Conference on Stochastic Analysis, Stochastic Dynamical Systems and Stochastic Finance

Shanghai Jiao Tong University, School of Mathematical Sciences

In recent years, fundamental advances have been made in different areas of stochastics – from stochastic partial differential equations, ergodic theory to manifold valued stochastic differential equations as well as non-dominated models and transport methods in financial mathematics.

The School of Mathematical Sciences takes this opportunity to bring together those working on different areas of research related to stochastics to exchange on common issues, their respective approaches, new results and new research directions. Alongside the presentation of each speaker, this conference also intends to facilitate research discussions and interactions in a stimulating and convivial atmosphere.

Attendance is open to any interested participants up to capacity. Therefore, would you like to attend, we kindly ask you to register by sending a mail to Hu Jie.

School of Mathematical Sciences
Shanghai Jiao Tong University
Dongchuan Road 800, Shanghai
from September 6th to 8th
706, School of Mathematical Sciences Building, Shanghai Jiao Tong University, Minhang Campus
Program and Abstracts:
For Title and abstracts, see on the dedicated program page.


Name Institution
Daniel Bartl University of Vienna
Mathias Beiglboeck University of Vienna
Zhenqing Chen Washington University
Zhao Dong Chinese Academy of Sciences
Kai Du Fudan University
Paolo Guasoni Dublin City University
Peter Imkeller Humboldt University Berlin
Michael Kupper Konstanz University
Zhenghu Li Beijing Normal University
Zhenya Liu Renmin University
Michael Roeckner Bielefeld University
Shanjian Tang Fudan University
Nizar Touzi Polytechnic University Paris
Martin Schweizer ETH Zurich
Fengyu Wang Tianjin University
Guojing Wang Soochow University
Bo Wu Fudan University
Hao Xing Boston University
Litan Yan Donghua University
Huaizhong Zhao Loughborough University
Xicheng Zhang Wuhan University

Members of the Organizing Committee:


Contact us:

For any additional information or registration, do not hesitate to contact Hu Jie.

Titles and Abstracts

Daniel Bartl

Adapted Wasserstein distance in finance and quantitative estimation
What notion of closeness for probabilistic models guarantees closeness of utility maximization/optimal stopping/…? We argue that while Wasserstein distances are not suitable for this task, there is a modified offspring (the adapted Wasserstein distance) that is suitable. We establish Lipschitz continuity w.r.t. adapted Wasserstein distance and show sharpness already in a simple Brownian setting. The picture is completed by providing statistical estimators and studying their convergence rate in adapted Wasserstein distance. Joint with J.Backhoff, M.Beiglboeck, M.Eder, J.Wiesel

Mathias Beiglboeck

All adapted topologies are equal
Several researchers have introduced topological structures on the set of laws of stochastic processes. A unifying goal of these authors is to strengthen the usual weak topology in order to adequately capture the temporal structure of stochastic processes. We find that all of these seemingly independent approaches define the same topology in finite discrete time. Moreover, we explain how optimal transport theory can be used obtain a compatible metric that is both tractable and highly relevant for a number of questions in mathematical finance.
(J. Backhoff, D. Bartl, D. Lacker, M. Eder)

Zhenqing Chen

Stability of heat kernel estimates and parabolic Harnack inequalities for symmetric Dirichlet forms
In this talk, I will present recent progress in the study of heat kernels and parabolic Harnack inequalities for symmetric Markov processes that have both diffusive and jumping parts on general metric measure spaces. Under general volume doubling condition and some mild assumptions on the scaling functions, we establish stability results for two-sided estimates for heat kernels in terms of the jumping kernels, the generalized capacity inequalities, and Poincare inequalities. Stable characterizations of the associated parabolic Harnack inequalities will also be given. Our results hold on spaces even when the underlying spaces have walk dimensions are larger than 2. Joint work with Takashi Kumagai and Jian Wang.

Kai Du

On stochastic parabolicity conditions for SPDEs
Stochastic parabolicity conditions are a class of structural conditions for stochastic PDEs to ensure wellposedness of the equations, which, in contrast to classical parabolicity conditions, involve additionally the coefficients of leading terms in the stochastic integral part. The proper form of stochastic parabolicity condition for weak solutions of SPDEs were found long ago and also valid in constructing L^p theory and Schauder theory of SPDEs of second-order. However, things may dramatically change for other problems, such as complex-valued SPDEs, systems of SPDEs, and higher-order SPDEs. More specifically, those conditions for weak solutions ($L^2$ theory) is not sufficient to ensure $L^p$-integrability of solutions, and certain modified conditions are required in those cases. The talk will present some p-dependent parabolicity conditions imposed on systems of SPDEs and on higher-order SPDEs, which ensure us to construct stochastic Schauder theory for those equations. Examples are discussed for necessity of modified parabolicity conditions and sharpness of our modifications. The talk is based on joint works with Jiakun Liu and Fu Zhang and with Yuxing Wang.

Zhao Dong

Large deviation principles for first-order scalar conservation laws with stochastic forcing
In this paper, we established the Freidlin-Wentzell type large deviation principles for first-order scalar conservation laws perturbed by small multiplicative noise. Due to the lack of the viscous terms in the stochastic equations, the kinetic solution to the Cauchy problem for Abstract 15 these first-order conservation laws is studied. Then, based on the well-posedness of the kinetic solutions, we show that the large deviations holds by utilising the weak convergence approach. This is joint work with Wu Jiang Lun, Zhang Rang Rang, Zhang Tu sheng.

Paolo Guasoni

Asset Prices in Segmented and Integrated Markets
This paper evaluates the effect of market integration on prices and welfare, in a model where two Lucas trees grow in separate regions with similar investors. We find equilibrium asset price dynamics and welfare both in segmentation, when each region holds its own asset and consumes its dividend, and in integration, when both regions trade both assets and consume both dividends. Integration always increases welfare. Asset prices may increase or decrease, depending on the time of integration, but decrease on average. Correlation in assets’ returns is zero or negative before integration, but significantly positive afterwards, explaining some effects commonly associated with financialization.

Peter Imkeller

On the geometry of some rough Weierstrass curves: SBR measure and local time
We investigate geometric properties of Weierstrass curves with one or two components, representing series based on trigonometric functions. They are Hoelder continuous, and not (para-)controlled with respect to each other. They can be embedded into a smooth dynamical system, where their graph emerges as a pullback attractor. Each one-dimensional component of the curve may also be seen in the light of this dynamical system. It turns out that occupation measures and Sinai-Bowen-Ruelle (SBR) measures on its stable manifold are dual to each other, via time reversal. A suitable version of approximate self similarity for deterministic functions yields approximate scaling properties for the measures. As a consequence, absolute continuity of the SBR measure is obtained, as well as the existence of a local time. The link between rough Weierstrass curves and smooth dynamical systems can be generalized considerably. Applications to regularization of singular ODE by rough (Weierstrass type) signals are on our agenda. This is joint work with G. dos Reis (U Edinburgh) and O. Pamen (U Liverpool and AIMS Ghana).

Michael Kupper

Nonlinear expectations under Wasserstein uncertainty and convex semigroups
We provide general results for convex semigroups, in particular uniqueness in terms of the generator. As an example, we consider scaling limits of Levy processes under distributional uncertainty with respect to (martingale) transport distances. The talk is based on joint works with Daniel Bartl, Robert Denk and Max Nendel.

Zhenghu Li

Ergodicities and exponential ergodicities of branching processes with immigration
Under natural assumptions, we prove the ergodicities and exponential ergodicities in Wasserstein and total variation distances of Dawson-Watanabe superprocesses without or with immigration. Those processes are infinite-dimensional generalizations of the well-known continuous-state branching processes, which are also known as Cox-Ingersoll-Ross type models and have played important roles in the study of mathematical finance. The strong Feller property of the processes in the total variation distance is derived as a by-product. The key of our approach is a set of estimates for the variations of the transition probabilities. The estimates in Wasserstein distance are derived from an upper bound of the kernels induced by the first moment of the superprocess. Those in total variation distance are based on a comparison of the cumulant semigroup of the superprocess with that of a continuous-state branching process. The results improve and extend considerably those of Stannat (2003a, 2003b) and Friesen (2019+). We also show a connection between the ergodicities of the associated immigration superprocesses and decomposable distributions.

Zhenya Liu

The Optimal Selling Price with Endogenous Maximum Drawdown
We propose the optimal endogenous drawdown in an investor’s objective function, which is concerned about the profit and the loss. The endogenous drawdown is the solution of an optimal stopping problem, including the maximum running process. The optimal selling price with endogenous maximum drawdown is determined by the weights on profit and loss of investor’s objective function and the stock price process, e.g., Geometric Brownian Motion. With the comparison of the stock markets in China and the United States, investors in China’s stock markets have a significant attitudes to suffer a more massive endogenous drawdown.

Michael Roeckner

Strong dissipativity of generalized time-fractional derivatives and quasi-linear (stochastic) partial differential equations
In this talk we shall identify generalized time-fractional derivatives as generators of $C_0$-operator semigroups and prove their strong dissipativity on Gelfand triples of properly in time weighted $L^2$-path spaces. In particular, the classical Caputo derivative is included as a special case. As a consequence one obtains the existence and uniqueness of solutions to evolution equations on Gelfand triples with generalized time-fractional derivatives. These equations are of type \begin{equation*} \frac{d}{dt} (k * u)(t) + A(t, u(t)) = f(t), \quad 0 < t < T \end{equation*} with (in general nonlinear) operators $A(t,\cdot)$ satisfying general weak monotonicity conditions. Here $k$ is a non-increasing locally Lebesgue-integrable nonnegative function on $\mathbb{R}_+$ with $\lim_{s \to \infty}k(s)=0$. Analogous results for the case, where $f$ is replaced by a time-fractional additive noise, are obtained as well. Applications include generalized time-fractional quasi-linear (stochastic) partial differential equations. In particular, time-fractional (stochastic) porous medium and fast diffusion equations with ordinary or fractional Laplace operators or the time-fractional (stochastic) $p$-Laplace equation are covered.

Shanjian Tang

Stochastic LQ and Associated Riccati equation of PDEs Driven by State- and Control-Dependent White Noise
The optimal stochastic control problem with a quadratic cost functional for linear partial differential equations (PDEs) driven by a state- and control-dependent white noise is formulated and studied. Both finite- and infinite-time horizons are considered. The multi- plicative white noise dynamics of the system give rise to a new phenomenon of singularity to the associated Riccati equation and even to the Lyapunov equation. Well-posedness of both Riccati equation and Lyapunov equation are obtained for the first time. The linear feedback coefficient of the optimal control turns out to be singular and expressed in terms of the solution of the associated Riccati equation. The null controllability is shown to be equivalent to the existence of the solution to Riccati equation with the singular terminal value. Finally, the controlled Anderson model is addressed as an illustrating example. This is a joint work with Ying Hu.

Nizar Touzi

Continuous-time Principal-Agent problem and optimal planning
Motivated by the approach introduced by Sanninkov to solve principal-agent problems, we provide a solution approach which allows to address a wider range of problems. The key argument uses a representation result from the theory of backward stochastic differential equations. This methodology extends to the mean field game version of the problem, and provides a connexion with the P.-L. Lions optimal planning problem.

Martin Schweizer

Absence of arbitrage without a numeraire
We propose a new approach to defining and characterising absence of arbitrage in a way which does not depend on an a priori chosen numeraire. In spirit, this is similar to Kabanov’s approach to modelling markets with transaction costs. The key idea is that the comparison basis for a profit is no longer wealth, which is numeraire-dependent, but a chosen reference portfolio, which is in units of assets and hence numeraire-independent. We provide dual characterisations in terms of discounting and martingale properties, and we also discuss to which extent the approach and results depend on the reference portfolio. This is based on joint work with Daniel Balint.

Fengyu Wang

Asymptotic Formulas for Empirical Measures of (Reflecting) Diffusion Processes on Riemannian Manifolds
Consider the (reflecting) diffusion process generated by on a compact connected Riemannian manifold possibly with a boundary , where is such that is a probability measure. Then the empirical measures \begin{equation}\mu_{t}:=\frac{1}{t}\int_0^t \delta_{X_s} d s,\ \ t>0 \end{equation} satisfy \begin{equation} \lim_{t\to \infty} \big\{t \mathbb{E}^x \left[W_2(\mu_{t},\mu)^2\right]\big\}= \sum_{i=1}^\infty\frac{2}{\lambda_i^2} \text{ uniformly in } x\in M,\end{equation} where $\mathbb{E}^x$ is the expectation for the diffusion process starting at point $x$, $W_2$ is the $L^2$-Warsserstein distance induced by the Riemannian metric, and the limit is finite if and only if $d\leq 3$. Moreover, when $d\geq 4$ the main order of $\mathbb{E}^x[W_2(\mu_{t},\mu)^2]$ is $t^{- 2/(d-2)}$ as $t\to\infty$.

Guojing Wang

Pricing basket CDS with some certain dependent default risk
In this talk, we consider the basket CDS spreads under some reduced form credit risk models. We explain how the default intensity of a defaultable firm can be defined as the intensity of a point process. The dependence of default can be described by the dependence among the default intensity processes of defaultable firms with contagion (interacting), the conditional independence, the common shock and the regime switching. We derive some closed form joint distributions for default times and present some analytic pricing formulas for the basket CDS spreads under the proposed credit risk models.

Bo Wu

Functional inequalities on general Riemannian loop spaces
In this talk, based on a family of cut-off processes, we provide a method for obtaining asymptotic and short time gradient and Hessian estimates for logarithmic heat kernel on a general complete Riemannian manifold. These together with heat kernel estimates and asymptotic comparison estimates yield a reliable way to obtain different gradient and Hessian estimates on general non-compact Riemannian manifolds. Even for the compact manifolds, the expression for Hessian of the heat semigroup with adapted random fields is new. As an application, by using the above estimates we obtain the local Log-Sobolev inequality and the Log-Sobolev inequality with a potential term on general Riemannian loop spaces respectively.

Hao Xing

Rational inattention and dynamic discrete choice
We adopt the posterior-based approach to study dynamic discrete choice problems with rational inattention. We show that the optimal solution for the Shannon entropy case is characterized by a system of equations that resembles the dynamic logit rule. We propose an efficient algorithm to solve this system and apply our model to explain phenomena such as status quo bias, confirmation bias, and belief polarization. We also study the dynamics of consideration sets. Unlike the choice-based approach, our approach applies to general uniformly posterior-separable information cost functions. A key condition for our approach to work in dynamic models is the convexity of the difference between the discounted (generalized) entropy of the prior beliefs about the future states and the entropy of the current posterior. This is a joint work with Jianjun Miao.

Huaizhong Zhao

Periodic measures and stochastic resonance
In this talk, I will talk about the periodic measures of Markovian systems and their ergodic theory. Our theory reveals how geometric ergodic periodic measure provides rigorous proof for the transition of the stochastic resonance problem. We derive a parabolic partial differential equation for the expected exit time of the Markovian periodic process and its periodic solution gives the expected transition time of stochastic resonance. We subsequently find the parameters when the expected transition time is equal to the period of the system which is the real meaning of resonance. This is a joint work with Chunrong Feng and Johnny Zhong.

Xicheng Zhang

A discretized version of Krylov’s estimate and its applications
In this paper we prove a discretized version of Krylov’s estimate for discretized Ito’s processes. As applications, we study the weak and strong convergences for Euler’s approximation of mean-field SDEs with measurable discontinuous and linear growth coefficients. Moreover, we also show the propagation of chaos for Euler’s approximation of mean-field SDEs.