A series of meetings with leading international scholars in the field of financial and actuarial mathematics
Global Seminar
In three years of its work, the Seminar has become the main highlight in the field of financial mathematics in Russia
Format: online
Language: Russian / English
More than 50 speakers
The speakers are globally recognized scholars and industry experts
Vast geography of participants — from Los Angeles to Sydney
The Seminar is open to everyone interested in financial mathematics
Seminar director
Yuri Kabanov
Dr. Sci. in Phys. and Maths, Professor
Chairman of the Board of Directors
Scientific Director of the Foundation
Member of Academia Europaea

The program for spring'25
February 15
Evgeny Burnaev
Adversarial Schrödinger Bridge Matching for Fast Generative Modeling
Topic:
15:00 (MSC)
Skoltech, Russia
The Schrödinger Bridge (SB) problem offers a powerful framework for combining optimal transport and diffusion models. A promising recent approach to solve the SB problem is the Iterative Markovian Fitting (IMF) procedure, which alternates between Markovian and reciprocal projections of continuous-time stochastic processes. However, the model built by the IMF procedure has a long inference time due to using many steps of numerical solvers for stochastic differential equations.
To address this limitation, we propose a novel Discrete-time IMF (D-IMF) procedure in which learning of stochastic processes is replaced by learning just a few transition probabilities in discrete time. Its great advantage is that in practice it can be naturally implemented using the Denoising Diffusion GAN (DD-GAN), an already well-established adversarial generative modeling technique. We show that our D-IMF procedure can provide the same quality of unpaired domain translation as the IMF, using only several generation steps instead of hundreds.
February 22
Renyuan Xu
Generative Diffusion Models: Foundations and Financial Applications
Topic:
15:00 (MSC)
New York University, USA
In recent years, generative AI has profoundly impacted various fields through its ability to model large-scale datasets and synthesize new content. Among these innovations, diffusion models offer a robust framework by iteratively refining noise into data, significantly enhancing the generation of high-fidelity and diverse data samples. Despite its empirical success in different domains, the theoretical foundations and systematic design of diffusion models remain largely unexplored. Furthermore, the use of diffusion models to generate dynamic data with complex structures, such as those found in financial systems, is still in an early stage.

In this talk, we will discuss the mathematical foundations of diffusion models from both optimization and generalization perspectives. We will also demonstrate how diffusion models can be applied to generate high-dimensional asset returns, addressing the curse of dimensionality by leveraging structural properties. Finally, we will briefly talk about how to incorporate domain-specific knowledge, such as volatility clustering in financial time series, to enhance the authenticity of generated data.
March 1
Urusov Mikhail
Representation property for 1d general diffusion semimartingales
Topic:
15:00 (MSC)
University of Duisburg-Essen, Germany
A general diffusion semimartingale is a 1d continuous semimartingale that is also a regular strong Markov process. The class of general diffusion semimartingales is a natural generalization of the class of (weak) solutions to SDEs. A continuous semimartingale has the representation property if all local martingales w.r.t. its canonical filtration have an integral representation w.r.t. its continuous local martingale part. The representation property is closely related to market completeness. We show that the representation property holds for a general diffusion semimartingale if and only if its scale function is (locally) absolutely continuous in the interior of the state space. Surprisingly, this means that not all general diffusion semimartingales possess the representation property, which is in contrast to the SDE case. Furthermore, it follows that the laws of general diffusion semimartingales with absolutely continuous scale functions are extreme points of their semimartingale problems. We construct a general diffusion semimartingale whose law is not an extreme point of its semimartingale problem. This contributes to the solution of the problems posed by Jacod and Yor and by Stroock and Yor on the extremality of strong Markov solutions (to martingale problems). This is a joint work with David Criens.
March 15
Dr Abdel KENOUFI
Introduction to Tropical Mathematics and some applications.
Topic:
17:00 (MSC)
University of Strasbourg, France
Until recently, Mathematics, Physics and Computer Sciences were organized around algebraic structures such as Groups, Rings, and Fields, which are not well-suited to modeling and solving Non-Linear
problems. Tropical Mathematics find their roots in theoretical informatics and computer sciences, especially in operations research, and automation. It is still a productive and rich approach in pure and applied mathematics,
in theoretical and mathematical physics, informatics and engineering
sciences. They are constructed on Idempotent Semi-Rings and Dioids, and permit an extension of Linear methods to Non-Linear problems and provide powerful analyzing and computing tools in Theoretical Physics and Applied Mathematics. This new type of Mathematics is based on original algebraic frameworks which yield to define General Topologies, Analysis, and Functional Analysis. This allows to revisit definitions of Integration, Non-Linear Distributions, Legendre Transforms, and Wavelets Theory for example. Some applications to Partial Differential Equations, Images and Signals Processing, and Intervals Computing, will be exhibited in the seminar in order to illustrate the versatility and efficiency of Tropical Mathematics.
March 22
Ekaterina Palamarchuk
Non-ergodic optimality criteria for linear stochastic systems and their applications
Topic:
15:00 (MSC)
Central Economic and Mathematical Institute of the Russian Academy of Sciences, Russia
The talk is devoted to the study of time-varying linear stochastic control systems. In order to derive an optimal control law over an infinite time horizon, non-ergodic optimality criteria are introduced, extending the notions of long-run averages. We consider two special examples of linear time-varying control systems. A system with a time-varying diffusion matrix and a system involving discounting in the cost are examined. We also discuss issues related to convergence rates for non-ergodic criteria. The final part of the talk is devoted to the study of asymptotically autonomous LQG control systems. Such systems may arise due to linearization, running optimization algorithms, and estimation and identification tasks under cost uncertainty. We introduce the regret as the performance loss resulted from the employment of a simple nominally optimal time-invariant feedback instead of an optimal time-varying stable control law. The notion of regret is widely used in adaptive control, machine, and reinforcement learning. The reason comes from the fact that a truly optimal control law may not be available for implementation, but it’s still possible to design another control preserving some optimality when the planning or observation horizon increases. Thus, a natural task to estimate the regret seems relevant here. We provide corresponding asymptotic estimates in almost sure sense and on the average.
March 29
Christa Cuchiero
Neural-signature methods and applications in finance
Topic:
15:00 (MSC)
University of Vienna, Austria
Signature methods represent a non-parametric way for extracting characteristic features from time series data which is essential in machine learning tasks, mathematical finance and risk assessment.
One focus of this talk lies on the use of signature as universal linear regression basis of certain continuous paths functionals in financial applications. In these applications key quantities that have to be computed efficiently are the expected signature or the characteristic function of the signature of some underlying stochastic process. Surprisingly this can be achieved for generic classes of diffusion processes, called signature SDEs, via techniques from affine and polynomial processes.
In terms of concrete applications we present recent contributions from signature based asset price models for joint VIX and SPX calibration and control problems in stochastic portfolio theory.
The talk is based on several joint works with Guido Gazzani, Janka Möller, Francesca Primavera, Sara-Svaluto Ferro and Josef Teichmann.
The program for autumn'24
September 21
Pavel Shevchenko
Solving stochastic dynamic integrated climate-economy models
Topic:
13:00 (12:00 CET)
Macquarie University, Australia
The classical dynamic integrated climate-economy (DICE) model has become the iconic typical reference point for the joint modelling of economic and climate systems, where all six model state variables (including carbon concentration, temperature, and economic capital) evolve over time deterministically and are affected by two controls (carbon emission mitigation rate and consumption). We consider the DICE model with stochastic shocks in various parts of the model and solve it under several scenarios as an optimal stochastic control problem to find the optimal policies in the presence of uncertainty. In the case of many stochastic shocks, to solve the problem we develop the least squares Monte Carlo (LSMC) method — a popular simulation method for solving optimal stochastic control problems in quantitative finance. To address the complexity and high dimensionality of the model, we incorporate deep neural network approximations in place of standard regression techniques within the LSMC framework.

This talk is based on the following papers:

1. A. Arandjelović, P.V. Shevchenko, T. Matsui, D. Murakami, T.A. Myrvoll (2024). Solving stochastic climate-economy models: A deep least-squares Monte
Carlo approach. http://arxiv.org/abs/2408.9 642

2. P.V. Shevchenko, D. Murakami, T. Matsui, T.A. Myrvoll (2022). Impact of COVID-19 type events on the economy and climate under the stochastic DICE model. Environmental Economics and Policy Studies 24, 459−476. https://ssrn.com/abstract=3 954 108
September 28
Yury Yanovich
Increasing Transparency and Accessibility of Blockchains
Topic:
15:00 (13:00 CET)
Skolkovo Institute of Science and Technology, Russia
Blockchains are characterized by algorithms for synchronizing data between network nodes (consensus), methods of data storage, and the programs executed on them (smart contracts). The talk will examine each of these aspects using selected tasks as examples: fault-tolerant random number generation, trees for provable responses to user queries, and risk of a platform for cryptocurrency-backed lending.

References:

[1] Krasnoselskii, M., Melnikov, G., & Yanovich, Y. (2020). No-dealer: Byzantine fault-tolerant random number generator. In IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[2] Krasnoselskii, M., Melnikov, G., & Yanovich, Y. (2021). DisCO: Peer-to-Peer Random Number Generator in Partial Synchronous Systems. In 2021 3rd Conference on Blockchain Research & Applications for Innovative Networks and Services (BRAINS).
[3] Chaleenutthawut, Y., Davydov, V., Evdokimov, M., Kasemsuk, S., Kruglik, S., Melnikov, G., & Yanovich, Y. (2024). Loan Portfolio Dataset From MakerDAO Blockchain Project. IEEE Access.
October 5
Peter TANKOV
Optimal stopping and divestment timing under scenario ambiguity and learning
Topic:
15:00

Institute Polytechnique de Paris, France
Aiming to analyze the impact of environmental transition on the value of assets and on asset stranding, we study optimal stopping and divestment timing decisions for an economic agent whose future revenues depend on the realization of a scenario from a given set of possible futures. Since the future scenario is unknown and the probabilities of individual prospective scenarios are ambiguous, we adopt the smooth model of decision making under ambiguity aversion of Klibanoff et al (2005), framing the optimal divestment decision as an optimal stopping problem with learning under ambiguity aversion. We then prove a minimax result reducing this problem to a series of standard optimal stopping problems with learning. The theory is illustrated with two examples: the problem of optimally selling a stock with ambigous drift, and the problem of optimal divestment from a coal-fired power plant under transition scenario ambiguity.
October 12
Yan Dolinsky
What if we knew what the future brings? Optimal investment for a frontrunner with price impact
Topic:
15:00
Hebrew University of Jerusalem, Israel
In this paper we study optimal investment when the investor can peek some time units into the future, but cannot fully take advantage of this knowledge because of quadratic transaction costs. In the Bachelier setting with exponential utility, we give an explicit solution to this control problem with intrinsically infinite-dimensional memory. This is made possible by solving the dual problem where we make use of the theory of Gaussian Volterra integral equations. Joint work with Peter Bank and Miklos Rasonyi.
November 2
dylan possamaï
A target approach to Stackelberg games
Topic:
15:00
ETH Zurich, Switzerland

In this paper, we provide a general approach to reformulating any continuous-time stochastic Stackelberg differential game under closed-loop strategies as a single-level optimisation problem with target constraints. More precisely, we consider a Stackelberg game in which the leader and the follower can both control the drift and the volatility of a stochastic output process, in order to maximise their respective expected utility. The aim is to characterise the Stackelberg equilibrium when the players adopt "closed-loop strategies", i.e. their decisions are based solely on the historical information of the output process, excluding especially any direct dependence on the underlying driving noise, often unobservable in real-world applications. We first show that, by considering the-second-order-backward stochastic differential equation associated with the continuation utility of the follower as a controlled state variable for the leader, the latter’s unconventional optimisation problem can be reformulated as a more standard stochastic control problem with stochastic target constraints. Thereafter, adapting the methodology developed by Soner and Touzi or Bouchard, Élie, and Imbert, the optimal strategies, as well as the corresponding value of the Stackelberg equilibrium, can be characterised through the solution of a well-specified system of Hamilton-Jacobi-Bellman equations. For a more comprehensive insight, we illustrate our approach through a simple example, facilitating both theoretical and numerical detailed comparisons with the solutions under different information structures studied in the literature. This is a joint work with Camilo Hernández, Nicolás Hernández Santibáñez, and Emma Hubert.
November 9
Olivier Gueant
An Overview of Market Making Models and Recent Applications in the Gold Market
Topic:
15:00
Université Paris 1 Panthéon-Sorbonne, France

Since the seminal work of Ho and Stoll, later refined by Avellaneda and Stoikov, algorithmic market-making models have evolved to incorporate increasingly realistic features such as trade sizes, complex price dynamics, tiering, externalization, and market impact. These models have been applied to a wide range of assets, from illiquid corporate bonds to highly liquid foreign exchange markets to cryptocurrencies (price-aware AMMs). This talk will provide an overview of the key developments of the past decade, highlighting both theoretical advancements and practical applications. It will then focus on recent applications of these models in the gold market by Barzykin, Bergault and I.

November 16
Martin Larsson
The numeraire e-variable and reverse information projection
Topic:
15:00
Carnegie Mellon University, USA

A recent approach to statistical inference is based on the concept of an e-variable: a nonnegative sample statistic whose expected value is at most one if a given null hypothesis is true. This approach has been found to produce strong statistical error bounds and high statistical power and is easily extendible to sequential, or online, settings. E-variables admit a natural interpretation as the payoff of a financial bet. In this talk I will discuss how classical ideas from mathematical finance, in particular the numeraire portfolio, enables an optimality theory for e-variables that significantly generalizes earlier results. Our results also lead to a duality theory which yields the so-called reverse information projection in complete generality. Our work showcases the power of financial methods in a setting where information-theoretic tools have traditionally been preferred. (Joint work with Aaditya Ramdas and Johannes Ruf.)

November 23
Cagin Ararat
Systemic Values-at-Risk: Computation and Convergence
Topic:
15:00
Department of Industrial Engineering Bilkent University, Turkey

We investigate the convergence properties of sample-average approximations (SAA) for set-valued systemic risk measures. We assume that the systemic risk measure is defined using a general aggregation function with some continuity properties and value-at-risk applied as a monetary risk measure. Our focus is on the theoretical convergence of its SAA under Wijsman and Hausdorff topologies for closed sets. After building the general theory, we provide an in-depth study of an important special case where the aggregation function is defined based on the Eisenberg-Noe network model. In this case, we provide mixed-integer programming formulations for calculating the SAA sets via their weighted-sum and norm-minimizing scalarizations. To demonstrate the applicability of our findings, we conduct a comprehensive sensitivity analysis by generating a financial network based on the preferential attachment model and modeling the economic disruptions via a Pareto distribution.
November 30
Yury A. Kutoyants
Adaptive Kalman-Bucy Filters. Low Noise Observations.
Topic:
15:00
Le Mans University, France

Several models of partially observed diffusion processes depending on unknown parameters are presented. The proposed algorithms of adaptive Kalman-Bucy filters and parameter estimators have recurrent structure and the questions of their asymptotic optimality are discussed. The properties of the filters and estimators are studied in the asymptotics of small noise. For some nonlinear partially observed systems the construction and properties of the corresponding extended adaptive Kalman filters are discussed too.
December 07
Miklós Rásonyi
Rate estimates for total variation distance
Topic:
15:00
HUN-REN Alfréd Rényi Institute of Mathematics, Hungary

For algorithms of machine learning, rate estimates are often provided in the Wasserstein metric or some variant thereof. There has been spectacular recent progress in the techniques for establishing such estimates.
At the same time, powerful methods have been developed in Malliavin calculus that enable to infer total variation convergence from weak convergence.
After presenting an overview of the developments above, we show some new results on total variation convergence that do not rely on Malliavin calculus nevertheless they are applicable to various stochastic systems.
December 14
Agostino Capponi
A Continuous Time Framework for Sequential Goal-Based Wealth Management
Topic:
15:00
Columbia University, USA
We develop a continuous time framework for sequential goals-based wealth management.
A stochastic factor process drives asset price dynamics as well as the client’s goal amount and income. We prove the weak dynamic programming principle for the value function of our control problem, which we show to be the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We develop an equivalent and computationally efficient representation of the Hamiltonian, which yields the optimal portfolio within a factor-dependent opportunity set defined by the maximum and minimum variance hypersurfaces. Our analysis shows that it is optimal to fund an expiring goal up to the level where the marginal benefit of additional fundedness is exceeded by the opportunity cost of diverting wealth from future goals. An all-or-nothing investor is more risk averse towards an approaching goal deadline if she is well funded, but more risk seeking if she is not on track with upcoming goals, compared to an investor with flexible goals.
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