Current events

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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

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.

[edit] Past Talks

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)

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Contents

[edit] SIAG/FME virtual seminars series

[edit] Forthcoming Talks

[edit] Past Talks

[edit] Past steering committees

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 The series of virtual talks, started by [https://www.siam.org/membership/activity-groups/detail/financial-mathematics-and-engineering 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.
-<jsmath>\diamond</jsmath> The talks will run every other week, and at least until the next [https://www.siam.org/conferences/cm/conference/fm21 SIAG/FME Biennial Meeting in June 2021]
+* 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 [https://www.bachelierfinance.org/bachelier-finance-society-world-seminars-online Bachelier Finance Society]
-<jsmath>\diamond</jsmath> The talks will alternate with those set up by the [https://www.bachelierfinance.org/bachelier-finance-society-world-seminars-online 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'''.
-<jsmath>\diamond</jsmath> All talks will be delivered remotely using Zoom.
+* 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.
-<jsmath>\diamond</jsmath> The talks are open to the public. Due to security reasons, '''all attendees have to register'''.
-<jsmath>\diamond</jsmath> 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 [http://lists.siam.org/mailman/listinfo/siam-fme SIAG/FME Mailing List].
-<jsmath>\diamond</jsmath> 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:'''
-<jsmath>\quad</jsmath> [http://www.columbia.edu/~ac3827/ Agostino Capponi] (SIAG/FME Chair, Columbia University)
+* Chair:  [https://www.maths.ox.ac.uk/people/samuel.cohen Samuel Cohen],
+* Vice Chair: [https://homepage.univie.ac.at/christa.cuchiero/ Christa Cuchiero],
+* Program Director: [https://personal.lse.ac.uk/veraart/ Luitgard A. M. Veraart],
+* Secretary:  [https://sites.google.com/site/ibrahimekren/home Ibrahim Ekren]
-<jsmath>\quad</jsmath> [https://sites.google.com/view/cialenco Igor Cialenco] (SIAG/FME Program Director, Illinois Institute of Technology)
-<jsmath>\quad</jsmath> [http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal] (University of Toronto)
-<jsmath>\quad</jsmath> [https://sircar.princeton.edu/ Ronnie Sircar] (Princeton University)
+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.
+----
 === Forthcoming Talks ===
-----
-----
+'''December 12, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
+''Speaker:''[https://web.stanford.edu/~jblanche/  Jose Blanchet], Stanford University
-'''Thursday, November 26, 2020'''
+[[Image:blanchet(1).jpg]]
-No Seminar due to Thanksgiving Day
+''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.
-'''Thursday, December 10, 2020,  1PM-2PM''' (Eastern US; GMT-4); [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration Link]
+''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.
-'''Early Career Talks'''
-''Speaker 1:'' '''[https://sites.google.com/view/denafiroozi/home Dena Firoozi]''',  Department of Decision Sciences,  University of Montreal
-''Title:''  TBA
-''Abstract:'' TBA
-''Speaker 2:'' '''[https://scholar.google.com/citations?user=ngnYT8gAAAAJ&hl=en  Sveinn Olafsson]''',  Industrial Engineering and Operations Research, Columbia University
-''Title:''  TBA
-''Abstract:'' TBA
-''Moderator:'' [http://users.wpi.edu/~ssturm/ Stephan Sturm], Department of Mathematical Sciences, Worcester Polytechnic Institute
------
-.
-=== Past Talks ===
-----
-----
-'''Thursday, October 29, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' '''[https://www.fm.mathematik.uni-muenchen.de/personen/professors/francesca_biagini/index.html Francesca Biagini]''', University  of  Munich
-[[Image:Francesca1.jpg]]
-''Title:''  Reduced-form setting under model uncertainty with non-linear affine intensities [https://youtu.be/YSspgAxFgvs Recorded Video]
-''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
-----
-'''Thursday, October 15, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Panel Discussion:'' Implications of COVID-19 on financial markets  [https://youtu.be/5JE1D7m5WWY Recorded video]
-''Panelists:''
-[[Image:fleming1.jpg]]
-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
 ----
-'''Thursday, October 1, 2020,  1PM-2PM''' (Eastern US; GMT-4);
-''Speaker:'' '''[https://www.samuel-drapeau.info/ Samuel Drapeau]''', Shanghai Jiao Tong University
-[[Image:Samuel1.jpg]]
-''Title:''  On Detecting Spoofing Strategies in High Frequency Trading [https://youtu.be/r1Td1d7rmJo Recorded video]
-''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)
-''Moderator:'' [http://ludkovski.faculty.pstat.ucsb.edu/ Mike Ludkovski], Department of Statistics and Applied Probability, UC Santa Barbara
 ----
+=== Past Talks ===
+'''November 14, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-'''Thursday, September 17, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+''Speaker:''[https://people.maths.ox.ac.uk/hambly/ Ben Hambly], University of Oxford
-''Speaker:'' [https://carmona.princeton.edu/ Rene Carmona], Princeton University
+[[Image:ben1.jpg]]
-[[Image:Rene1.jpg]]
+''Title:'' Systemic risk, endogenous contagion and McKean-Vlasov control
-''Title:'' Contract theory and  mean field games to inform epidemic models [https://youtu.be/S9p7UWcgWkw Recorded video]
+''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.
-''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.
+''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.
-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
 ----
+''October 10, 2024, 1PM-2PM (EST)''' [https://siam.zoom.us/webinar/register/WN_s8rIcHwiS-uPM3Dkuok-Wg Registration link]
-'''Thursday, September 3, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+''Speaker:'' [https://www.buffalo.edu/cas/math/people/faculty/bichuch.html Maxim Bichuch], University at Buffalo
-''Speaker:'' [https://sites.google.com/view/sergey-nadtochiy Sergey Nadtochiy], Illinois Institute of Technology
+[[Image:maxim1.jpg]]
-[[Image:Sergey1.jpg]]
+''Title:'' A Deep Learning Scheme for Solving Fully Nonlinear Partial Differential Equation
-''Title:''  A simple microstructural explanation of the concavity of price impact [https://youtu.be/nr83hx84o88 Recorded video]
+''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.
-''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.
+''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.
-''Moderator:'' [https://sircar.princeton.edu/ Ronnie Sircar], Princeton University
 ----
+'''June 13, 2024, 1PM-2PM (EST)'''
-'''Thursday, August 20, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+''Speaker:'' [https://users.renyi.hu/~rasonyi/ Miklós Rásonyi], HUN-REN Alfréd Rényi Institute of Mathematics
-''Speaker:'' [https://www.guasoni.com/ Paolo Guasoni], Dublin City University
+[[Image:miklos.jpg]]
-[[Image:Paolo1.jpg]]
+''Title:'' Portolio choice for exponential investors when prices are mean-reverting
-''Title:''  The cost of Lightning Network channels and its implications for the network's structure
+''Abstract:'' Several asset classes show mean-reverting features, e.g. commodities, commodity futures,
-[https://youtu.be/E7CmPisjw-A Recorded video]
+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
-''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.
+=== Past steering committees ===
-''Moderator:''  [http://www.columbia.edu/~ac3827/ Agostino Capponi],  Columbia University
+-2022
-----
+<jsmath>\quad</jsmath> [http://www.columbia.edu/~ac3827/ Agostino Capponi] (SIAG/FME Chair, Columbia University)
-'''Thursday, July 23, 2020,  1PM-2PM''' (Eastern US; GMT-4);
+<jsmath>\quad</jsmath> [https://sites.google.com/view/cialenco Igor Cialenco] (SIAG/FME Program Director, Illinois Institute of Technology)
-'''Early Career Talks'''
+<jsmath>\quad</jsmath> [http://sebastian.statistics.utoronto.ca/ Sebastian Jaimungal] (University of Toronto)
+<jsmath>\quad</jsmath> [https://sircar.princeton.edu/ Ronnie Sircar] (Princeton University)
-[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
-----
-'''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
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-'''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
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-'''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
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-'''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
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-'''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