-2022

\quad Agostino Capponi (SIAG/FME Chair, Columbia University)

\quad Igor Cialenco (SIAG/FME Program Director, Illinois Institute of Technology)

\quad Sebastian Jaimungal (University of Toronto)

\quad Ronnie Sircar (Princeton University)

Retrieved from "http://wiki.siam.org/siag-fm/index.php/Current_events"

Current events

From SIAG-FM

Current revision

Contents

[edit] SIAG/FME virtual seminars series

[edit] Forthcoming Talks

[edit] Past Talks

[edit] Past steering committees

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 === Forthcoming Talks ===
+We are delighted that we have joined forces with the Bachelier Finance Society to implement a joint online seminar series. The next date is
-'''April 11, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+'''June 11, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-''Speaker:'' [https://sites.google.com/view/ofelia-bonesini Ofelia Bonesini], Imperial College London
+''Speaker:'' [https://sites.google.com/site/sergiopulidonino/home Sergio Pulido], ensIIE
-''Title:''  Rough volatility, path-dependent PDEs and weak rates of convergence
+[[Image:sergio_pulido.jpeg|200px|Image: 200 pixels]]
-''Abstract:''   In the setting of stochastic Volterra equations, and in particular rough volatility models, we show that conditional expectations are the unique classical solutions to path dependent PDEs. The latter arise from the functional Itô formula developed by [Viens, F. Zhang, J. (2019). A martingale approach for fractional Brownian motions and related path dependent PDEs. Ann. Appl. Probab.]. We then leverage these tools to study weak rates of convergence for discretised stochastic integrals of smooth functions of a Riemann-Liouville fractional Brownian motion with Hurst parameter H in (0,1/2). These integrals approximate log-stock prices in rough volatility models. We obtain weak error rates of order 1 if the test function is quadratic and of order H+1/2 for smooth test functions.
-----
+''Title:''  Boundary attainment conditions for stochastic Volterra equations
-----
-=== Past Talks ===
-'''March 14, 2024, 1PM-2:30PM (EST)'''  [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-SIAG/FME CodeQuest 2024 Finalists
-This SIAG/FME virtual seminar will feature the five finalist (Team Floor F, UW Goose, Blanco, APAM, and QuantHub) in the SIAG/FME CodeQuest 2024. This year’s CodeQuest features  a problem based on Decentralized Finance and Automated Asset Management. Teams were tasked first with the challenge of implementing aspects of an automatic market maker (AMM) and second with constructing a portfolio over a pool of AMMs that minimizes the portfolio’s CVaR while simultaneously attaining a probabilistic constraint on terminal wealth. Teams will have the chance to present their approach to the optimization problem and field questions from the committee. Come cheer them on as they compete for a cash prize!
+''Abstract:'' In this presentation, I will discuss boundary attainment conditions for one-dimensional stochastic Volterra equations (SVEs) of convolution type. In the first part of the talk, I will present an Osgood-type test for explosion to infinity of SVEs driven by additive noise, featuring kernels from a family that includes the fractional kernel. I will also investigate stability results for explosion times with respect to the kernels, including the case of an Euler-Maruyama approximation scheme. In the second part, I will present a Feller-type test that establishes, on a general open interval of the real line, necessary and sufficient conditions for boundary attainment of solutions to SVEs with possibly multiplicative noise. Here, I will consider dynamics governed by nonsingular kernels, which preserve the semimartingale property of the processes while introducing memory effects through a path-dependent drift. I will also show an application of these results to the Volterra square-root diffusion. The talk is based on joint works with Alessandro Bondi.
-----
+''Bio:''  Sergio Pulido is an Associate Professor (Maître de conférences HDR) at the École Nationale Supérieure d'Informatique pour l'Industrie et l'Entreprise (ensIIE) and a permanent member of the Laboratoire de Mathématiques et Modélisation d'Évry (LaMME), a joint research unit of the Centre National de la Recherche Scientifique (CNRS), Université Évry Paris-Saclay, and the ensIIE.
-'''February 8, 2024, 1PM-2PM (EST)'''
+He currently serves as Head of the International Relations Office at the ensIIE and co-manages the M1 in Applied Mathematics (Évry site) at Université Paris-Saclay. He is also an Associate Researcher in the Mathematical Finance group at the Centre de Mathématiques Appliquées (CMAP) of École Polytechnique.
-''Speaker:'' [https://www.kcl.ac.uk/people/ryan-donnelly Ryan Donnelly], King's College London
+Before joining ensIIE, he was a Postdoctoral Researcher at the Swissquote Chair in Quantitative Finance at the École Polytechnique Fédérale de Lausanne (EPFL), and a Postdoctoral Associate in Applied Probability and Mathematical Finance at Carnegie Mellon University. He received a PhD in Mathematics from Cornell University, an M.S. in Mathematics from Universidad de los Andes, and a B.S. in Mathematics from Universidad Nacional de Colombia.
-[[Image:ryan.jpg]]
-''Title:''  Dynamic Inventory Management with Mean-Field Competition
-''Abstract:''  Agents attempt to maximize expected profits earned by selling multiple units of a perishable product where their revenue streams are affected by the prices they quote as well as the distribution of other prices quoted in the market by other agents. We propose a model which captures this competitive effect and directly analyze the model in the mean-field limit as the number of agents is very large. We classify mean-field Nash equilibrium in terms of the solution to a Hamilton-Jacobi-Bellman equation and a consistency condition and use this to motivate an iterative numerical algorithm to compute equilibrium. Properties of the equilibrium pricing strategies and overall market dynamics are then investigated, in particular how they depend on the strength of the competitive interaction and the ability to oversell the product.
+Sergio Pulido’s recent research focuses on stochastic models with rough trajectories and their applications in finance. From a more theoretical perspective, he has studied stochastic processes solving stochastic convolution equations, namely Stochastic Volterra Equations (SVEs).
 ----
-'''January 11, 2024, 1PM-2PM (EST)'''
-''Speaker:'' [https://sites.google.com/site/athenapicarelli/home/education Athena Picarelli], University of Verona
-[[Image:athena.jpg]]
-''Title:''  A deep solver for BSDEs with jumps
-''Abstract:''  The aim of this work is to propose an extension of the deep solver by Han, Jentzen, E (2018) to the case of forward backward stochastic differential equations (FBSDEs) with jumps. As in the aforementioned solver, starting from a discretized version of the FBSDE and parametrizing the (high dimensional) control processes by means of a family of artificial neural networks (ANNs), the FBSDE is viewed as model-based reinforcement learning problem and the ANN parameters are fitted so as to minimize a prescribed loss function. We take into account both finite and infinite jumpactivity by introducing, in the latter case, an approximation with finitely many jumps of the forward process. We successfully apply our algorithm to option pricing problems in low and high dimension and discuss the applicability in the context of counterparty credit risk.
 ----
+=== Past Talks ===
+'''May 14, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://sites.google.com/site/ruimenghu1/ Ruimeng Hu], University of California, Santa Barbara
-'''December 14, 2023, 1PM-2PM (EST)'''  [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+[[Image:ruimeng_hu.jpeg|200px|Image: 200 pixels]]
-''Speaker:'' [https://www.yujui-huang.com/home Yu-Jui Huang], University of Colorado Boulder
-[[Image:yu-jui1.jpg]]
+''Title:'' Machine Learning for Stochastic Control and Games: From Foundations to Mean-Field Learning
-''Title:'' Partial Information Breeds Systemic Risk
+''Abstract:'' Machine learning has become an increasingly useful tool for solving high-dimensional stochastic control and game problems that are difficult to handle with classical numerical methods. In this talk, I will begin with a general overview of several learning-based approaches for stochastic control and games, including direct policy parameterization, PDE-based methods, and BSDE-based methods, and discuss how these ideas extend to multi-agent and mean-field settings. I will then focus on recent joint work on a new learning framework for mean-field games, called mean-field actor-critic flow. The method combines actor-critic ideas from reinforcement learning with an optimal transport-based update of the population distribution, leading to a coupled learning dynamic for the value function, policy, and mean-field law. I will describe the main algorithmic ideas, discuss a global exponential convergence result under suitable time-scale separation, and present numerical examples illustrating the method.
-''Abstract:''  We consider finitely many investors who perform mean-variance portfolio selection under a relative performance criterion. That is, each investor is concerned about not only her terminal wealth, but how it compares to the average terminal wealth of all investors (i.e., the mean field). At the inter-personal level, each investor selects a trading strategy in response to others' strategies (which affect the mean field). The selected strategy additionally needs to yield an equilibrium intra-personally, so as to resolve time inconsistency among the investor's current and future selves (triggered by the mean-variance objective). A Nash equilibrium we look for is thus a tuple of trading strategies under which every investor achieves her intra-personal equilibrium simultaneously. We derive such a Nash equilibrium explicitly in the idealized case of full information (i.e., the dynamics of the underlying stock is perfectly known), and semi-explicitly in the realistic case of partial information (i.e., the stock evolution is observed, but the expected return of the stock is not precisely known). The formula under partial information involves an additional state process that serves to filter the true state of the expected return. Its effect on trading is captured by two degenerate Cauchy problems, one of which depends on the other, whose solutions are constructed by elliptic regularization and a stability analysis of the state process. Our results indicate that partial information alone can reduce investors' wealth significantly, thereby causing or aggravating systemic risk. Intriguingly, in two different scenarios of the expected return (i.e., it is constant or alternating between two values), our Nash equilibrium formula spells out two distinct manners systemic risk materializes. This is joint work with Li-Hsien Sun.
-''Moderator:'' [https://people.maths.ox.ac.uk/cohens/ Sam Cohen], University of Oxford
-----
-'''November 9, 2023, 1PM-2PM (EST)'''
-''Speaker:'' [https://pesenti.utstat.utoronto.ca/ Silvana Pesenti], University of Toronto
-[[Image:silvana1.jpg]]
-''Title:'' Dynamic robust risk measures with applications
+''Bio:''  Ruimeng Hu is an Associate Professor in the Department of Mathematics and the Department of Statistics and Applied Probability at the University of California, Santa Barbara. Her research interests include stochastic control, mean-field games, machine learning, and their applications in finance, economics, and multi-agent systems. Before joining UCSB, she was a Term Assistant Professor in the Department of Industrial Engineering and Operations Research at Columbia University.  Her research is supported by grants from the National Science Foundation and the Office of Naval Research. She also serves as an Associate Editor for SIAM Journal on Financial Mathematics and Digital Finance.
-''Abstract:''  This talk is focused on distributionally robust risk measures, which are the largest value a risk measure can attain within an uncertainty set. Uncertainty sets are typically characterised by balls around a reference distribution, thus containing plausible alternative distributions. I first discuss worst-case distortion risk measures for Wasserstein uncertainty sets and an application to portfolio optimisation.
-Second, I discuss the dynamic setting, where the risk of stochastic processes is evaluated using time-consistent dynamic risk measures. In the dynamic setting I introduce dynamic robust risk measures and dynamic uncertainty sets and study conditions on the uncertainty sets that lead to well-known properties of dynamic robust risk measures, such as convexity and coherence. Furthermore, we proof necessary and sufficient properties for a robust dynamic risk measures to be time-consistent and to admit a recursive representation.
-''Moderator:'' [https://personal.lse.ac.uk/veraart/ Luitgard A. M. Veraart], LSE
 ----
+'''April 9, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-'''October 12, 2023, 1PM-2PM (EST)'''
+''Speaker:'' [https://yufei-zhang.github.io/ Yufei Zhang], Imperial College London
-''Speaker:'' [http://ludkovski.pstat.ucsb.edu Mike Ludkovski],  University of California Santa Barbara
+[[Image:yufei_zhang.jpeg|200px|Image: 200 pixels]]
-[[Image:Mike.jpg]]
-''Title:'' Machine Learning Surrogates for Parametric and Adaptive Optimal Execution
+''Title:'' An alpha-potential game framework for dynamic N-player games
-''Abstract:'' We investigate optimal order execution with dynamic parametric uncertainty. Our base model features discrete time, stochastic transient price impact generalizing Obizhaeva and Wang (2013). We first consider learning the optimal strategy across a multi-dimensional range of model configurations, including price impact and resilience parameters, as well as initial stochastic states. We develop a numerical algorithm based on dynamic programming and deep learning, utilizing an actor-critic framework to construct two neural-network (NN) surrogates for the value function and the feedback control. We then apply the lens of adaptive robust stochastic control to consider online statistical learning of model parameters along with a worst-case min-max optimization. Thus, the controller is dynamically learning model parameters based on her observations while explicitly accounting for Bayesian uncertainty of the learned parameter estimates via a posterior uncertainty set. We propose a modeling framework which allows a time-consistent 3-way marriage between dynamic learning, dynamic robustness and dynamic control. We extend our NN approach to tackle the resulting 7-dimensional adaptive robust optimal order execution problem, and illustrate with comparisons to alternative frameworks, such as adaptive or static robust strategies. This is joint work with Moritz Voss (UCLA) and Tao Chen (QRM).
+''Abstract:'' Game theory has a long history, yet identifying Nash equilibria in dynamic non-cooperative games remains a fundamental challenge with significant computational and conceptual complexity. Over the past decade, mean field game theory has emerged as a pivotal framework, offering important theoretical insights and computational advances for the analysis of large-scale stochastic games. However, mean field games require homogeneity and weak interactions among players and focus only on the limiting behavior when the number of players goes to infinity.
-''Moderator:''  Tomoyuki Ichiba, UCSB
+In this talk, we present a new paradigm for dynamic N-player games, called alpha-potential games, where the change of a player's objective function resulting from a unilateral deviation of her strategy is equal to the change of an alpha-potential function up to an error alpha. Within this framework, the problem of computing approximate Nash equilibria reduces to a stochastic control problem for the alpha-potential function, significantly simplifying both analysis and computation. The parameter alpha also reveals important structural properties of the game, such as the population size, the intensity of player interactions, and the degree of heterogeneity across players. We will discuss through simple examples some recent theoretical and algorithmic developments, along with a few open problems for this new game framework.
-----
+''Bio:''  Yufei Zhang is an Associate Professor in Mathematical Finance and Machine Learning in the Department of Mathematics at Imperial College London, where he also serves as Co-Director of the MSc in Mathematics and Finance program. Before joining Imperial, he was an Assistant Professor in the Department of Statistics at the London School of Economics and Political Science. He earned his PhD in Mathematics from the University of Oxford in 2021. Yufei was awarded the J.P. MorganChase Faculty Research Award in 2025 for his work on the mathematics of artificial intelligence.
+Yufei’s research lies at the intersection of stochastic control, game theory, machine learning, and mathematical finance, with a particular emphasis on developing theoretical foundations and algorithmic frameworks for complex decision-making in dynamic and uncertain environments.
-'''September 14, 2023, 1PM-2PM (EST) Early career talks '''
-''Speaker 1:'' [https://www.gokcedayanikli.com/ Gökçe Dayanıkli], University of Illinois Urbana-Champaign
-[[Image:Gokce1.jpg]]
-''Title:'' Single level approach to solve Stackelberg Mean Field Games
-''Abstract:'' In this talk, we introduce the bi-level Stackelberg mean field game problem between a principal and a mean field of agents evolving on a continuous state space and discuss how to write it as a single-level problem to propose an efficient numerical solution. In the model, the agents in the population play a non-cooperative game and choose their controls to optimize their individual objectives by interacting with the principal and other agents in the society through the population distribution. The principal can influence the resulting mean field game Nash equilibrium through incentives in order to optimize her own objective. We analyze this game by using a probabilistic approach. We then rewrite this bi-level problem as a single-level problem and propose a numerical approach to solve the Stackelberg mean field game. We look at different financial applications such as the systemic risk model for a regulator and many banks and optimal contract between a principal and a large number of agents. (This is a joint work with Mathieu Lauriere.)
-''Speaker 2:'' [https://www.imperial.ac.uk/people/d.itkin David Itkin], Imperial College London
-[[Image:David2.jpg]]
-''Title:'' Ergodic robust maximization of asymptotic growth with stochastic factors.
-''Abstract:''  We consider an asymptotic robust growth problem under model uncertainty and in the presence of (non-Markovian) stochastic covariance. Building on the previous work of Kardaras & Robertson we fix two inputs representing the instantaneous covariation and invariant density for the asset process X, but additionally allow these quantities to depend on a stochastic factor process Y. Under mild technical assumptions we show that the robust growth optimal strategy is functionally generated and, unexpectedly, does not depend on the factor process Y. Remarkably this remains true even if the joint covariation of X and Y is prescribed as an input. Our result provides a comprehensive answer to a question proposed by Fernholz in 2002. This talk is based on joint work with Benedikt Koch, Martin Larsson and Josef Teichmann.
-''Moderator:'' [https://sites.google.com/view/cialenco Igor Cialenco], Illinois Tech
 ----
+'''March 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://sites.google.com/view/abijabereduardo/ Eduardo Abi Jaber], Ecole Polytechnique
-'''May 11, 2023, 1PM-2PM (EST)'''  Early Career Talks
+[[Image:eduardo_abijaber.jpg|200px|Image: 200 pixels]]
-''Speaker 1:'' [https://sites.google.com/berkeley.edu/haoyang-cao/home Haoyang Cao], École Polytechnique
-[[Image:Haoyang1.jpg]]
+''Title:'' Path-Signatures: Memory and Stationarity
-''Title:'' Feasibility and Transferability of Transfer Learning: A Mathematical Framework
+''Abstract:'' We explore the interplay between path-signatures, memory, and stationarity, highlighting their implications for machine learning, representation of stochastic processes and applications in mathematical finance. In a first part, we provide explicit series expansions to certain stochastic path-dependent integral equations in terms of the path signature of the time augmented driving Brownian motion. Our framework encompasses a large class of stochastic linear Volterra and delay equations and in particular the fractional Brownian motion with a Hurst index H in (0, 1). Our expressions allow to disentangle an infinite dimensional Markovian structure. In addition they open the door to: (i) straightforward and simple approximation schemes that we illustrate numerically, (ii) representations of certain Fourier-Laplace transforms in terms of a non-standard infinite dimensional Riccati equation with important applications for pricing and hedging in quantitative finance. In a second part, we introduce a time-invariant version of the signature: the fading-memory signature, with powerful algebraic, analytic and probabilistic properties and applications to learning stationary relationships in time series. This is based on joint works with Paul Gassiat, Louis-Amand Gérard, Yuxing Huang, Dimitri Sotnikov.
-''Abstract:'' Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. Despite its numerous empirical successes, theoretical analysis for transfer learning is limited. In this talk we introduce for the first time, to the best of our knowledge, a mathematical framework for the general procedure of transfer learning. Our unique reformulation of transfer learning as an optimization problem allows the analysis of its feasibility. Additionally, we propose a novel concept of transfer risk to evaluate transferability of transfer learning. At the end we will demonstrate how this framework can be embedded in both a generic image classification problem and a portfolio optimization problem to demonstrate the potential and benefits of incorporating transfer risk in the evaluation of transfer learning performance. This talk is primarily based on “Feasibility and Transferability of Transfer Learning: A Mathematical Framework” by Cao, Gu, Guo and Rosenbaum.
+''Bio:''  Eduardo Abi Jaber is a Professor of Applied Mathematics at Ecole Polytechnique. He defended his Habilitation à Diriger des Recherches in 2024 and his PhD in 2018.
+His research investigates the role of memory in quantitative finance, advancing the mathematical foundations of sophisticated tools such as Volterra processes and path signatures. Beyond theory, his work translates into practical solutions to key challenges in the field, including volatility modeling and portfolio optimization. Positioned at the crossroads of mathematics and finance, his research combines rigorous analysis, advanced modeling, bespoke numerical methods, and systematic validation against real-world data.
-''Speaker 2:'' [https://sites.google.com/view/stephan-eckstein/startseite Stephan Eckstein], ETH Zürich
+Author of more than 40 papers, with publications in leading journals in applied probability and quantitative finance, Eduardo’s contributions have been recognized with several prestigious awards, including the Amies Prize for the best CIFRE PhD thesis in applied mathematics (2019) and the Junior Scholar Award of the Bachelier Finance Society (2018).  He has delivered over 100 invited talks worldwide. He serves as an Associate Editor for Mathematical Finance and the International Journal of Theoretical and Applied Finance, and co-organizes the internationally recognized Bachelier Seminar in Paris. Over the years, he has led a research group comprising more than 10 PhD students and postdoctoral researchers.
-[[Image:Stephan1.jpg]]
-''Title:'' Numerical aspects of adapted optimal transport
-''Abstract:'' Recent work has established that the adapted Wasserstein distance satisfies many desirable theoretical properties for multi-period market models, guaranteeing for instance robustness against model misspecification and geodesic closedness of the set of martingale models. To actually benefit from such properties in practice, one requires ways to numerically calculate adapted optimal transport problems. The goal of this talk is to present various computational tools aimed at working with adapted optimal transport in practice. Among others, we show feasibility of discrete approximations for continuous problems and how to control errors introduced by regularization methods. Extending the classical theory, we introduce a variant of Sinkhorn's algorithm to solve the regularized problem and establish its exponential convergence. We will illustrate geometric aspects of the adapted optimal transport problem by numerically calculating model ensembles of different martingale models. Based on joint work with Gudmund Pammer.
-''Moderator:'' [https://www.lse.ac.uk/Mathematics/people/Luitgard-Veraart Luitgard Veraart] (London School of Economics and Political Science)
------
-'''April 13, 2023, 1PM-2PM (EST)'''  [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-''Speaker:'' [Mihai Sirbu https://web.ma.utexas.edu/users/sirbu/], Department of Mathematics, University of Texas at Austin
-[[Image:Mihai1.jpg]]
-''Title:'' Backward martingale transport and Fitzpatrick functions in pseudo-Euclidean spaces
-''Abstract:'' : Motivated by a variant of the Kyle equilibrium model with insider from Rochet and Vila (94), we study a one-period multi-dimensional backward martingale problem with quadratic objective given by a bilinear form S. The role of the Kantorovich potentials is played by the Fitzpatrick functions. Based on joint work with Dmitry Kramkov: [ arXiv https://arxiv.org/abs/2209.04664].
-''Moderator:'' [https://www.pstat.ucsb.edu/people/tomoyuki-ichiba Tomoyuki Ichiba] (University of California, Santa Barbara)
 ----
+'''February 12, 2026, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
+''Speaker:'' [https://www.wias-berlin.de/people/bayerc/ Christian Bayer], WIAS Berlin
+[[Image:christian_bayer.jpg|300px|Image: 300 pixels]]
-'''March 9, 2023, 1PM-2PM (EST)'''
-''Speaker:'' [http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal], Department of Statistical Sciences, University of Toronto
+''Title:'' Global and local regression: a signature approach with applications
-[[Image:Sebastian1.jpg]]
+''Abstract:'' The path signature is a powerful tool for solving regression problems on path space, i.e., for computing conditional expectations $\mathbb{E}[Y | X]$ when the random variable $X$ is a stochastic process -- or a time-series. We provide new theoretical convergence guarantees for two different, complementary approaches to regression using signature methods. In the context of global regression, we show that linear functionals of the robust signature are universal in the $L^p$ sense in a wide class of examples. In addition, we present a local regression method based on signature semi-metrics, and show universality as well as rates of convergence. Based on joint works with Davit Gogolashvili, Luca Pelizzari, and John Schoenmakers.
-''Title:'' Risk-Aware Reinforcement Learning for Finance
-''Abstract:'' Reinforcement learning (RL) is a model agnostic approach for “learning” optimal strategies where an agent is faced with maximizing or minimizing an objective functional over a class of strategies that are, in general, stochastic processes. Traditionally, in RL, the objective functionals are expectations of total running rewards/costs – and ignores risks. In this talk, I will provide an overview of some new approaches to pose and solve time consistent versions of risk-aware RL problems with applications to financial modeling.
+''Bio:'' Christian Bayer obtained his PhD at the TU Vienna on numerical methods for stochastic differential equations. He is working as a Senior Researcher at the Weierstrass Institute of Applied Analysis and Stochastics in Berlin. His research interests are in rough volatility, computational finance, stochastic numerics, and stochastic optimal control.
-(based on various joint works with Alvaro Cartea, Anthony Coache, Silvana Pesenti, Yuri Saporito, Rodrigo Targino, Hariam Tatsat, and Ye Sheng Wang)
-''Moderator:'' [https://people.maths.ox.ac.uk/cohens/ Sam Cohen] (University of Oxford)
 ----
+'''December 11, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-'''February 9, 2023, 1PM-2PM (EST)'''
+''Speaker:'' [https://sites.lsa.umich.edu/erhan/ Erhan Bayraktar], University of Michigan
-''Speaker:'' [https://www.maths.ox.ac.uk/people/christoph.reisinger Christoph Reisinger], Mathematical Institute, University of Oxford
+[[Image:erhan_bayraktar.jpg|200px|Image: 200 pixels]]
-[[Image:Christoph1.jpg]]
-''Title:'' Risk management of option books with arbitrage-free neural-SDE market models
+''Title:'' A Mean-Field Approach to DeFi Currency Exchanges
-''Abstract:'' Modelling joint dynamics of liquid vanilla options is crucial for arbitrage-free pricing of illiquid derivatives and managing risks of option trade books. This talk develops a nonparametric model for the European option book, respecting underlying financial constraints while being practically implementable. We derive a state space for prices which are free from static (or model-independent) arbitrage and study the inference problem where a neural SDE model is learnt from discrete time series data of stock and option prices. We validate our approach with numerical experiments using data generated from a Heston stochastic local volatility model and EURO STOXX 50 index data. A subsequent Value-at-Risk (VaR) backtesting analysis shows better coverage performance and less procyclicality than standard filtered historical simulation approaches. Finally, we derive sensitivity-based and minimum-variance-based hedging strategies. When applied to various portfolios of EURO STOXX 50 index options over usual and stressed market periods, our market models can lead to lower hedging errors than Black-Scholes delta-vega hedging, and are less sensitive to the choice of hedging instruments.
-''Moderator:'' [https://sites.google.com/view/cialenco Igor Cialenco] (Illinois Institute of Technology)
-----
-'''January 12, 2023, 1PM-2PM (EST)'''
-''Speaker:'' [https://www.kth.se/profile/sigridkn Sigrid Källblad], KTH Royal Institute of Technology
-[[Image:PhotoSK.jpg]]
-''Title:'' Adapted Wasserstein distance between the laws of SDEs
-''Abstract:'' We consider here an adapted optimal transport problem between the laws of Markovian stochastic differential equations (SDEs) and establish optimality of the so-called synchronous coupling between the given laws. The proof of this result is based on time-discretisation methods and reveals an interesting connection between the synchronous coupling and the celebrated discrete-time Knothe–Rosenblatt rearrangement. We also provide a related result on equality of various topologies when restricted to certain laws of continuous-time stochastic processes. The talk is based on joint work with Julio Backhoff-Veraguas and Ben Robinson.
-''Moderator:'' [https://www.pstat.ucsb.edu/people/tomoyuki-ichiba Tomoyuki Ichiba] (University of California, Santa Barbara)
----
-'''December 8, 2022, 1PM-2PM (EST) Early Career Talks'''
-''Speaker 1:'' [https://www.wu.ac.at/en/statmath/faculty-staff/faculty/gabriela-kovacova/ Gabriela Kovacova], WU Vienna
-[[Image:PhotoGK.jpg]]
-''Title:'' Set-valued Bellman's principle: evidence from the mean-risk problem
-''Abstract:'' Selecting a portfolio of risky assets which maximizes the expected terminal values at the same time as it minimizes portfolio risk is known as the mean-risk problem. The usual approach in the literature is to combine the mean and the risk to obtain a problem with a single objective. In a dynamic setting this scalarization, however, comes at the cost of time inconsistency. We show that these difficulties disappear by considering the problem in its natural form, that is, as a bi-objective optimization problem. As such the mean-risk problem satisfies an appropriate notion of time consistency, closely related to existence of a moving scalarization. Moreover, the mean-risk problem satisfies a Bellman's principle appropriate for a bi-objective optimization problem: a set-valued Bellman's principle. Based on joint work with Birgit Rudloff.
+''Abstract:'' We investigate the behavior of liquidity providers (LPs) by modeling a decentralized cryptocurrency exchange (DEX) based on Uniswap v3. LPs with heterogeneous characteristics choose optimal liquidity positions subject to uncertainty regarding the size of exogenous incoming transactions and the prices of assets in the wider market. They engage in a game among themselves, and the resulting liquidity distribution determines the exchange rate dynamics and potential arbitrage opportunities of the pool. We calibrate the distribution of LP characteristics based on Uniswap data and the equilibrium strategy resulting from this mean-field game produces pool exchange rate dynamics and liquidity evolution consistent with observed pool behavior. We subsequently introduce Maximal Extractable Value bots who perform Just-In-Time liquidity attacks, and develop a Stackelberg game between LPs and bots. This addition results in more accurate simulated pool exchange rate dynamics and stronger predictive power regarding the evolution of the pool liquidity distribution.
-''Speaker 2:'' [http://stat.columbia.edu/department-directory/name/johannes-wiesel/ Johannes Wiesel], Columbia University
+''Bio:''  Erhan Bayraktar, the Susan Smith Chair holder, is a full professor of Mathematics at the University of Michigan, where he has taught since 2004. His research spans stochastic analysis, control, applied probability, mean field games, machine learning, and mathematical finance, with applications in financial risk management. He serves as a corresponding editor for the SIAM Journal on Control and Optimization and sits on the editorial boards of Applied Mathematics and Optimization, Frontiers in Mathematical Finance, Mathematics of Operations Research, and Mathematical Finance. Bayraktar has secured continuous funding from the National Science Foundation, including a prestigious CAREER grant. Since 2015, he has directed the Risk Management and Quantitative Finance Masters program, shaping its development. He has mentored 17 Ph.D. students and over 40 post-docs, many now leading in academia and industry.
-[[Image:PhotoJW.jpg]]
-''Title:'' An optimal transport based toolbox for mathematical finance
-''Abstract:'' In this talk I explain how the theory of optimal transport (OT) can be used to obtain new characterisations of quantities of interest in mathematical finance. I give two examples: I first describe how OT provides a nonparametric measure of association between two random vectors. It has the interesting property that it is equal to zero if and only if the random vectors are independent, and equal to one if and only if there exists a function f, such that Y=f(X). Secondly, I show how convex order of probability measures can be characterised by OT in general dimensions. This characterisation is based on a combination of Brenier’s theorem and OT duality, and gives a computationally tractable way of checking convex order between arbitrary probability measures.
-''Moderator:'' [https://www.pstat.ucsb.edu/people/tomoyuki-ichiba Tomoyuki Ichiba] (University of California, Santa Barbara)
 ----
-'''November 10, 2022, 1PM-2PM (EST)'''
-''Speaker:'' [http://www.maths.lse.ac.uk/Personal/jruf/ Johannes Ruf], London School of Economics and Political Science
+'''November 13, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-[[Image:Ruf1.jpg]]
+''Speaker:'' [https://sites.google.com/site/lucianocampi1/ Luciano Campi], University of Milan
-''Title:'' Estimation of Growth in Fund Models
+[[Image:luciano_campi.jpg|200px|Image: 200 pixels]]
-''Abstract:'' For purposes of long-term investment portfolio choice, we study estimation of growth in fund models. The latter are statistical descriptions of markets where all asset returns are spanned by the returns of a lower-dimensional collection of funds, modulo orthogonal noise; equivalently, they may be characterised as models where the global growth-optimal portfolio only involves investment in the aforementioned funds. The loss of growth due to estimation error in fund models under local frequentist estimation is determined entirely by the number of funds. Furthermore, under a general filtering framework for Bayesian estimation, the loss of growth increases as the investment universe does. A shrinkage method that targets maximal growth with the least amount of deviation is proposed. Empirical evidence suggests that shrinkage gives a stable estimate that more closely follows growth potential than an unrestricted Bayesian estimate.
-This is joint work with Kostas Kardaras and Hyeng Keun Koo.
-SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4180821
+''Title:'' Optimal coarse correlated equilibria in mean field games
-''Moderator:'' [https://www.maths.ox.ac.uk/people/samuel.cohen Samuel Cohen] (University of Oxford)
+''Abstract:'' We will consider coarse correlated equilibria (CCE) in continuous time mean field games. CCEs are generalizations of Nash equilibria, when a moderator (correlation device) recommend strategies to the players that are not convenient to unilaterally reject. We will first address existence and approximations results when the number of players goes to infinity. Second, we will provide a linear programming approach through the notion of relaxed strategies in the same spirit as the works by Kurtz and Stockbridge, which have been recently extended to mean field games in several papers by Bouveret, Dumitrescu, Leutscher and Tankov. Within such a linear programming setting and under some regularity assumptions, we will show existence of an optimal CCE with respect to a fixed criterion for the moderator. Finally, we will propose an equivalent Lagrangian formulation and a primal-dual algorithm to compute an optimal CCE numerically. This talk is based on joint papers with F. Cannerozzi, F. Cartellier, M. Fischer and I. Tzouanas.
-----
-'''October 13, 2022, 1PM-2PM (EST)'''
-''Speaker:'' [https://www.finance.uni-freiburg.de/mitarbeiter/prof.-dr.-e.-luetkebohmert-holtz Eva Lütkebohmert-Holtz], University of Freiburg
-[[Image:Eva1.jpg]]
-''Title:'' Robust deep hedging
-''Abstract:'' We study pricing and hedging under parameter uncertainty for a class of Markov processes which we call generalized affine processes and which includes the Black–Scholes model as well as the constant elasticity of variance (CEV) model as special cases. Based on a general dynamic programming principle, we are able to link the associated nonlinear expectation to a variational form of the Kolmogorov equation which opens the door for fast numerical pricing in the robust framework. The main novelty of the paper is that we propose a deep hedging approach which efficiently solves the hedging problem under parameter uncertainty. We numerically evaluate this method on simulated and real data and show that the robust deep hedging outperforms existing hedging approaches in highly volatile periods. (Joint work with Thorsten Schmidt and Julian Sester.)
+''Bio:''  Luciano Campi is currently Full Professor of Probability and Mathematical Statistics at the Department of Mathematics "Federigo Enriques", University of Milan. His recent research focuses on stochastic control, stochastic differential games, mean field games, and their applications to energy markets. Before joining the University of Milan, he held academic positions at the London School of Economics, University Paris 13, and University Paris Dauphine. He earned his PhD in Mathematics from the University of Paris 6 and in Computational Mathematics from the University of Padua. Luciano Campi is an Associate Editor for the IMA Journal of Applied Mathematics and Decisions in Economics and Finance.
-''Moderator:'' [https://sites.google.com/view/cialenco Igor Cialenco] (Illinois Institute of Technology)
 ----
-'''September 8, 2022, 1PM-2PM (EST)'''
-''Speaker:'' Agostino Capponi, Columbia University
-[[Image:Agostino1.jpg]]
+'''October 9, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]:
-''Title:'' The Adoption of Blockchain-Based Decentralized Exchanges
+''Speaker:'' [https://www.math.kit.edu/stoch/~baeuerle/en Nicole Bäuerle], Karlsruhe Institute of Technology
-''Abstract:'' Decentralized finance (DeFi) is a competitive marketplace of decentralized financial applications that operate through smart contracts, which currently manages over $57 billion USD. We investigate the market microstructure of Automated Market Makers (AMMs), the most prominent type of pricing functions implemented by DeFi exchanges, and highlight the fundamental differences with centralized limit order books. Through a dynamic game theoretical model we demonstrate that, in equilibrium, arbitrageurs extract gains from liquidity providers for a broad class of convex, twice differentiable, and positively homogeneous pricing functions. We show that pricing curves with higher convexity result in higher price impact, which reduces arbitrage and liquidity freezes, but also decreases trading volume. We provide statistical support for our model implications using transaction-level data of Uniswap and SushiSwap AMMs (joint work with R. Jia).
+[[Image:nicole_bauerle.jpg|200px|Image: 200 pixels]]
-''Moderator:'' [https://www.lse.ac.uk/Mathematics/people/Luitgard-Veraart Luitgard Veraart] (London School of Economics and Political Science)
+''Title:'' Competitive portfolio optimization
-----
-----
+''Abstract:'' Within a common arbitrage-free semimartingale financial market we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their relative wealth. The utility function can be rather general here. Exploiting the linearity of the stochastic integral and making use of the classical pricing theory we are able to express all Nash equilibrium investment strategies in terms of the optimal strategies for the classical one agent expected utility problems.  We give applications to specific financial markets and compare our results with those given in the literature. A more specific model with price impacts is also discussed. Moreover, we consider the problem of determining all Nash equilibrium investment strategies for n agents who try to maximize the expected utility of their wealth under the constraint that with certain probability the own wealth exceeds a linear combination of the others. We compare the investment strategy to the optimal one without competition. (Joint work with T. Göll)
-'''June 9, 2022, 1PM-2PM (EST)'''
-''Speaker:'' Harvey Stein, Two Sigma Investments [https://youtu.be/ylyN94G_CJc Recorded video]
+''Bio:''  Nicole Bäuerle received the Ph.D. degree in mathematics from Ulm University, Ulm, Germany, in 1996. Since 2005, she has been a Professor of probability with the Karlsruhe Institute of Technology, Karlsruhe, Germany. From 2002 to 2005, she was a Professor of insurance mathematics with the University of Hannover, Hannover, Germany. She has authored or coauthored more than 80 papers and a book jointly with Ulrich Rieder on Markov Decision Processes with Applications to Finance. Her research interests include stochastic processes and control with applications to finance, insurance, and stochastic networks. Dr. Bäuerle has served on the editorial board of many journals and is currently Deputy Editor in Chief of the Journal of Applied Probability  and an Associate Editor of  Statistics and Risk Modeling.
-[[Image:Harvey1.jpg]]
-''Title:'' Model Invariants and Functional Regularization
-''Abstract:'' When modeling data, we would like to know that our models are extracting facts about the data itself, and not about something arbitrary, like the order of the factors used in the modeling. Formally speaking, this means we want the model to be invariant with respect to certain transformations.
-Here we look at different models and the nature of their invariants. We find that regression, MLE and Bayesian estimation all are invariant with respect to linear transformations, whereas regularized regressions have a far more limited set of invariants. As a result, regularized regressions produce results that are less about the data itself and more about how it is parameterized.
-To correct this, we propose an alternative expression of regularization which we call functional regularization. Ridge regression and lasso are special cases of functional regularization, as is Bayesian estimation. But functional regularization gives a framework under which the models become invariant with respect to linear transformations. It is also more flexible, and easier to understand, thus yielding a number of advantages over ridge regression and lasso.
-''Moderator:'' [https://people.maths.ox.ac.uk/cohens/ Sam Cohen] (University of Oxford)
------
-'''May 12, 2022, 1PM-2PM (EST)'''
-''Speaker:'' [http://www.carole.bernard.free.fr/ Carole Bernard], Grenoble Ecole de Management / Faculty of Economics, Vrije Universiteit Brussel  [https://youtu.be/m2AS_eo50V8 Recorded video]
-[[Image:Carole1.jpg]]
-''Title:'' Multivariate Portfolio Choice via Quantiles
-''Abstract:'' Agents who optimize a univariate law-invariant increasing objective (e.g., an expected utility, a Cumulative Prospect Theory objective function) obtain optimal payoffs that are decreasing in the pricing kernel. Such observation plays a key role in the theory developed by He and Zhou (2011) on ``Portfolio Choice via Quantiles’’. In this paper, we first show how it is possible to extend this quantile approach to solve a multivariate optimal portfolio  for some multivariate preferences (e.g., multivariate expected utility, inf-convolution of tail risk measures). Second, we develop a new approach allowing us to solve the multivariate optimal portfolio problem numerically.
-This is based on joint papers with Luca Aquino De Gennaro, Andrea Perchiazzo and Steven Vanduffel.
-''Moderator:'' [https://www.lse.ac.uk/Mathematics/people/Luitgard-Veraart Luitgard Veraart] (London School of Economics)
 ----
-'''April 14, 2022, 1PM-2PM (EST)'''
-''Speaker:'' [http://sircar.princeton.edu/ Ronnie Sircar], ORFE, Princeton University [https://youtu.be/qOeKzLBM5HM Recorded video]
+'''September 11, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+and this edition will feature two speakers:
-[[Image:Ronnie1.jpg]]
+''Speaker:'' [https://chiaraamorino.github.io Chiara Amorino], Universitat Pompeu Fabra
-''Title:'' ORFEUS
+[[Image:chiara_amorino.jpg|300px|Image: 300 pixels]]
-''Abstract:'' We describe part of a project, sponsored by ARPA-E, to quantify, allocate and account for the risk introduced to electricity grid due to the unpredictable reliability of production from renewables. Incorporating this stochasticity into grid risk management is viewed by the industry (which has remained almost entirely tethered to a deterministic viewpoint an in particular to weather forecasts) as increasingly crucial, as we aim for greater renewables penetration to reduce dependence on carbon-emitting fuels. Our methodology involves feeding Monte Carlo simulations of solar, wind and load into a grid optimization software that emulates the performance and costs of the Texas electricity grid. This outputs a distribution of running costs, from which we can numerically extract a measure of system (grid) risk.  The more challenging part is to allocate this risk back (top down) to the individual renewable assets to assign them a reliability cost. This adapts exisiting approaches for the risk allocation problem related to Shapley values, but is computationally intense. We show results, project to potential future grids, and propose a way to incorporate the reliability costs back into the day ahead bid curve and thereby re-optimize unit commitment and economic dispatch of assets taking into (some) account the stochasticity.
-This work is in collaboration with a team listed here: [https://orfeus.princeton.edu/ orfeus.princeton.edu]
+''Title:'' Minimax rate for multivariate data under componentwise local differential privacy constraints
-and this part in particular with Michal Grzadkowski and Arvind Shrivats (both Princeton).
-''Moderator:'' [http://ichiba.faculty.pstat.ucsb.edu/ Tomoyuki Ichiba] (University of California Santa Barbara)
-----
-'''March 10, 2022, 1PM-2PM (EST)''' '''Early Career Talks''' [https://www.youtube.com/watch?v=avuHe-gQdL8 Recorded video]
-''Speaker 1:'' [http://mesiasalfeus.weebly.com/ Mesias Alfeus], Stellenbosch University
-[[Image:Mesias1.jpg]]
-''Title:''  Quantitative Finance: Toward A General Framework for Modelling Roll-Over Risk
-''Abstract:'' Quantitative Finance underwent significant development over the past decade. For example, modelling of the term structure of interest rates after the GFC poses a unique challenge. The persistent phenomena of market basis spreads are an indication that markets are pricing various risks which are not captured in classical models. We pioneer a roll-over risk modelling framework to provide empirical evidence to the observed basis spreads, i.e., a spread between LIBOR of different tenors and LIBOR-OIS spread. This roll-over risk consists of two components, a credit risk component due to the possibility of being downgraded and thus facing a higher credit spread when attempting to roll over short–term borrowing, and a component reflecting the (systemic) possibility of being unable to roll over short–term borrowing at the reference rate (e.g., LIBOR) due to an absence of liquidity in the market. The modelling framework is of “reduced form” in the sense that the source of credit risk is not modelled (nor is the source of liquidity risk).  We show how such model can be calibrated to market data, and used for relative pricing of interest rate derivatives, including bespoke tenor frequencies not liquidly traded in the market.
+''Abstract:'' Our research analyses the trade-off between maintaining privacy and preserving statistical accuracy when dealing with multivariate data subject to componentwise local differential privacy (CLDP). Under CLDP, each component of the private data is released through a separate privacy channel. This allows for varying levels of privacy protection for different components or for the privatization of each component by different entities, each with their own distinct privacy policies. It also covers practical situations where it is impossible to privatize all components of the raw data jointly.
+We develop general techniques for establishing minimax bounds that quantify the statistical cost of privacy as a function of the privacy levels \alpha_1,…,\alpha_d of the d components. The versatility and efficiency of these techniques are demonstrated through various statistical applications. Specifically, we examine nonparametric density estimation and joint moments estimation under CLDP, providing upper and lower bounds that match up to constant factors, along with an associated data-driven adaptive procedure. We also conduct a detailed analysis of the effective privacy level, exploring how information about a private characteristic of an individual may be inferred from the publicly visible characteristics of the same individual.
-''Speaker 2:'' [https://yufei-zhang.github.io/ Yufei Zhang], Department of Statistics, London School of Economics
-[[Image:Yufei1.jpg]]
+''Bio:''  Chiara Amorino is currently an Assistant Professor in the Statistics Group at Universitat Pompeu Fabra in Barcelona, a position she has held since April 2024. In 2025, she was awarded the prestigious Ramón y Cajal Fellowship in Mathematics, a five-year individual grant from the Spanish Ministry of Economy, Industry and Competitiveness. Since 2024, she has also served as Chair of the Bernoulli Young Researchers Committee for Europe.
-''Title:''  Exploration-exploitation trade-off for continuous-time episodic reinforcement learning with linear-convex models
+Before joining UPF, she was a postdoctoral researcher at the University of Luxembourg in the group of Prof. Mark Podolskij. She earned her PhD in Mathematics from Université Paris-Saclay (LaMME) under the supervision of Prof. Arnaud Gloter, defending her thesis in July 2020.
-''Abstract:'' We develop a probabilistic framework for analysing model-based reinforcement learning in the episodic setting. We then apply it to study finite-time horizon stochastic control problems with linear dynamics but unknown coefficients and convex, but possibly irregular, objective function. Using probabilistic representations, we study regularity of the associated cost functions and establish precise estimates for the performance gap between applying optimal feedback control derived from estimated and true model parameters. Next, we propose a phase-based learning algorithm for which we show how to optimise exploration-exploitation trade-off. Our algorithm achieves sublinear (or even logarithmic) regrets in high probability and expectation, which matches the best possible results from the literature.
+Her research interests include statistical inference for stochastic differential equations, interacting particle systems, Hawkes processes, fractional processes, and local differential privacy.
-This talk is based on several projects with Xin Guo (UC Berkeley), Anran Hu (UC Berkeley), Lukasz Szpruch (U of Edinburgh, Alan Turing Institute) and Tanut Treetanthiploet (Alan Turing Institute).
-''Moderator:'' [https://people.maths.ox.ac.uk/cohens/ Sam Cohen], University of Oxford
+''Speaker:'' [https://www.faycaldrissi.com Fayçal Drissi], University of Oxford
+[[Image:faycal_drissi.jpg|200px|Image: 200 pixels]]
-----
+''Title:'' Equilibrium Liquidity Provision in Concentrated Liquidity Automated Market Makers
-'''February 10, 2022, 1PM-2PM (EST)''' [https://www.youtube.com/watch?v=dd1LBaqkOcg Recorded video]
+''Abstract:'' Automated market makers (AMMs) with concentrated liquidity (CL) are the most widely used decentralised exchanges, with daily trading volumes around $4 billion. In CL markets, liquidity providers (LPs) strategically choose price ranges to balance fee revenues against adverse selection losses. We develop a model of competition among LPs and characterise the equilibrium distribution of liquidity across ranges. The analysis shows how equilibrium outcomes depend on the number of competing LPs, the ratio of informed to uninformed trading flow, and wealth heterogeneity among liquidity providers. Finally, we examine the role of “noise” liquidity provision and show how it affects equilibrium allocations and execution costs.
-''Speaker:'' [https://www.lpsm.paris/pageperso/crepey/ Stephane Crepey],  Université de Paris / LPSM
-[[Image:Stephane1.jpg]]
-''Title:'' Darwinian model risk and reverse stress testing
-''Abstract:'' We consider the risk of adverse model selection, whereby a trader selects a model leading to superiorly competitive prices, the ensuing valuation loss for the bank being more than offset by gains on the hedging side of the position. But this is only the case until a stress event reveals the erroneous nature of the model used, forcing the bank to liquidate the position and its hedge at the cost of heavy losses. This "Darwinian model risk" is directional (related to a long-term moment of order one of the PnL) and likely to stay unnoticed from traditional risk systems, which are focused on shorter-term moments of order two and beyond. One possible approach to detect it consists of long-term, large-scale simulations, revealing the consequences of using various models in extreme scenarios.
-Based on:
-) Claudio Albanese, Stéphane Crépey, and Stefano Iabichino (2021). A Darwinian theory of model risk. Risk Magazine July pages 72-77.
-) Claudio Albanese, Stéphane Crépey, and Stefano Iabichino (2021). Reverse Stress Testing. ssrn.3544866
+''Bio:'' Fayçal Drissi is currently a postdoctoral researcher at the Oxford-Man Institute, University of Oxford. He obtained a Ph.D. in Mathematics from Université Paris 1 Panthéon-Sorbonne in 2023. His thesis focused on the microstructure of traditional electronic markets and decentralised exchanges that use Automated Market Makers (AMMs). Prior to his doctoral studies, he spent five years in the hedge fund industry doing research and development related to systematic trading and global macro.
 ----
-'''January 13, 2022, 1PM-2PM (EST)''' [https://www.youtube.com/watch?v=I13zdNPNXCo Recorded video]
-''Speaker:'' [http://www.columbia.edu/~dl3133/ Daniel Lacker], IEOR, Columbia University
-[[Image:Daniel1.jpg]]
+'''June 12, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+and this edition will feature two speakers:
-''Title:'' Stochastic games on large graphs in mean field and sparse regimes
+''Speaker:'' [https://www.valentin.tissot-daguette.com Valentin Tissot-Daguette], Bloomberg
-''Abstract:'' The now-widespread theory of mean field games provides a systematic framework for modeling stochastic dynamic games with many interacting players. It is fundamentally limited, however, to models in which players view each other as exchangeable. This talk discusses some recent efforts to push past this limitation by incorporating heterogeneous interactions, such as those governed by networks. I will focus on a recent case study, joint with Agathe Soret, on linear-quadratic stochastic differential games in which each player is labeled by a vertex in a graph and interacts symmetrically with its nearest neighbors. Large-scale asymptotics are developed for various graph sequences, with an emphasis on the difference between sparse and dense regimes.
+[[Image:valentin_tissot.jpg|200px|Image: 200 pixels]]
-''Moderator:'' [http://ichiba.faculty.pstat.ucsb.edu/ Tomoyuki Ichiba] (University of California Santa Barbara)
+''Title:'' Pathwise Superhedging of Asian Claims
-----
+''Abstract:'' The talk unveils pathwise superhedging strategies for convex Asian claims using a dynamic hedge in the underlying and a static position in vanilla options. For an Asian call, where the seller is long the matching vanilla contract, the dynamic hedge may involve the time spent by the asset - or its running average - above the strike. The validity of average-based strategies stems from a mysterious identity relating the Asian call payoff to a strip of binary options across maturities.
+The strategies are then tested on synthetic data, where we compare the variance of their P&Ls and hedging turnover. We finally connect these findings with Martingale Optimal Transport and derive robust price bounds for forward start (convex) Asian claims.
+Special thanks to Bruno Dupire, Hélyette Geman, Julien Guyon, Bryan Liang, Marcel Nutz, and Nizar Touzi.
-'''Thursday, December 2, 2021,  1PM-2PM''' '''Early Career Talks''' [https://youtu.be/62BFBdVtM8w Recorded Video]
-''Speaker 1:'' [https://renyuanxu.github.io/ Renyuan Xu], Epstein Department of Industrial and Systems Engineering, University of Southern California
+''Bio:''  Valentin Tissot-Daguette is a quantitative researcher at Bloomberg. He recently obtained his PhD degree from Princeton University, under the co-supervision of Prof. Mete Soner and Bruno Dupire. Previously, Valentin studied at EPFL and ETH Zurich where he completed a Bachelor's degree in Mathematics and a Master's degree in Financial Engineering. His research interests include exotic derivatives, free boundary problems, and stochastic control.
-[[Image:Renyuan1.jpg]]
-''Title:''  Learning in Linear-quadratic Framework: From Single-agent to Multi-agent, and to Mean-field
+''Speaker:'' [https://daspurba.github.io Purba Das], King's College London
-''Abstract:'' Linear-quadratic (LQ) framework is widely studied in the literature of stochastic control, game theory, and mean-field analysis due to its simple structure, tractable solution, and local approximation power to nonlinear control problems. In this talk, we discuss several theoretical results of the policy gradient (PG) method, a popular reinforcement learning algorithm, for several LQ problems where agents are assumed to have limited information about the stochastic system. In the single-agent setting, we explain how the PG method is guaranteed to learn the global optimal policy.  In the multi-agent setting, we show that (a modified) PG method could guide agents to find the Nash equilibrium solution provided there is a certain level of noise in the system. The noise can either come from the underlying dynamics or carefully designed explorations from the agents. Finally when the number of agents goes to infinity, we propose an exploration scheme with entropy regularization that could help each individual agent to explore the unknown system as well as the behavior of other agents. The proposed scheme is shown to be able to speed up and stabilize the learning procedure.
+[[Image:purba_das.jpg|200px|Image: 200 pixels]]
-This talk is based on several projects with Xin Guo (UC Berkeley), Ben Hambly (U of Oxford), Huining Yang (U of Oxford),  and Thaleia Zariphopoulou (UT Austin).
+''Title:'' Invariance of Stochastic integral with respect to the choice of partitions
+''Abstract:'' We study the concept of quadratic variation of a continuous path along a sequence of partitions and its dependence with respect to the choice of the partition sequence. We introduce the concept of quadratic roughness of a path along a partition sequence and show that for Hölder-continuous paths satisfying this roughness condition, the quadratic variation along balanced partitions is invariant with respect to the choice of the partition sequence. We further present several constructions of paths and processes with finite quadratic variation along a refining sequence of partitions and, in particular, study the dependence of quadratic variation with respect to the sequence of partitions for these constructions.
-''Speaker 2:'' [https://p-casgrain.github.io/ Philippe Casgrain], ETH Zürich and Princeton University
-[[Image:Philippe1.jpg]]
+''Bio:'' Purba Das is a Lecturer (Assistant Professor) in Financial Mathematics in the Department of Mathematics at King’s College London. Before KCL, she was a Byrne Research Assistant Professor of Mathematics at the University of Michigan for one year. She completed her DPhil in Mathematics at the University of Oxford under the supervision of Professor Rama Cont.
-''Title:''  Anytime-valid sequential testing for elicitable functionals via supermartingales
-''Abstract:'' We consider the problem of testing statistical hypotheses and building confidence sequences for elicitable and identifiable functionals, a broad class of statistical quantities which are of particular interest in the field of quantitative risk management. Assuming a framework in which data is collected sequentially, where a user may choose to accept or reject a hypothesis at any point in time, we provide powerful distribution-free and anytime-valid testing methods which rely on controlled supermartingales. Leveraging tools from online convex optimization, we show that tests can be optimized to improve their statistical power, with asymptotic guarantees for rejecting false hypotheses. By "inverting the test", these methods are extended to the task of confidence sequence building. Lastly, we implement these techniques on a range of examples to demonstrate their effectiveness.
-''Moderator:'' [http://sircar.princeton.edu/ Ronnie Sircar], Princeton University
 ----
+'''May 8, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+''Speaker:'' [https://sites.google.com/site/juliobackhoff/home Julio Backhoff], University of Vienna
-'''Thursday, November 4, 2021,  1PM-2PM'''
+[[Image:julio_backhoff.jpg|200px|Image: 200 pixels]]
-''Speaker:'' [https://people.math.ethz.ch/~beacciaio/ Beatrice Acciaio], Department of Mathematics, ETH Zurich
-[[Image:Beatrice1.jpg]]
+''Title:'' Of ‘most exciting’ games and the specific relative entropy between martingales
-''Title:'' Adapted Wasserstein distances in mathematical finance
+''Abstract:'' The laws of two continuous martingales will typically be singular to each other and hence have infinite relative entropy. But this does not need to happen in discrete time. This suggests defining a new object, the specific relative entropy, as a scaled limit of the relative entropy between the discretized laws of the martingales. This definition goes back to Nina Gantert’s PhD thesis, and in recent time Hans Foellmer has rekindled the study of this object. Independently, this object has made sporadic appearances in finance over the years, for instance in works by Avellaneda et al. and more recently Dolinsky and Cohen.
-''Abstract:'' The causality constraint in optimal transport allows to capture the temporal structure of the transported objects, which is crucial when transporting measures on path spaces. In its symmetric formulation, this leads to the definition of adapted Wasserstein distances. I will present several applications of those concepts to illustrate their suitability for multiple problems in mathematical finance and stochastic analysis.
+In this talk we will first discuss the existence of a closed-form formula for the specific relative entropy, depending on the quadratic variation of the involved martingales. Next we will describe an application of this object to prediction markets. Concretely, David Aldous asked in an open question to determine the ‘most exciting’ game, i.e. the prediction market with the highest entropy. With M. Beiglböck we give an answer to this question by solving a stochastic control problem whose cost criterion is the specific relative entropy. Finally we will discuss an application to the ‘most exciting’ game with multiple outcomes, based on joint work with Wang and Zhang, highlighting a novel connection to the field of Monge-Ampere equations.
-----
+''Bio:''  Julio Backhoff obtained his PhD in Mathematics from Humboldt-Universität zu Berlin, in 2015, under the supervision of Prof. Ulrich Horst. From 2015 to 2019, he held various postdoc and university assistant positions in the group of Prof. Mathias Beiglböck at the University of Vienna and the Vienna University of Technology. In 2020, he became an assistant professor at the University of Twente. Since 2021, he has been an assistant professor at the University of Vienna (Mathematics Faculty, Mathematical Finance and Probability group). His research lies at the intersection of Mathematical Finance, Stochastic control / analysis, and optimal transport theory. For the most part, he is interested in problems coming from model-free finance and model calibration, with an emphasis on adapted and martingale transport.
-'''Thursday, October 7, 2021,  1PM-2PM'''
-''Speaker:'' [https://sites.google.com/site/ibrahimekren/home Ibrahim Ekren], Department of Mathematics, Florida State University
-[[Image:Ibrahim1.jpg]]
-''Title:''  Optimal Transport and Risk Aversion in Kyle's Model of Informed Trading [https://youtu.be/sAAbIAx39cg Recorded video]
-''Abstract:'' We establish connections between optimal transport theory and the dynamic version of the Kyle model, including new characterizations of informed trading profits via conjugate duality and Monge-Kantorovich duality. We use these connections to extend the model to multiple assets, general distributions, and risk-averse market makers. With risk-averse market makers, liquidity is lower, assets exhibit short-term reversals, and risk premia depend on market maker inventories, which are mean reverting. We illustrate the model by showing that implied volatilities predict stock returns when there is informed trading in stocks and options and market makers are risk averse. Based on joint work with Kerry Back, Francois Cocquemas and Abraham Lioui.
 ----
+'''April 10, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-'''Thursday, September 16, 2021,  1PM-2PM'''
+''Speaker:'' [https://sites.google.com/view/sarasvaluto-ferro Sara Svaluto-Ferro], University of Verona
-''Speaker:'' [http://math.bu.edu/people/kspiliop/ Konstantinos Spiliopoulos], Department of Mathematics & Statistics, Boston University
+[[Image:sara.jpg]]
-[[Image:Kostas1.jpg]]
-''Title:''  Normalization effects on neural networks: generalization properties and high dimensions [https://youtu.be/z8VGLpoYHxY Recorded video]
-''Abstract:'' We consider neural networks and characterize their performance when trained with stochastic gradient descent as the number of hidden units N and gradient descent steps grow to infinity. In particular, we investigate the effect of normalizations (almost equivalently: initialization schemes) on the network's statistical output.  We develop an asymptotic expansion for the neural network's statistical output, demonstrating that to leading order in N there is no bias-variance trade off and that to leading order in N  the variance of the neural network's statistical output decays as the implied normalization by the scaling parameter approaches a certain scaling known as the mean field normalization. Numerical studies on the well known MNIST and CIFAR10 datasets show that generalization properties monotonically improve as the neural network's normalization gets closer to the mean field normalization. Motivated by this theory, we present a mesh free, which is key since meshes become infeasible in higher dimensions, deep learning algorithm for solving high dimensional partial differential equations (PDEs). The algorithm is being tested in a number of problems including option pricing in up to 500 dimensions. We call the algorithm a "Deep Galerkin Method (DGM)" since it is similar in spirit to Galerkin methods, with the solution approximated by a neural network instead of a linear combination of basis functions.
-''Moderator:'' [http://www.columbia.edu/~ac3827/index.html Agostino Capponi], Department of Industrial Engineering and Operations Research, Columbia University
-----
+''Title:'' Signature-based models: theory, calibration, and expansions
+''Abstract:'' Signature methods provide a non-parametric approach to extracting features from trajectories, offering versatile applications in finance. The structure of signature components enables the use of advanced mathematical tools and the construction of highly general models capable of capturing diverse behaviors.
-'''Thursday, April 29, 2021,  1PM-2PM'''
+In this talk, we introduce the concept of the signature and its key properties, illustrating its potential through two financial applications.
-''Speaker:'' [https://sites.google.com/site/phamxuanhuyen/home Huyen Pham], Professor, LPSM/Université de Paris
+The first application focuses on a stochastic volatility model where volatility dynamics are described by linear functions of the (time-extended) signature of a primary process. When this process is of polynomial type, its truncated signature retains this structure, allowing for closed-form expressions of the squared VIX. By incorporating the Brownian motion driving the stock price, both the log-price and the squared VIX can be expressed linearly in terms of the signature of the augmented process, achieving highly accurate calibration results for SPX and VIX options.
-[[Image:Huyen1.jpg]]
+The second application examines the local-in-time expansion of a continuous-time process and its conditional moments, including the characteristic function. By leveraging the time-extended Itô signature—composed of iterated integrals of deterministic and stochastic signals (time, multiple correlated Brownian motions, and compound Poisson processes)—we derive automated expansions to any order with explicit coefficients. This provides stochastic representations suitable for asymptotic analysis in the short-time limit.
-''Title:''  DeepSet and their derivative networks for solving symmetric problems [https://youtu.be/_Owtz40rzlI Recorded video]
-''Abstract:'' Machine learning methods represent a breakthrough for solving nonlinear partial differential equations (PDEs) and control problems in very high dimension, and have been the subject of intense  research over the last five years.
+''Bio:'' Sara Svaluto-Ferro is an Associate Professor at the University of Verona, Department of Economics, specializing in mathematical methods for economics and finance. Previously, she was an Assistant Professor (RTDB) at the same department and a postdoctoral researcher at the University of Vienna in the research group of Prof. C. Cuchiero. She earned her PhD in Mathematics from ETH Zurich under the supervision of Prof. M. Larsson.
-In this talk, we consider a widespread class of problems that are invariant to permutations of their inputs (state variables or model parameters). This occurs for example in multi- asset models for option pricing with exchangeable payoff, or for optimal trading portfolio with respect to the market price of covariance risk.  Our main application comes actually from mean-field control problems and the corresponding PDEs in the Wasserstein space of probability measures. Their particle approximations, for which we provide a rate of convergence, lead to  symmetric PDEs that are solved by deep learning algorithms based on certain types  of neural networks, named DeepSet. We illustrate the performance and accuracy  of the DeepSet networks compared to classical feedforward ones, and provide several numerical results of our algorithm for the examples of a mean-field systemic risk, and mean-variance problem. Finally, we show how the combination of DeepSet and DeepOnet, a network architecture recently proposed for learning operators, provides an efficient approximation for a family of optimal trading strategies in terms of market price of covariance risk coefficients.
+Her research focuses on stochastic processes, including affine and polynomial models, stochastic representations of PDEs, and optimization in infinite-dimensional spaces, with applications in financial mathematics, systemic risk, and rough volatility modeling. In the last years she dedicated particular attention to applications of signature processes in finance.
-''Moderator:'' [http://www.columbia.edu/~ac3827/index.html Agostino Capponi], Department of Industrial Engineering and Operations Research, Columbia University
 ----
-'''Thursday, April 1, 2021,  1PM-2PM, Early Career Talks'''  [https://youtu.be/xL9ZGGaF7Xk Recorded video]
+'''March 20, 2025, 1PM-2.30PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-''Speaker 1:'' [https://sites.google.com/view/xiaofeis/home Xiaofei Shi], Department of Statistics, Columbia University
+and this edition will feature two speakers:
-[[Image:Xiaofei1.jpg]]
+''Speaker:'' [https://www.maths.ox.ac.uk/people/terry.lyons  Terry Lyons], University of Oxford
-''Title:'' Equilibrium Asset Pricing with Liquidity Risk
+[[Image:lyons(1).jpg]]
-''Abstract:'' In a risk-sharing economy we study how the price dynamics of an asset depends on its “liquidity”. An equilibrium is achieved through a system of coupled forward-backward SDEs, whose solution turns out to be amenable to an asymptotic analysis for the practically relevant regime of large liquidity. We also discuss how to leverage deep-learning techniques to obtain numerical solutions, and compare them with our asymptotic approximations.
-''Speaker 2:'' [https://mlauriere.github.io/ Mathieu Laurière], ORFE, Princeton University
+''Title:'' The Mathematics of Complex Streamed Data
-[[Image:Mathieu1.jpg]]
+''Abstract:'' Multimodal streamed data is essentially different to unimodal streamed data. Consider this:
-''Title:'' Deep learning for Mean Field Games, and applications to finance
+‘A commuter arrives at a bus stop before the bus’ – they catch it;
-''Abstract:'' Mean field game (MFG) theory is a framework to study equilibria in populations consisting of a very large number of strategic agents. This paradigm is motivated by a broad range of models, from economics to crowd motion, finance, and risk management. Developing scalable computational methods is a key step towards concrete applications. In this talk, we propose several numerical methods for MFGs based on machine learning tools such as function approximation via neural networks and stochastic optimization. We will present numerical results for models motivated by applications to finance. This is joint work with René Carmona.
+‘The bus arrives first’ – they miss it.
+These are the same two events, but the order changes everything. Yet most models treat these as identical: ‘A bus and a person arrived’ They ignore timing and relationships.
-''Moderator:''  [https://www.wu.ac.at/en/statmath/faculty-staff/faculty/birgit-rudloff Birgit Rudloff], Vienna University of Economics and Business
+This simplification isn’t harmless. A timed series gives no information about order within sampling intervals. As a result, the sampling rate has to come from the bottom up if it is to preserve this order information. Rough path theory makes a radical change and describes the stream over an interval using a group element. According to the choice of group it is possible to capture order information and to allow a top down description of the data stream without using essential information about the order of events.
-----
+This approach to describing streamed data is important to data science because it reduces the dimension needed for descriptive feature sets and so reduces the size of the data set needed to train. There are numerous prize winning illustrations of the methodology in use and the impact can be measured in the hundreds of millions of US dollars.
+''Bio:'' Terry Lyons FLSW FRSE FRS is the Wallis Professor Emeritus and Professor of Mathematics at the University of Oxford, a fellow of St Anne's College, Oxford and a Faculty Fellow at The Alan Turing Institute. He is currently PI of the DataSıg and of the complementary research programme CIMDA-Oxford. He was the President of the London Mathematical Society (2013-2015), the Director (2011-2015) of the Oxford Man Institute of Quantitative Finance and the Director of the Wales Institute of Mathematical and Computational Sciences (2008-2011). He came to Oxford in 2000 having previously been Professor of Mathematics at Imperial College London (1993-2000), and before that he held the Colin Maclaurin Chair at Edinburgh (1985-93).
-'''Thursday, March 4, 2021, 1PM-2PM'''
+Professor Lyons’s long-term research interests are all focused on Rough Paths, Stochastic Analysis, and applications – particularly to Finance and more generally to the summarising of large complex data. More specifically, his interests are in developing mathematical tools that can be used to effectively model and describe high dimensional systems that exhibit randomness as well as the complex multimodal data streams that arise in human activity. Professor Lyons is involved in a wide range of problems from pure mathematical ones to questions of efficient numerical calculation. He is, in particular, recognised for developing what is now known as the theory of rough paths.
-''Speaker:'' '''[http://www.math.columbia.edu/~mnutz/ Marcel Nutz]''', Professor, Columbia University
-[[Image:Marcel1.jpg]]
+''Speaker:'' [https://luhao-zhang.github.io/  Luhao Zhang], Johns Hopkins University
-''Title:''  Entropic Optimal Transport [https://youtu.be/PYT4KwrX-Us Recorded video]
+[[Image:zhang.jpg]]
-''Abstract:'' Applied optimal transport is flourishing after computational advances have enabled its use in real-world problems with large data sets. Entropic regularization is a key method to approximate optimal transport in high dimensions while retaining feasible computational complexity. In this talk we discuss the convergence of entropic optimal transport to the unregularized counterpart as the regularization parameter vanishes, with a focus on the local behavior. Based on joint works with Espen Bernton (Columbia), Promit Ghosal (MIT), Johannes Wiesel (Columbia).
-''Moderator:'' [https://soner.princeton.edu/ Mete Soner],  Princeton University
-----
+''Title:'' A Class of Interpretable and Decomposable Multi-period Convex Risk Measures
-'''Thursday, February 4, 2021, 1PM-2PM'''
+''Abstract:'' Multi-period risk measures evaluate the risk of a stochastic process by assigning it a scalar value. A desirable property of these measures is dynamic decomposition, which allows the risk evaluation to be expressed as a dynamic program. However, many widely used risk measures, such as Conditional Value-at-Risk, do not possess this property. In this work, we introduce a novel class of multi-period convex risk measures that do admit dynamic decomposition.
-''Speaker:'' '''[https://profiles.sussex.ac.uk/p2765-carol-alexander Carol Alexander]''', Professor of Finance, University of Sussex
+Our proposed risk measure evaluates the worst-case expectation of a random outcome across all possible stochastic processes, penalized by their deviations from a nominal process in terms of both the likelihood ratio and the outcome. We show that this risk measure can be reformulated as a dynamic program, where, at each time period, it assesses the worst-case expectation of future costs, adjusting by reweighting and relocating the conditional nominal distribution. This recursive structure enables more efficient computation and clearer interpretation of risk over multiple periods.
-[[Image:Carol1.jpg]]
-''Title:'' Trading and Hedging Bitcoin Volatility [https://youtu.be/c9Fr9Ew5Hys Recorded video]
+''Bio:'' Luhao Zhang is an assistant professor in the Department of Applied Mathematics and Statistics at Johns Hopkins University. Before joining JHU, she was a postdoctoral research scientist in the Department of Industrial Engineering and Operations Research at Columbia University from 2023 to 2024. She completed her Ph.D. in Mathematics at the University of Texas at Austin in 2023.
-''Abstract:''
+Her research lies on interdisciplinary topics that integrate stochastic analysis, mathematical finance, and robust optimization, with an emphasis on how to exploit information optimally for decision-making in stochastic and uncertain environments from modelling and quantitative aspects. Another recent research interest of hers is the mathematical foundation of generative AI, human-AI interactions, and continuous-time reinforcement learning.
-This talk is in three sections. It starts with a general overview of crypto asset markets, focussing on data complexities and the trading behaviour of bitcoin and ether in centralised exchanges. Following this we examine the empirical research on bitcoin implied volatility and the bitcoin variance risk premium which laid the foundation for our collaboration with CryptoCompare to live-stream a bitcoin implied volatility index. The last section examines perpetual futures, a derivative product that is so far unique to unregulated crypto exchanges. By now, several of these exchanges allow very high leverage but substantial margin calls can lead to frequent defaults. We model the impact of margin constraints and default aversion on optimal hedging of bitcoin spot price volatility.
-''Moderator:''  [http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal], University of Toronto
-----
-'''Thursday, January 21, 2021, 1PM-2PM'''
-''Speaker:'' '''[https://sites.google.com/site/alvarocartea/home Alvaro Cartea]''', University of Oxford
-[[Image:Alvaro1.jpg]]
-''Title:''  Optimal Execution with Stochastic and Deterministic Delay [https://youtu.be/ReH-vKtlJFE Recorded video]
-''Abstract:'' We show how traders use aggressive immediate execution limit orders (IELOs) to liquidate a position when there are random delays in all the steps of a trade, i.e., there is latency in the marketplace and latency is random. We frame our model as a delayed impulse control problem in which the trader controls the times and the price limit of the IELOs she sends to the exchange.  Our paper is the first to study an optimal liquidation problem that accounts for: random delays, price impact, and transaction costs.  We introduce a new type of impulse control problem with stochastic (or deterministic) delay, not previously studied in the literature. The value functions are characterised as the solution to a coupled system of a Hamilton-Jacobi-Bellman quasi-variational inequality (HJBQVI) and a partial differential equation. We use a Feynman-Kac representation to reduce the system  to a HJBQVI, for which we prove existence and uniqueness in a viscosity sense. We employ foreign exchange high-frequency trade data to implement the random-latency-optimal strategy  and to compare it with four benchmarks: executing the entire order at once,  optimal execution with deterministic latency, optimal execution with zero latency, and time-weighted average price. For example, in the EUR/USD currency pair, we show that the random-latency-optimal strategy and the deterministic-latency optimal strategy outperform the benchmarks between 3 USD per million EUR traded and 110 USD per million EUR traded, this is between one and 37 times the value of the transaction fees paid by liquidity takers.
-Joint work with and Leandro Sanchez Betancourt.
-''Moderator:''  [https://sites.google.com/view/cialenco Igor Cialenco], Illinois Institute of Technology
-----
-'''Thursday, November 26, 2020'''
-No Seminar due to Thanksgiving Day
-----
-'''Thursday, December 10, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-'''Early Career Talks''' [https://youtu.be/q0mS5IPeN_U Recorded video]
-''Speaker 1:'' '''[https://sites.google.com/view/denafiroozi/home Dena Firoozi]''',  Department of Decision Sciences, HEC Montréal - University of Montreal
-[[Image:Dena1.jpg]]
-''Title:''  Belief Estimation by Agents in Major-Minor LQG Mean Field Games
-''Abstract:'' Motivated by optimal execution in electronic markets where complete market information is not available to traders, we present partially observed LQG mean field games (MFG) consisting of one major agent and a large population of minor agents. We consider the case where the major agent has partial observations of its own state, and each minor agent has partial observations of its own state and the major agent's state. The assumption of partial observations by all agents leads to a new situation involving second-order beliefs (estimates of estimates). This is one of the rare examples of a partially observed game which has a terminating belief of belief recursion. Time permitting, we present an ϵ -Nash equilibrium result for completely observed LQG MFGs with two major agents, and discuss the partial information patterns yielding tractable solutions.
-''Speaker 2:'' '''[https://scholar.google.com/citations?user=ngnYT8gAAAAJ&hl=en  Sveinn Olafsson]''',  Industrial Engineering and Operations Research, Columbia University
-[[Image:Olafsson1.jpg]]
-''Title:''  Personalized Robo-Advising: Enhancing Investment through Client Interaction
-''Abstract:''  Automated investment managers, or robo-advisors, have emerged as an alternative to traditional financial advisors. The viability of robo-advisors crucially depends on their ability to offer personalized financial advice. We introduce a novel framework, in which a robo-advisor interacts with a client to solve an adaptive mean-variance portfolio optimization problem. The risk-return tradeoff adapts to the client's risk profile, which depends on idiosyncratic characteristics, market returns, and economic conditions. We show that the optimal investment strategy includes both a myopic term and intertemporal hedging demand, driven by the client's dynamic risk profile. We characterize portfolio personalization via a tradeoff faced by the robo-advisor between receiving client information in a timely manner and mitigating behavioral biases in the risk profile communicated by the client. We argue that the optimal portfolio's Sharpe ratio and return distribution improve if the robo-advisor counters the client's tendency to reduce market exposure during economic contractions when the market risk-return tradeoff is more favorable. This is joint work with Agostino Capponi and Thaleia Zariphopoulou.
-''Moderator:'' [http://users.wpi.edu/~ssturm/ Stephan Sturm], Department of Mathematical Sciences, Worcester Polytechnic Institute
 -----
+'''December 12, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-'''Thursday, November 12, 2020,  1PM-2PM'''
+''Speaker:''[https://web.stanford.edu/~jblanche/  Jose Blanchet], Stanford University
-''Speaker:'' '''[https://people.epfl.ch/damir.filipovic Damir Filipovic]''', EPFL and Swiss Finance Institute
+[[Image:blanchet(1).jpg]]
-[[Image:Damir1.jpg]]
+''Title:'' Inference in Stochastic Optimization with Heavy Tailed Input
-''Title:''  A Machine Learning Approach to Portfolio Pricing and Risk Management for High-Dimensional Problems [https://youtu.be/zWengy7TPZ0 Recorded Video]
+''Abstract:''  We will start the talk by discussing empirical evidence from a wide range of areas (including insurance, health care, machine learning, among others) suggesting that often infinite variance models are well-suited for inference, particularly in online data-driven decision making. We will argue that infinite variance estimators can be considered appropriate depending on easy-to-monitor features of historical data and on the spatial and temporal scales over which an online algorithm will be deployed (even if the underlying dynamics have finite variance gradients “in theory”). We will then discuss inference tools that can be applied to monitor the quality of solutions of infinite-variance stochastic gradient descent (SGD) based on several asymptotic statistics. Our results extend classical finite-variance weak-convergence analysis of SGD and state-of-the-art infinite variance asymptotic statistics derived under homogeneity conditions which limit the applicability of SGD in typical online optimization tasks. Based on joint work with Aleks Mijatovic, Wenhao Yang.
-''Abstract:'' We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting. The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces. We show results based on polynomial and neural network bases. Both offer superior results to naive Monte Carlo methods and other existing methods like least-squares Monte Carlo and replicating portfolios.
+''Bio:'' Jose Blanchet is a faculty member in the Management Science and Engineering Department at Stanford University – where he earned his Ph.D. in 2004. Prior to joining the Stanford faculty, Jose was a professor in the IEOR and Statistics Departments at Columbia University (2008-2017) and before that he was faculty member in the Statistics Department at Harvard University (2004-2008). Jose is a recipient of the 2009 Best Publication Award given by the INFORMS Applied Probability Society and of the 2010 Erlang Prize. He also received a PECASE award given by NSF in 2010. He worked as an analyst in Protego Financial Advisors, a leading investment bank in Mexico. He has research interests in applied probability and Monte Carlo methods. He serves in the editorial board of ALEA, Advances in Applied Probability, Extremes, Insurance: Mathematics and Economics, Journal of Applied Probability, Mathematics of Operations Research, and Stochastic Systems.
-''Moderator:'' [https://carmona.princeton.edu/ Rene Carmona], Princeton University
 ----
+'''November 14, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+''Speaker:''[https://people.maths.ox.ac.uk/hambly/ Ben Hambly], University of Oxford
-'''Thursday, October 29, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+[[Image:ben1.jpg]]
-''Speaker:'' '''[https://www.fm.mathematik.uni-muenchen.de/personen/professors/francesca_biagini/index.html Francesca Biagini]''', University  of  Munich
+''Title:'' Systemic risk, endogenous contagion and McKean-Vlasov control
-[[Image:Francesca1.jpg]]
+''Abstract:''  We consider some particle system models for systemic risk. The particles represent the health of financial institutions and we incorporate common noise and contagion into their dynamics. Defaults within the system reduce the financial health of other institutions, causing contagion. By taking a mean field limit we derive a McKean-Vlasov equation for the financial system as a whole. The task of a central planner, who wishes to control the system to prevent systemic events at minimal cost, leads to a novel McKean-Vlasov control problem. We discuss the mathematical issues and illustrate the results numerically.
-''Title:''  Reduced-form setting under model uncertainty with non-linear affine intensities [https://youtu.be/YSspgAxFgvs Recorded Video]
+''Bio:'' Ben Hambly received his PhD in 1990 from the University of Cambridge and held lectureships at the Universities of Edinburgh and Bristol before moving to Oxford in 2000. He has interests in stochastic PDEs, rough paths, random processes in random and fractal environments, reinforcement learning ,modelling order books, systemic risk and electricity markets.
-''Abstract:'' In this talk we present  a market model including financial assets and  life insurance liabilities  within a reduced-form framework under model uncertainty by following  (1). In particular we extend this framework to include mortality  intensities following an affine process under parameter uncertainty, as defined in (2). This allows both to introduce the definition of a longevity bond under model uncertainty in a consistent way with the classical case under one prior, as well as to compute it by explicit formulas or by numerical methods. We also study conditions to guarantee the existence of a càdlàg modification for the longevity bond’s value process.  Furthermore, we show how the resulting market model extended with the longevity bond is arbitrage-free and study arbitrage-free pricing of contingent claims or life insurance liabilities in this setting.
-This talk is based on:
-(1) Francesca Biagini and Yinglin Zhang. Reduced-form framework under model uncertainty. The Annals of Applied Probability, 29(4):2481–2522, 2019.
-(2) Francesca Biagini and Katharina Oberpriller. Reduced-form setting under model uncertainty with non-linear affine intensities.  Preprint University of Munich and Gran Sasso Science Institute, 2020.
-(3) Tolulope Fadina, Ariel Neufeld, and Thorsten Schmidt. Affine processes under parameter uncertainty. Probability, Uncertainty and Quantitative Risk volume 4 (5), 2019.
-''Moderator:'' [http://fouque.faculty.pstat.ucsb.edu/ Jean-Pierre Fouque], Department of Statistics and Applied Probability, UC Santa Barbara
 ----
+''October 10, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+''Speaker:'' [https://www.buffalo.edu/cas/math/people/faculty/bichuch.html Maxim Bichuch], University at Buffalo
-'''Thursday, October 15, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+[[Image:maxim1.jpg]]
-''Panel Discussion:'' Implications of COVID-19 on financial markets  [https://youtu.be/5JE1D7m5WWY Recorded video]
+''Title:'' A Deep Learning Scheme for Solving Fully Nonlinear Partial Differential Equation
-''Panelists:''
+''Abstract:''  We study the convergence of a deep learning algorithm applied to a general class of fully nonlinear second order Partial Differential Equations. By using a suitable finite difference approximation to the loss function of the deep learning scheme we show the convergence of the numerical solution to the unique viscosity solution. We apply our results and illustrate this convergence to the finite horizon optimal investment problem with proportional transaction costs in single and multi-asset settings.
-[[Image:fleming1.jpg]]
+''Bio:'' Maxim Bichuch holds a M.S. from NYU and a Ph.D. from CMU both in Financial Mathematics. He has been a Postdoctoral Research Associate & Lecturer in the ORFE department in Princeton, and an Assistant Professor at WPI and JHU, before joining the department of Mathematics at UB. Prior to obtaining his Ph.D. He has also gained corporate experience working for Citigroup and Bear Stearns. His research interests include optimal portfolio selection, optimal investment and consumption, optimal control with transaction costs, viscosity solutions, stochastic volatility, credit, funding and counterparty risks, and most recently electricity markets, machine learning, decentralized finance and fintech.
-Michael J. Fleming, Vice President and Financial Economist, Federal Reserve Bank of New York, New York, US
-[[Image:huang1.jpg]]
-Wenqian Huang, Economist, Bank for International Settlements (BIS), Basel,  Switzerland
-[[Image:rios1.jpg]]
-David Rios, Lecturer at Columbia University and NYU Tandon
-''Abstract:'' This panel will discuss the implications of COVID 19 on financial markets. Dr. Fleming will discuss the pandemic's effect on the Treasury market and how – and why – the Fed took unprecedented steps to address the market disruptions. Treasury market volatility and illiquidity jumped to unusually high levels in March 2020 due to unwinding of relative value trades, selling by foreign investors, limited dealer intermediation capacity, and the withdrawal of some market participants. Dr. Huang will discuss the resilience of central counterparties (CCPs) during this period of turbulence. CCPs issued large margin calls, but the extent of the procyclicality of margining is the consequence of various design choices. Dr, Huang will highlight systemic considerations related to the nexus between banks and CCPs, and why central banks need to assess banks and CCPs jointly rather than in isolation in regards to margins. Dr. Rios will discuss the massive and quick reaction to COVID by the US Government. With respect to the mortgage market there has been much success in averting a 2008 type drop in home prices despite record high unemployment. He will argue why policies to provide liquidity to the American homeowner through refinancing seem to have improved since 2008, but are still less effective than 2003.
-''Moderator:'' [http://www.columbia.edu/~ac3827/index.html Agostino Capponi],  Department of Industrial Engineering and Operations Research,  Columbia University
 ----
+'''June 13, 2024, 1PM-2PM (EST)'''
+''Speaker:'' [https://users.renyi.hu/~rasonyi/ Miklós Rásonyi], HUN-REN Alfréd Rényi Institute of Mathematics
-'''Thursday, October 1, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+[[Image:miklos.jpg]]
-''Speaker:'' '''[https://www.samuel-drapeau.info/ Samuel Drapeau]''', Shanghai Jiao Tong University
+''Title:'' Portolio choice for exponential investors when prices are mean-reverting
-[[Image:Samuel1.jpg]]
+''Abstract:'' Several asset classes show mean-reverting features, e.g. commodities, commodity futures,
+long-term safe assets (gold). We investigate the portfolio choice problem for
-''Title:''  On Detecting Spoofing Strategies in High Frequency Trading [https://youtu.be/r1Td1d7rmJo Recorded video]
+investors with exponential utilities (=high risk aversion) as the investment horizon T
+tends to infinity. It turns out that the optimal equivalent safe rate grows in a superlinear
-''Abstract:'' The development of high frequency and algorithmic trading allowed to considerably reduce the bid ask spread by increasing liquidity in limit order books. Beyond the problem of optimal placement of market and limit orders, the possibility to cancel orders for free leaves room for price manipulations, one of such being spoofing. Detecting spoofing from a regulatory viewpoint is challenging due to the sheer amount of orders and difficulty to discriminate between legitimate and manipulative flows of orders. However, it is empirical evidence that volume imbalance reflecting offer and demand on both sides of the limit order book has an impact on subsequent price movements. Spoofers use this effect to artificially modify the imbalance by posting limit orders and then execute market orders at subsequent better prices while canceling at a high speed their previous limit orders. In this work we set up a model to determine where a spoofer would place its limit orders to maximize its gains as a function of the imbalance impact on the price movement. We study the solution of this non local optimization problem as a function of the imbalance. With this at hand, we calibrate on real data from TMX the imbalance impact (as a function of its depth) on the resulting price movement. Based on this calibration and theoretical results, we then provide some methods and numerical results as how to detect in real time some eventual  spoofing behavior based on Wasserstein distances. Joint work with Tao Xuan (SJTU), Ling Lan (SJTU) and Andrew Day (Western University)
+way, depending on the strength of the mean-reversion effect. We cannot find
+the exact optimisers but construct a family of simple, explicit strategies that
-''Moderator:'' [http://ludkovski.faculty.pstat.ucsb.edu/ Mike Ludkovski], Department of Statistics and Applied Probability, UC Santa Barbara
+are optimal asymptotically (they generate equivalent safe rates of the optimal order).
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+Interestingly, the presence or absence of a drift leads to entirely different
+conclusions, the nonzero drift case spectacularly outperforming the driftless one.
+Time permitting, we also review some
-'''Thursday, September 17, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' [https://carmona.princeton.edu/ Rene Carmona], Princeton University
-[[Image:Rene1.jpg]]
-''Title:'' Contract theory and  mean field games to inform epidemic models [https://youtu.be/S9p7UWcgWkw Recorded video]
-''Abstract:'' After a short introduction to contract theory, we review recent results on models involving one principal and a field of agents, both for continuous and discrete state spaces.
-We conclude with the discussion of an application to the control of the spread of an epidemic to illustrate the potential to inform regulatory decisions.
-''Moderator:''  [http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal],  University of Toronto
-----
-'''Thursday, September 3, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' [https://sites.google.com/view/sergey-nadtochiy Sergey Nadtochiy], Illinois Institute of Technology
-[[Image:Sergey1.jpg]]
-''Title:''  A simple microstructural explanation of the concavity of price impact [https://youtu.be/nr83hx84o88 Recorded video]
-''Abstract:'' I will present a simple model of market microstructure which explains the concavity of price impact. In the proposed model, the local relationship between the order flow and the fundamental price (i.e. the local price impact) is linear, with a constant slope, which makes the model dynamically consistent. Nevertheless, the expected impact on midprice from a large sequence of co-directional trades is nonlinear and asymptotically concave. The main practical conclusion of the model is that, throughout a meta-order, the volumes at the best bid and ask prices change (on average) in favor of the executor. This conclusion, in turn, relies on two more concrete predictions of the model, one of which can be tested using publicly available market data and does not require the (difficult to obtain) information about meta-orders. I will present the theoretical results and will support them with the empirical analysis.
-''Moderator:'' [https://sircar.princeton.edu/ Ronnie Sircar], Princeton University
-----
-'''Thursday, August 20, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' [https://www.guasoni.com/ Paolo Guasoni], Dublin City University
-[[Image:Paolo1.jpg]]
-''Title:''  The cost of Lightning Network channels and its implications for the network's structure
-[https://youtu.be/E7CmPisjw-A Recorded video]
-''Abstract:'' A channel in the Lightning Network is a protocol to secure bitcoin payments and escrow holdings between two parties, designed to increase transaction immediacy and reduce blockchain congestion. In a lightning channel, each party commits collateral towards future payments to the counterparty. Payments are cryptographically secured updates of the collaterals. This paper obtains conditions under which two parties optimally establish a channel, finds explicit formulas for channels’ costs, and derives implications for the network’s structure under cooperation assumptions among small sets of users. As optimal network structures eschew redundant channels, they typically exhibit low degree. If agents’ payment rates are sufficiently homogeneous, centralization through a common intermediary may become optimal.
-''Moderator:''  [http://www.columbia.edu/~ac3827/ Agostino Capponi],  Columbia University
-----
-'''Thursday, July 23, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-'''Early Career Talks'''
-[https://sites.google.com/site/ruimenghu1/ Ruimeng Hu], University of California Santa Barbara
-[[Image:Ruimeng1.jpg]]
-''Title:''  Deep fictitious play for stochastic differential games [https://youtu.be/nf9BAdzeO6s Recorded Video]
-''Abstract:'' Differential games, as an offspring of game theory and optimal control, provide the modeling and analysis of conflict in the context of a dynamic system. Computing Nash equilibria is one of the core objectives in differential games, with a major bottleneck coming from the notorious intractability of N-player games. This leads to the difficulty of the curse of dimensionality, which will be overcome by the algorithms of deep fictitious play using machine learning tools. We discuss the approaches to solve open-loop and Markovian Nash equilibria with convergence analysis.
-[https://max.reppen.ch/ A. Max Reppen], Boston University
-[[Image:Max1.jpg]]
-''Title:''  Discrete dividend payments in continuous time [https://youtu.be/nf9BAdzeO6s Recorded Video]
-''Abstract:'' We propose a model in which dividend payments occur at regular, deterministic intervals in an otherwise continuous model. This contrasts traditional models where either the payment of continuous dividends is controlled or the dynamics are given by discrete time processes. Moreover, between two dividend payments, the structure allows for other types of control; we consider the possibility of equity issuance at any point in time. The value is characterized as the fixed point of an optimal control problem with periodic initial and terminal conditions. We prove the regularity and uniqueness of the corresponding dynamic programming equation, and the convergence of an efficient numerical algorithm that we use to study the problem. The model enables us to find the loss caused by infrequent dividend payments. We show that under realistic parameter values this loss varies from around 1% to 24% depending on the state of the system, and that using the optimal policy from the continuous problem further increases the loss.
-''Moderator:''  [https://sites.google.com/view/cialenco Igor Cialenco], Illinois Institute of Technology
-----
-'''Thursday, June 25, 2020, 1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' [http://fouque.faculty.pstat.ucsb.edu/ Jean-Pierre Fouque], University of California Santa Barbara
-[[Image:JP1.jpg]]
-''Title:''  Accuracy of Approximation for Portfolio Optimization under Multiscale Stochastic Environment
-''[https://youtu.be/RMe7td6r-M0 Recorded Video]''
-''Abstract:'' For the problem of portfolio optimization when returns and volatilities are driven by stochastic factors, approximations for value functions and optimal strategies have been proposed in the regime where these factors are running on slow and fast timescales. But, until now, rigorous results of accuracy of these approximations have only been obtained for cases that can be linearized, typically limited to power utilities and a single factor driving the environment. This talk is about treating cases with general utility functions and multi factors. Our approach is to construct sub- and super- solutions to the fully nonlinear problem such that their difference is at the desired level of accuracy. We first present a regular perturbation case with a power utility and two factors nearly fully correlated. Then, we show how to deal with a singular perturbation in the case of a general utility function with a fast varying factor.
-Joint work with Maxim Bichuch, Ruimeng Hu, and Ronnie Sircar.
-''Moderator:'' [http://www.columbia.edu/~ac3827/ Agostino Capponi],  Department of Industrial Engineering and Operations Research, Columbia University
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-'''Thursday, June 11, 2020, 1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' [https://people.math.ethz.ch/~patrickc/ Patrick Cheridito],  ETH Zurich
-[[Image:Patrick1.jpg]]
-''Title:''  Deep optimal stopping
-''[https://youtu.be/wob_EhtFLR8 Recorded Video]''
-''Abstract:'' I present a deep learning method for optimal stopping problems which directly learns the optimal stopping rule from Monte Carlo samples. As such, it is broadly applicable in situations where the underlying randomness can efficiently be simulated. The approach is tested on three problems: the pricing of a Bermudan max-call option, the pricing of a callable multi barrier reverse convertible and the problem of optimally stopping a fractional Brownian motion. In all three cases it produces very accurate results in high-dimensional situations with short computing times. Joint work with Sebastian Becker and Arnulf Jentzen.
-''Moderator:'' '''[http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal]''', Department of Statistical Sciences, University of Toronto
-----
-'''Thursday, May 28, 2020, 1PM-2PM''' (Eastern US; GMT-4);
-''Panel Discussion:'' '''Energy Markets'''
-''[https://www.youtube.com/watch?v=0FoI7Akh8o4 Recorded Video]''
-''Abstract:'' The aim is to discuss recent events in energy/electricity/commodity markets, such as negative prices, as well as related mathematical modeling challenges.
-''Panelists:''
-[[Image:ReneAid1.jpg]]
-<jsmath>\qquad </jsmath> [https://sites.google.com/view/reneaid Rene Aid], Université Paris-Dauphine, France
-[[Image:swindle1.jpg]]
-<jsmath>\qquad </jsmath> [https://scovilleriskpartners.com/team/glen-swindle/ Glen Swindle], Scoville Risk Partners, USA
-[[Image:Zef1.jpg]]
-<jsmath>\qquad </jsmath> Zef Lokhandwalla, Bloomberg LP, USA
-[[Image:Mike1.jpg]]
-<jsmath>\qquad </jsmath> [http://ludkovski.faculty.pstat.ucsb.edu/ Mike Ludkovski], University of California Santa Barbara, USA
-''Moderator:'' [https://sircar.princeton.edu/ Ronnie Sircar], ORFE, Princeton University
-----
-'''Thursday, May 14, 2020, 1PM-2:30PM''' (Eastern US; GMT-4);
-''Speaker: [https://en.wikipedia.org/wiki/Bruno_Dupire Bruno Dupire], Head of Quantitative Research, Bloomberg LP''
-[[Image:Bruno1.jpg]]
-''Title:''  '''The Geometry of Money and the Perils of Parameterization'''
-''[https://www.youtube.com/watch?v=KKf223qn3Po Recorded Video]''
-''Abstract:'' Market participants use parametric forms to make sure prices are orderly aligned. It may prevent static arbitrages but could it lead to dynamic arbitrages?
-Markets trade thousands of underlying, each one with tens or even hundreds of options, quoted throughout the day. Needless to say, the quotes are not generated manually. They are automated and derived from a functional form with a few parameters. If we know this parameterization, we know in advance how the prices tomorrow of many traded securities will belong to a low dimensional (number of parameters) manifold in a high dimensional (number of securities). If the vector of today prices does not belong to the convex hull of the manifold it creates arbitrage. We examine market practice (Black-Scholes, stochastic volatility models, interest rate interpolation by piecewise constant instantaneous forward rates, converging implied volatilities for extreme strikes in FX...) and show that many violate the no arbitrage condition.
-''Moderator:'' [https://sites.google.com/view/cialenco Igor Cialenco], Illinois Institute of Technology
-----
-'''Thursday, April 30, 2020, 1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' '''[https://sites.google.com/site/blankanorahorvath/home Blanka Horvath]''', Department of Mathematics, King's College London, UK
-[[Image:Horvath1.jpg]]
-''Title:'' '''A Data-driven Market Simulator for Small Data Environments'''
-''[https://www.youtube.com/watch?v=xXwHBQFOVoc Recorded Video]''
-''Abstract:'' In this talk we investigate how Deep Hedging brings a new impetus into the modelling of financial markets. While a DNN-based  data-driven market generation unveils a new and highly flexible way of modelling financial time series, it  is by no means "model-free". In fact, the concrete modelling choice is decisive for the features of the resulting generative model. After a very short walk through historical market models we proceed to neural network based generative modelling approaches for financial time series. We then investigate some of the challenges to achieve good results in the latter, and highlight some applications and pitfalls.  While most generative models tend to rely on large amounts of training data, we present here a parsimonious generative model that works reliably even in environments where the amount of available training data is notoriously small. Furthermore, we discuss how a rough paths perspective combined with a parsimonious Variational Autoencoder framework provides a powerful way for encoding and evaluating financial time series data in such environments. Lastly, we also discuss some pricing and hedging considerations in a DNN framework and their connection to Market Generation.  The talk is based on joint work with H. Buehler, I. Perez Arribaz, T. Lyons and B. Wood.
-''Moderator:'' [http://www.columbia.edu/~ac3827/ Agostino Capponi],  Department of Industrial Engineering and Operations Research, Columbia University
-----
-'''Thursday, April 16, 2020, 1PM-2PM''' (Eastern US; GMT-4)
-''Speaker:'' '''[https://soner.princeton.edu Mete Soner]''', Department of Operations Research and Financial Engineering, Princeton University
-[[Image:soner.jpg]]
-''Title:''  '''Trading with impact'''
-''[https://www.youtube.com/watch?v=G15CHXcf38g Recorded Video]''
-''Abstract:''  It is well known that large trades cause unfavorable price impact resulting in trading losses.  These losses are particularly high when the underlying instrument is not liquid enough or when the trade size is large.  Other type of market frictions such as transaction costs also cause similar effects.  When one considers hedging or portfolio management or equilibrium models these effects must be taken into account.  After describing widely used approaches of Cetin, Jarrow & Protter and Almgren & Chris, I first study the impact of resilience and then the structure of the optimal portfolios.  This talk will be a summary of many results obtained jointly with many people  including, Peter Bank, Bruno Bouchard, Umut Cetin,  Ludovic Moreau, Johannes Muhle-Karbe, Nizar Touzi and Moritz Voss.
-''Moderator:'' '''[http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal]''', Department of Statistical Sciences, University of Toronto
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 === Past steering committees ===