The next meeting will take place in Amsterdam on Friday 28 November 2025.
Location: TBA
Registration: here
Schedule:
14:00 - 14:30 : Hidde Fokkema
14:30 - 15:00 : Michael Kurz
15:00 - 15:30 : Truong Nguyen
15:30-16:00: Coffee Break
16:00 - 16:30 : Matteo Ferrari
16:30 - 17:00 : Arco de Kort
17:00 - 17:30 : Evgenii Vladimirov
17:30-20:00: Closing and dinner
Speakets, Titles and Abstracts:
Matteo Ferrari (UvA): Measuring Financial Resilience Using Backward Stochastic Differential Equations
Abstract: We propose the resilience rate as a measure of financial resilience. It captures the expected rate at which a dynamic risk evaluation recovers, i.e., bounces back, after the risk-acceptance set is breached. We develop the corresponding stochastic calculus by establishing representation theorems for a suitable time-derivative of solutions to backward stochastic differential equations (BSDEs) with jumps evaluated at stopping times. These results reveal that our resilience rate can be represented as an expectation of the generator of a BSDE. We analyze the properties of the resilience rate. We also introduce resilience- acceptance sets and study their properties in relation to both the resilience rate and the dynamic risk measure. We illustrate our results in several examples.
Hidde Fokkema (UvA): Sample-efficient Learning of Concepts with Theoretical Guarantees: from Data to Concepts without Interventions
Abstract: Machine learning is a vital part of many real-world systems, but several concerns remain about the lack of interpretability, explainability and robustness of black-box AI systems. Concept Bottleneck Models (CBM) address some of these challenges by learning interpretable concepts from high-dimensional data, e.g. images, which are used to predict labels. In this paper, we describe a framework that provides theoretical guarantees on the correctness of the learned concepts and on the number of required labels, without requiring any interventions. Our framework leverages causal representation learning (CRL) methods to learn latent causal variables from high-dimensional observations in an unsupervised way, and then learns to align these variables with interpretable concepts with few concept labels. We prophose a linear and a non-parametric estimator for this mapping, providing a finite-sample high probability result in the linear case and an asymptotic consistency result for the non-parametric estimator. We evaluate our framework in synthetic and image benchmarks, showing that the learned concepts have less impurities and are often more accurate than other CBMs, even in settings with strong correlations between concepts. Finally, we will describe how this method could potentially be deployed in settings such as fraud detection and anomaly detection.
Arco de Kort (UU): Valuation of Interest Rate Derivatives on Arithmetic Averages of Risk-Free Rates
Abstract: In the post-LIBOR-era, interest rate products based on the arithmetic average of Risk-Free Rates (RFRs) are becoming increasingly popular for clients. These instruments are more intuitive for clients but introduce mathematical challenges in the valuation. In this presentation I will discuss a few approximations to model the convexity adjustment, including SABR based methods and Breeden-Litzenberger approaches. This comparative analysis highlights accuracy and applicability across different maturities and volatility levels.
Michael Kurz (MN & TU Delft): TBA
Abstract: TBA
Truong Nguyen (UU): Adaptive Multilevel Fourier–RQMC Methods for Multivariate Shortfall Risk
Abstract: Systemic risk measures were introduced to capture the global risk and the corresponding contagion effects generated by an interconnected system of financial institutions. Among these, the multivariate shortfall risk measure (MSRM) provides a principled framework for pre-aggregation capital allocation, determining the minimal distribution of capital across institutions required to secure the system. While the theoretical foundations of MSRM are well established, efficient numerical methods for their computation remain limited. In this work, we develop a new class of algorithms that combine Fourier methods with randomized quasi-Monte Carlo (RQMC) to compute the multivariate shortfall risk and the associated optimal allocations. We provide a rigorous mathematical foundation for the Fourier-based approach, including an analysis of convergence rates. Beyond the single-level RQMC method, we introduce an adaptive multilevel (ML) RQMC scheme, which leverages the geometric convergence of the allocation optimization to achieve further variance reduction and computational gains. Several numerical examples confirm the superior performance of the proposed Fourier–RQMC approach over the Sample Average Approximation (SAA) and stochastic optimization benchmarks. Moreover, ML-RQMC yields additional speedups over single-level RQMC while preserving accuracy in optimal risk allocation.
Evgenii Vladimirov (EUR): Functional Estimation of Option Pricing Models
Abstract: In this paper, we develop a novel estimation procedure for parametric option pricing models specified under the risk-neutral measure. We set up our estimation strategy to minimize the distance in a functional sense between the option-implied and model-implied logarithm of conditional characteristic functions. Within a broad class of option pricing models, for which the characteristic function is marginally affine, the model's latent state vector can be concentrated out in closed form. As a result, our estimation procedure is computationally fast and easy to implement, while at the same time exploiting all distributional information contained in an option panel about the risk-neutral dynamics of the underlying asset price. We establish the asymptotic properties of the parameter and state estimators and investigate the finite-sample performance of our method in Monte Carlo simulations. In an empirical application, we illustrate the usefulness of our estimation procedure based on by far the largest option panel, both in the time-series and cross-sectional dimensions, considered in the related literature.