Wir freuen uns, die Munich Risk and Insurance Days 2026 ankündigen zu dürfen! Renommierte Expertinnen und Experten aus Wissenschaft und Industrie werden die neuesten Entwicklungen im Risikomanagement sowie in der Finanz- und Versicherungsmathematik vorstellen und diskutieren. Der Workshop bietet eine ausgezeichnete Plattform für den Austausch zwischen Theorie und Praxis.

Die Veranstaltung findet am 8. und 9. Oktober 2026 in der Boltzmannstraße 3, 85748 Garching (Forschungszentrum), statt.

Teilnahme kostenfrei nach Anmeldung (Name, eMail Adresse, Universität/ Unternehmen) per email an: bettina.haas@tum.de

Many scientific areas, ranging from computer science to environmental science and finance, give rise to multivariate time series processes that exhibit long memory, that is, time-dependent data with slowly decaying autocorrelation. Efficient modeling in such settings is crucial for various analytical tasks, including inference and prediction; however, traditional approaches are challenging even in modest dimensions. Several settings are also characterized by a (possibly inferred) network encoding the presence or absence of component associations via its edge topology, yet current methods are often inadequate for successfully exploiting this structure. In this talk, we propose a novel approach to modeling multivariate long-memory time series by defining two network-based models, capturing both the temporal and the underlying network dynamics. Our proposed likelihood-based exact estimation method exhibits strong asymptotic properties and, thanks to the low-dimensional parameter space induced by the network, yields more stable estimates than traditional models, allowing us to tackle scenarios where current methods fail due to computational limitations.

Consensus-based optimization (CBO) is a derivative-free optimization method driven by interacting particles and multiplicative noise. Its diffusion degenerates at consensus, and every consensus configuration is an equilibrium. These features prevent a direct application of standard approaches to uniform-in-time propagation of chaos based on uniform ellipticity or global contractivity. I will present quantitative uniform-in-time propagation of chaos results for first-order and second-order CBO under explicit parameter and moment conditions. The proof exploits dissipation in centered variables and a refined stability estimate for the weighted consensus point. Exponential decay of centered moments, together with concentration estimates, makes the error coefficients integrable in time and yields the classical Monte Carlo rate. For the original first-order model, this requires neither additional confinement nor a bounded-domain cutoff. I will then discuss the asymptotic shape of the concentrating mean-field density for a dynamically regularized first-order CBO model. Beyond convergence to a Dirac mass, we identify a nontrivial limiting profile after centering and rescaling, and establish convergence in L1. The proof combines Wasserstein convergence with weighted Fisher information estimates that control the regularity of the rescaled density. The talk is based on joint work with Nicolai Gerber, Seung-Yeal Ha, Franca Hoffmann, Wuchen Li, and Urbain Vaes.

In recent years, there has been increasing awareness of the risks of collapse or tipping points in a wide variety of complex systems, ranging from human medical conditions and pandemics to ecosystems, climate, finance, and society. Even in systems where governing equations are known, such as the atmospheric flow, predictability is limited by the chaotic nature of the system and by the limited resolution in observations and computer simulations. These phenomena are naturally modelled by strongly nonlinear stochastic processes, which permit a statistical description. In this talk, I will present methods for analyzing data from such complex systems, with an application to an important tipping element in the climate: the Atlantic Meridional Overturning Circulation.

The aim of this conference is to bring together experts in quantum, classical and stochastic dynamics to exchange ideas about common methods and results in this broad field of mathematical physics. It will also be an occasion to celebrate the 80th birthday of Herbert Spohn. The conference will take place at the TUM Institute for Advanced Studies (IAS) on the Garching Research Campus and is organised and supported by the CRC TRR352 Mathematics of Many-Body Quantum Systems and Their Collective Phenomena. Registration is free and now open online: https://sites.google.com/view/largescaledynamics2026/home

TBA ____________________________________ Invited by Prof. Helmut Schwichtenberg

Granger causality is commonly formulated through linear prediction in vector autoregressive models, limiting its ability to detect nonlinear predictive relationships. We propose a reproducing kernel Hilbert space (RKHS)-based test for nonlinear Granger non-causality in the conditional mean for nonlinear autoregressive processes. The key idea is a conditional-centering decomposition of the target regression function into an own-history component and an orthogonal component capturing the additional predictive contribution of the potential source history. Non-causality is characterized by the vanishing of the latter component. The own-history component is estimated by kernel ridge regression, and the residuals are embedded in a second RKHS using a conditionally centered kernel. This yields an RKHS-valued residual moment whose squared norm forms the test statistic. We establish a weighted chi-square null limit and consistency against fixed alternatives for the population-centered statistic. An empirically centered version is shown to retain the null limit and enables spectral calibration of critical values and $p$-values without resampling. Simulation studies demonstrate accurate size control and power against nonlinear alternatives, and real-data applications illustrate the method's usefulness for detecting nonlinear predictive relationships in time series.

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