International Seminar on SDEs and Related Topics
 This online seminar takes place every four weeks on Friday at
12:30 UTC  11:30 UTC during European daylight saving times (until Oct 29) 

12:30 noon  1:30 pm  2:30 pm  8:30 pm (7:30 pm until Oct 29) 

London  Berlin, Paris  Helsinki  Beijing 
Zoom link Meeting ID: 618 9100 7917
No registration required. To get an email reminder before each event write to sdeseminar[at]jyu.fi.
Organisers
 Stefan Ankirchner (FSU Jena, Germany)
 Christian Bender (Saarland University, Germany)
 Rainer Buckdahn (Universite de Bretagne Occidentale, France)
 Dan Crisan (Imperial College London, UK)
 Christel Geiss (University of Jyväskylä, Finland)
 Stefan Geiss (University of Jyväskylä, Finland)
 Céline Labart (Université Savoie MontBlanc, France)
 Juan Li (Shandong University, China)
 Andreas Neuenkirch (University of Mannheim, Germany)
 Shige Peng (Shandong University, China)
 Adrien Richou (University of Bordeaux, France)
Schedule 2023 Autumn
Oct 20, 2023
Theodor Sturm (University of Bonn, Germany)Nov 17, 2023
Dec 15, 2023
Schedule 2024 Spring
Jan 12, 2024
Xiaolu Tan (The Chinese University of Hong Kong)Jan 12, 2024
Feb 09, 2024
Mar 08, 2024
Apr 05, 2024
May 03, 2024
May 31, 2024
Contact and links

Stefan Geiss: stefan.geiss[at]jyu.fi or stefanfriedrich.geiss[at]gmail.com

The online seminar is hosted by the Department of Mathematics
and Statistics (University of Jyväskylä) and supported by the journal
Probability, Uncertainty and Quantitative Risk.
Schedule 2023 Spring
Jan 13, 2023
Terry Lyons Poster:(University of Oxford, United Kingdom)
Abstract: We will survey the growing contribution of rough path based ideas, such as the signature of a path, to the understanding real world streamed data.
A more comprehensive account can be found at www.DataSig.ac.uk/papers
Jan 27, 2023
JeanFrançois Chassagneux Poster: Slides(Université Paris Cité, France)
Abstract: We introduce a class of 'weak hedging problems’ which contains as special examples the quantile hedging problem (Föllmer & Leukert 1999) and PnL (Profit and Loss) matching problem (introduced in Bouchard & Vu 2012). We show that they can generally be rewritten as a kind of Monge transport problem. Using this observation, we introduce a Kantorovich version of the problem and, in some cases, we are able to prove a dual formulation. This allows us to design numerical methods based on SGD (stochastic gradient descent} algorithms to compute the weak hedging price.
Feb 10, 2023
Martin Hairer Poster: Slides(EPFL, Switzerland and Imperial College London, United Kingdom)
Abstract: We report on recent progress on the problem of building a stochastic process that admits the hypothetical YangMills measure as its invariant measure. One interesting feature of our construction is that it preserves gaugecovariance in the limit even though it is broken by our UV regularisation. This is based on joint work with Ajay Chandra, Ilya Chevyrev, and Hao Shen.
Feb 24, 2023
Rainer Buckdahn Poster: Slides(Universite de Bretagne Occidentale, France)
Abstract: The talk is devoted to the study of Pontryagin's stochastic maximum principle for a meanfield optimal control problem under Peng's sublinear Gexpectation. The dynamics of the controlled state process is given by a SDE driven by a GBrownian motion, whose coefficients depend on the control, the controlled state process but also on its law under the Gexpectation. Also the associated cost functional is of meanfield type. Under the assumption of a convex control state space we study Pontryagin's stochastic maximum principle, which gives a necessary optimality condition for the control process. Under additional convexity assumptions on the Hamiltonian it is shown that this necessary condition is also a sufficient one. The main difficulty which we had to overcome in our work consists in the differentiation of the Gexpectation of parametrised random variables. As particularly delicate turns out to handle the Gexpectation of a function of the controlled state process inside the running cost of the cost functional. For this we had to study a measurable selection theorem for setvalued functions whose values are subsets of the representing set of probability measures for the Gexpectation. The talk is based on a recent joint work with Juan Li and Bowen He (Shandong University, Weihai, China).
Mar 10, 2023
Christa Cuchiero Poster: Slides Video (expires September 2023)(University of Vienna, Austria)
Abstract: A plethora of stochastic models used in diverse areas, like mathematical finance, population genetics or physics, stems from the class of affine and polynomial processes. The history of these processes is on the one hand closely connected with the important concept of tractability, that is a substantial reduction of computational efforts due to special structural features, and on the other hand with a unifying framework for a large number of probabilistic models. One early instance in the literature where this unifying affine and polynomial point of view can be applied is Lévy's stochastic area formula. Starting from this example, we present a guided tour through the main properties and results as well as classical and recent applications, which culminates in the surprising insight that infinite dimensional affine and polynomial processes are actually close to generic stochastic processes.
Mar 24, 2023
Antoine Lejay Poster: Slides Video (expires September 2023)(Université de Lorraine, France)
Abstract: The skew Brownian motion (SBM) is a useful process to deal with diffusion
in media presenting some interface. Actually, it behaves like a Brownian
motion away from 0. When reaching 0, its behavior is ruled by a single
parameter θ ∈ [1,1] which affects the tendency of the particle to go
upward or downward. When θ =±1, the SBM is a reflected Brownian motion
while for θ = 0, the SBM is a Brownian motion.
In this talk, we discuss the main properties of the Maximum
Likelihood Estimator (MLE), which is consistent and asymptotically mixed
normal with a non standard rate of 1/4. In particular, we study its
behavior with respect to the value of the true parameter θ and we give an
infinite series expansion of the MLE thanks to a recent asymptotic
inverse function theorem.
From joint works with E. Mordecki, S. Torres, and S. Mazzonetto.
Mar 31, 2023 11:30 UTC European daylight saving times have started
Andreas Neuenkirch Poster: Slides Video (expires September 2023)(University on Mannheim, Germany)
Abstract: The CIR process is the prototype stochastic differential equation (SDE) for the class of square root diffusions. These equations have widespread applications, in particular in finance, biology and chemistry. Moreover, since the diffusion coefficient contains a square root and is not Lipschitz continuous, the CIR process is also the prototype example for an SDE whose coefficients do not satisfy the socalled standard assumptions for numerical analysis. Due to these reasons, the approximation of the CIR process has attracted a lot of attention in the last 20 years. In this talk, I will give a stateoftheart summary and will present some of the latest developments for the strong approximation of the CIR process.
Apr 14, 2023
Konstantinos Dareiotis Poster: Slides Video (expires September 2023)(University of Leeds, United Kingdom)
Abstract: In this talk, we consider differential equations perturbed by multiplicative fractional Brownian noise.
Depending on the value of the Hurst parameter H, the resulting equation is pathwise viewed as an ordinary ( H>1),
Young ( H ∈ (1/2, 1)) or rough (H ∈ (1/3, 1/2)) differential equation. In all three regimes we show regularisation
by noise phenomena by proving the strongest kind of wellposedness for equations with irregular drifts: strong existence and
pathbypath uniqueness. In the Young and smooth regime H>1/2 the condition on the drift coefficient is optimal in the sense that
it agrees with the one known for the additive case. In the rough regime H ∈ (1/3,1/2) we assume positive but arbitrarily small
drift regularity for strong wellposedness, while for distributional drift we obtain weak existence.
This is a joint work with Máté Gerencsér.
Apr 21, 2023
René Schilling Poster: Slides(Technische Universität Dresden, Germany)
Abstract: We discuss a proof of Liouville's theorem (all bounded harmonic functions are constant) for a class of Fourier multiplier
operators; this class includes Lévyoperators. We also show extensions where the harmonic functions are allowed to grow.
This is joint work with D. Berger (TU Dresden) und E. Shargorodsky (King's College, London).
May 05, 2023
Máté Gerencsér Poster: Slides Video (expires October 2023)(TU Wien, Austria)
Abstract: We consider integrals of expressions of the form f(X_{t }), where X is a stochastic process, and f is only a distribution. We overview some recent results on defining such integrals and discuss a variety of their applications in regularisation by noise for stochastic differential equations such as existence, uniqueness, stability, regularity, and approximation properties.
May 19, 2023
Christoph Reisinger Poster: Slides Video (expires October 2023)(University of Oxford, United Kingdom)
Abstract: Fully coupled McKean–Vlasov forwardbackward stochastic differential equations (MVFBSDEs) arise naturally from large
population optimization problems. Judging the quality of given numerical solutions for MVFBSDEs, which usually require Picard iterations
and approximations of nested conditional expectations, is typically difficult. In this talk, we propose an a posteriori error estimator to quantify
the L_{2}approximation error of an arbitrarily generated approximation on a time grid. We establish that the error estimator is
equivalent to the global approximation error between the given numerical solution and the solution of a forward Euler discretized MVFBSDE.
We further demonstrate that, for sufficiently fine time grids, the accuracy of numerical solutions for solving the continuous MVFBSDE can also
be measured by the error estimator. The error estimates justify the use of residualbased algorithms for solving MVFBSDEs. Numerical
experiments for MVFBSDEs arising from mean field control and games confirm the effectiveness and practical applicability of the error estimator.
Schedule 2022 Autumn
Oct 28, 2022 @ 12:30 UTC (3:30pm Helsinki), one hour later than usual!
David Nualart Poster: Slides(Kansas University):
Abstract: In this talk we will present some recent results on first and second order fluctuations of
a class of additive functionals of a fractional Brownian motion. Two different behaviors arise depending
of the value of the Hurst parameter H. When the Hurst parameter is larger or equal than 1/3, the limit in
distribution turns out to be an independent Brownian motion subordinated to the local time. When H is less than 1/3, the limit
is a constant multiple of the derivative of the local time.
Nov 11, 2022 @ 12:30 UTC (3:30pm Helsinki), one hour later than usual!
Yaozhong Hu Poster: Slides(University of Alberta, Canada)
Abstract: This talk wil be concerned with the existence and uniqueness of a strong solution to the onedimensional stochastic wave equation
assuming , where
is a mean zero
Gaussian noise which is white in time and fractional in space
with Hurst parameter .
The idea is to decompose the simple onedimensional Green kernel into some more complicated ones which make the thing work
This talk is based on joint work with Shuhui Liu and Xiong Wang.
Nov 25, 2022
Ying Jiao Poster: Slides(Université Claude Bernard Lyon 1, France)
Abstract: As the world is facing global climate changes, the Intergovernmental Panel on Climate Change (IPCC) has set the idealized carbonneutral
scenario around 2050. In the meantime, many carbon reduction scenarios, known as Shared Socioeconomic Pathways (SSPs) have been
proposed in the literature. We aim to investigates the impact of transition risk on a firm’s lowcarbon production. On the one hand, we consider a firm that
searches to optimize its emission level under the double objectives of maximizing its production profit and respecting the emission mitigation scenarios.
Solving the penalized optimization problem provides the optimal emission according to a given SSPs benchmark. On the other hand, such transitions affect the
firm’s credit risk. We model the default time by using the structural default approach and are particularly concerned with how the adopted strategies following
different SSPs scenarios may influence the firm’s default probability. Finally we discuss possible extensions of the model to a large portfolio of climate concerned
firms and propose numerical methods to compute the cumulative losses.
This is joint work with Florian Bourgey and Emmanuel Gobet.
Dec 09, 2022
Yuri Kabanov Poster: Slides(University of FrancheComté, Besançon, France)
Abstract: In the classical collective risk theory it is usually assumed that the capital reserve of a company is placed in a bank account paying zero interest. In the recent three decades the theory was extended to cover a more realistic situation where the reserve is invested, fully or partially, in a risky asset (e.g., in a portfolio evolving as a market index). This natural generalization generates a huge variety of new ruin problems which can be considered as the exit problem for a semimartingale OrnsteinUhlenbeck process. Roughly speaking, each “classical” ruin problem, e.g., a version of the CramerLundberg model (for the nonlife insurance, for the annuity payments etc.) can be combined with a model of price of the risky security (geometric Brownian motion, geometric Lévy process, various models with stochastic volatilities, etc.). In the talk we present new asymptotic results for the ruin probabilities, in particular, for the Sparre Andersen type models with risky investments having the geometric Lévy dynamics and for CramérLundberg type models with investments in a risky asset with a regime switching price.
Schedule 2022 Spring
Jan 14, 2022
Arturo KohatsuHiga Poster: Slides(Ritsumeikan University, Kusatsu):
Abstract: Recently, I have been interested in how acceleration of approximation methods can be used in order to derive theoretical properties about the laws of their
limits. In most situations, numerical approximations do not have good properties to assure that their properties transfer to their limit laws. In this talk, I will give two examples
with different acceleration techniques. In the first, we will consider a jump driven sde with purely atomic Lévy measure with infinite activity. We will apply the AsmussenRosinki
approximation for small jumps of infinite activity Lévy processes and argue that the use of this approximation in order to derive properties of the limits is not possible. Instead,
we will combine this method with the moment method in order to improve the approximation and finally obtain results on the regularity of the laws of stochastic differential
equations with jumps. This approach is interesting as it only uses Malliavin calculus with respect to the Brownian motion associated to the AsmussenRosinki approach. This
is joint work with V. Bally and L. Caramellino.
In a second part, we will discuss how to obtain optimal upper bounds for the joint density of a stable process and its supremum using Multilevel Monte Carlo method
techniques. We build an adhoc Malliavin Calculus method based on the ChamberMallowsStruck/Kanter simulation approach for stable increments.
This is joint work with Jorge GonzalesCazares and Alex Mijatovic.
Jan 28, 2022
Denis Talay Poster: Slides(INRIA & École Polytechnique):
First hitting time Laplace transforms of solutions of SDEs are Lipschitz continuous in the Hurst parameter of the driving fractional Brownian noise
Abstract: Sensitivity analysis w.r.t. the longrange/memory noise parameter for
probability distributions of functionals of solutions to stochastic
differential equations is an important stochastic modeling issue in many
applications.
In this talk we consider solutions {X_{t}^{H}}_{ t ∈ R+ } to stochastic
differential equations driven
by fractional Brownian motions. We examine Laplace transforms of
functionals which are
irregular with regard to Malliavin calculus, namely, first passage times
of X^{ H} at a given threshold.
We will present parts of the machinery necessary to prove the Lipschitz
continuity w.r.t. H around the value ½. This result
implies that, for applications where first hitting times are a crucial
information,
the Markov Brownian model is a good proxy model as long as the Hurst
parameter remains close to ½.
This is joint work with Alexandre Richard (Ecole CentraleSupelec, France).
Feb 11, 2022
Mireille Bossy Poster: Slides(INRIA, Université Côte d'Azur, France):
Weak convergence rate approximation for some SDEs with superlinear growth coefficients
Abstract: In this talk, I would like to discuss around the approximation of the solution of the following class of 1DSDEs
dX_{t } = b(X_{t })dt + σ X_{t }^{α} dW_{t }, X_{0 }=x>0,
when α >1.
I will first present some motivating examples where such SDEs arise in modelling approaches, as longtime limit approximation of McKean SDEs
of CIRtype for the amplitude of local wind fluctuations. Of course, a priori knowledge on such SDEs about the
conditions on the coefficients ensuring wellposedness and some control on the moments is required, not only for their use as models
but also to study the convergence of timeintegration schemes.
Here, we focus on the weak convergence rate. First, a set of conditions for the C^{1,4} regularity of the FeynmanKac formula
is proposed using stochastic tools. While direct derivation of the FeynmanKac formula requires some smoothness for the drift b, we show how
to avoid the costly control of the moments of the successive derivatives of the flow process by using a change of measure technique, allowing b to be
taken superlinear too. We then introduce an exponential scheme for the time integration of the SDE, which reproduces well the control of the moments of the
exact process and for which we prove a convergence rate of order one.
This talk is based on two recent papers with Kerlyns Martínez Rodríguez (University of Vaparaíso) and Jean Francois Jabir (HSE University Moscow).
Feb 25, 2022 @ 12:30 UTC, one hour later than usual!
Xin Guo Poster: Slides(University of California at Berkeley):
Generative adversarial models: an analytical perspective
Abstract: Recently, the popularity and successes of Generative Adversarial Networks (GANs) in computer vision and image generation have attracted intense
attention from the mathematical finance community. GANs have since then been applied to financial data generation and lately shown capable of computing
solutions for high dimensional meanfield games.
In this talk, we will discuss the connection of GANs with optimal transport, game structure of GANs in an SDE framework, and present our latest work
on the stochastic control approach for the stability of GANs training.
Based on joint work with Haoyang Cao of Ecole Polytechnique, and Othmane Mounjid of UC Berkeley.
Mar 11, 2022
Lukasz Szpruch Poster: Slides(University of Edinburgh):
Gradient Flows for Regularized Stochastic Control Problems
Abstract: We study stochastic control problems regularized by the relative entropy, where the action
space is the space of measures. By exploiting the Pontryagin optimality principle, we identify a suitable metric
space on which we construct the gradient flow for the measurevalued control process along which the cost functional
is guaranteed to decrease.
It is shown that under appropriate conditions, this gradient flow has an invariant measure
which is the optimal control for the regularized stochastic control problem. If the problem we work with is sufficiently
convex, the gradient flow converges exponentially fast. Furthermore, the optimal measure valued control admits Bayesian
interpretation which means that one can incorporate prior knowledge when solving the stochastic control problem.
This work
is motivated by a desire to extend the theoretical underpinning for the convergence of stochastic gradient type algorithms widely
used in the reinforcement learning community to solve control problems.
Mar 25, 2022 @ 12:30 UTC, one hour later than usual!
Jianfeng Zhang Poster: Slides(University of Southern California):
Propagation of Monotonicity for Mean Field Game Master Equations
Abstract: It is well known in the mean field game literature that a certain monotonicity condition is crucial for the uniqueness of mean field equilibria and for the wellposedness of the associated master equation. One interesting observation is that the propagation of the monotonicity (either in LasryLions sense or in displacement sense) of the value function plays the key role here. We shall introduce a method to find conditions on the coefficients which ensure that any solution of the master equation will maintain the monotonicity property. This method also allows us to consider antimonotonicity and obtain the desired wellposedness provided the coefficients are sufficient antimonotone in appropriate sense. We finally extend our results to mean field games of controls. The talk is based on a joint work with GangboMeszarosMou and two joint works with Mou.
Apr 08, 2022
Krzysztof Bogdan Poster: Slides(Wrocław University of Science and Technology):
Selfsimilar solution for Hardy operator
Abstract: We will discuss the largetime asymptotics of solutions to the heat equation for the fractional Laplacian with added subcritical or even critical Hardytype potential. The asymptotics is governed by a selfsimilar solution of the equation, obtained as a normalized limit at the origin of the kernel of the corresponding FeynmanKac semigroup. This will be our focus. Interestingly, an OrnsteinUhlenbeck semigroup turns out to be an important tool for the analysis. The paper is on arXiv and it is joint work with P. Kim (Seoul), T. Jakubowski, and D. Pilarczyk (Wrocław).
Apr 22, 2022
Étienne Pardoux Poster: Slides(AixMarseille Université):
Epidemic models with varying infectivity and varying susceptibility
Abstract: Almost a century ago, Kermack and McKendrick suggested to take into account the fact that the infectivity of infectious individuals vary with the time
elapsed since their infection, the duration of the infectious period can have a very general distribution, and in case of loss of immunity, it is progressive.
Unfortunately, almost all the literature on mathematical models of epidemics concentrate on simpler ODE models, which are the law of large numbers limits, as the size
of the population tends to infinity, of finite population stochastic Markovian models.
We consider the models suggested by Kermack and McKendrick, and obtain those models (or in one case a generalization of their model), which are convolution type
equations, as law of large numbers limits of general non Markov stochastic finite population models. We also derive some associated Central Limit Theorems. Our
approach involves the study of a class of mean field Poisson driven SDEs.
This is joint work with Guodong Pang, Raphaël Forien and Arsene Brice ZotsaNgoufack.
May 06, 2022
François Delarue Poster: Slides(Université Côte d’Azur, Nice):
Weak solutions to the master equation of potential mean field games
Abstract: The talk is motivated by the theory of mean field games, initiated by Lasry and Lions and by Caines, Huang and Malhamé. The very purpose of it is to address the socalled master equation, which describes the value of the game, when equilibria may not be unique. In order to do so, we restrict ourselves to a class of mean field games that coincide with the first order condition of an optimal control problem set over McKeanVlasov dynamics. Such games are called potential. We then introduce a notion of weak solution to the master equation and prove that existence and uniqueness hold under quite general assumptions. The key point is to interpret the master equation in a conservative sense and then to adapt to the infinite dimensional setting earlier arguments for hyperbolic systems deriving from a HamiltonJacobiBellman equation. This a joint work with Alekos Cecchin (Padova, Italy), see arXiv 2204.04315
May 20, 2022
Ying Hu Poster: Slides Video (expires November 2022)(Université de Rennes 1, CNRS):
Abstract: The aim of this talk is to give some wellposedness results for scalar valued Backward Stochastic Differential Equations (BSDEs) when the generator has a sublinear growth, superlinear growth, subquadratic growth in the second variable. In each of these cases, we give some precise conditions for terminal random variables to guarantee the existence and uniqueness of the solution. Joint works with Shengjun Fan and Shanjian Tang.
Schedule 2021
Oct 29, 2021
Peter Friz (TU Berlin and WIAS Berlin): Poster:Abstract: A hybrid theory of rough stochastic analysis is built that seamlessly combines the advantages of both Itô's stochastic  and Lyons' rough differential equations. A major role is played by a new stochastic variant of controlled rough paths spaces, with norms that reflect some generalized stochastic sewing lemma, and which may prove useful whenever rough paths and Itô integration meet. We will mentioned several applications. Joint work with Antoine Hocquet and Khoa Lê (both TU Berlin).
Nov 12, 2021
Xunyu Zhou (Columbia University, New York): Poster:A Martingale Lens
Abstract: We propose a unified framework to study policy evaluation (PE) and the associated temporal difference (TD) methods for reinforcement learning in continuous time and space. Mathematically, PE is to devise a datadriven FeynmanKac formula without knowing any coefficients of a PDE. We show that this problem is equivalent to maintaining the martingale condition of a process. From this perspective, we present two methods for designing PE algorithms. The first one, using a "martingale loss function", interprets the classical gradient MonteCarlo algorithm. The second method is based on a system of equations called the "martingale orthogonality conditions". Solving these equations in different ways recovers various classical TD algorithms, such as TD, LSTD, and GTD. We apply these results to option pricing and portfolio selection. This is joint work with Yanwei Jia.
Nov 26, 2021
Michael Scheutzow Poster: Slides(Technische Universität Berlin):
Abstract: We provide an introduction to generalized couplings and present a recent result [contained in S.: Couplings,
generalized couplings and uniqueness of invariant measures. ECP, 2020] which says that the existence of a generalized coupling
for a Markov chain implies uniqueness of an invariant probability measure even if
the state space is just a metric space without requiring separability or completeness as in previous works. The proof turns out to be
rather elementary. We show how this result can be applied to show uniqueness of an invariant measure for
the solution process of a stochastic functional differential equation (SFDE) and we show how generalized couplings can be employed
to show weak uniqueness of solutions of an SFDE with Hölder continuous coefficients.
Parts of the talk are based on joint work with Alex Kulik (Wroclaw).
Dec 10, 2021
Nizar Touzi Poster: Slides(CMAP & Polytechnique Paris):
Abstract: The problem of optimal planning was introduced by P.L. Lions in the context of a mean field game, by fixing a target distribution in the FockerPlanck equation and relaxing the boundary condition in the HJB equation. We analyze an extension of this problem to the pathdependent setting which has remarkable connections with optimal transport and optimal incentive theory in economics. We provide a general characterization of mean field optimal planning solutions, and we solve explicitly the minimum entropy optimal planning problem.