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From SIAG-FM
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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.
Forthcoming Talks
November 14, 2024, 1PM-2PM (EST) Registration link
Speaker:Ben Hambly, University at 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.
Past Talks
November 14, 2024, 1PM-2PM (EST) Registration link
Speaker:Ben Hambly, University at 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
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)
