The 7th Berlin Workshop on Mathematical Finance for Young Researchers provides a forum for PhD students, postdoctoral researchers, and young faculty members from all over the world to discuss their research in an informal atmosphere. Keynote lectures will be given by

• Sara Biagini (Rome)
• Luciano Campi (Milano)
• Giorgio Ferrari (Bielefeld)
• Mete H. Soner (Princeton)
• Luitgard Veraart (London)

We also invite up to 20 contributed talks from young researchers. The deadline for abstract submission is May 20. Notification of acceptance will be sent by May 31. Accommodation for speakers will be arranged. 
Limited support for travel expenses may be available upon request. Here a link to the workshop webpage. 

This workshop brings together mathematicians and economists to discuss recent developments in the field of Risk Mitigation with a particular focus on applications to climate, energy and financial risk. The workshop is part of a conference series initiated by the Editors-in-Chief of the Springer published journal Mathematics and Financial Economics to promote the interaction between mathematics, economics and finance. Previous workshops focussed on Knightian Uncertain in Financial Markets, Mathematics of Behavioral Economics, and Many-Player Games and Applications.

The workshop will be held on September 2nd and 3rd, 2024 at the Humboldt University Berlin. It is sponsored by Springer Verlag, the Collaborative Research Center 1283(Bielefeld), the Collaborative Research Center 190 (Berlin, Munich) and the Berlin-Oxford International Research Training Group IRTG 2544. The workshop is jointly organized by the chair Applied Financial Mathematics at Humboldt University Berlin and the Center for Mathematical Economics at Bielefeld University.

Click here for the conference webpage. 

In this talk, we will introduce Consensus-Based Optimization (CBO) for min-max problems, a novel multi-particle, derivative-free optimization method that can provably identify global equilibrium points. This paradigm facilitates the transition to the mean-field limit, making the method amenable to theoretical analysis and providing rigorous convergence guarantees under reasonable assumptions about the initialization and the objective function, including nonconvex-nonconcave objectives. Additionally, numerical evidence will be presented to demonstrate the algorithm's effectiveness. This talk is based on joint works with Giacomo Borghi, Enis Chenchene, Hui Huang, and Konstantin Riedl.

We obtain a probabilistic solution to linear-quadratic optimal control problems with state constraints. Given a closed set $\mathcal D\subseteq [0,T]\times\mathbb R^d$, a diffusion $X$ in $\mathbb R^d$ must be linearly controlled in order to keep the time-space process $(t,X_t)$ inside the set $\mathcal $mathcal C:=([0,T]\times\mathbb R^d)\setminus\mathcal D$, while at the same time minimising an expected cost that depends on the state $(t,X_t)$ and it is quadratic in the speed of the control exerted. We find an explicit probabilistic representation for the value function and the optimal control under a set of mild sufficient conditions concerning the coefficients of the underlying dynamics and the regularity of the set $\mathcal C$. Fully explicit formulae are presented in some relevant examples.

(Joint work with Erik Ekstr\"om, University of Uppsala, Sweden)
 

In this talk, we delve into the application of metaheuristics via extensive systems of interacting particles to tackle complex optimization problems, starting from the Particle Swarm Optimization (PSO) method. This technique leverages collective intelligence, where individual particles adapt their trajectories based on their own success and the influence of their neighbors, directing the swarm toward the optimal solution. We will investigate the continuous model proposed by Grassi and Pareschi, providing evidence of its convergence to global minimizers and illustrating its relationship to Consensus-Based Optimization (CBO) in the limit of zero inertia. The talk is based on joint works with Cristina Cipriani and Hui Huang.