Insurance Mathematics and Stochastic Finance

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Talks in Financial and Insurance Mathematics

This is the regular weekly research seminar on Insurance Mathematics and Stochastic Finance.

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Autumn Semester 2025

Date / Time Speaker Title Location
9 October 2025
17:15-18:15 		Prof. Dr. Syoiti Ninomiya
Institute of Science Tokyo
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Talks in Financial and Insurance Mathematics

Title New architectures of high-order deep neural networks based on weak approximation schemes for SDEs
Speaker, Affiliation Prof. Dr. Syoiti Ninomiya, Institute of Science Tokyo
Date, Time 9 October 2025, 17:15-18:15
Location HG G 43
Abstract New deep neural network architectures based on high-order weak approximation algorithms for stochastic differential equations (SDEs) are proposed. The core of these architectures is formed by high-order weak approximation algorithms of the explicit Runge--Kutta type, in which the approximation is realised solely through iterative compositions and linear combinations of the vector fields of the target SDEs.
New architectures of high-order deep neural networks based on weak approximation schemes for SDEsread_more
HG G 43
16 October 2025
17:15-18:15 		Dr. Florian Kogelbauer
ETH Zurich
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Talks in Financial and Insurance Mathematics

Title Slow Spectral Manifolds in Kinetic Models
Speaker, Affiliation Dr. Florian Kogelbauer, ETH Zurich
Date, Time 16 October 2025, 17:15-18:15
Location HG G 43
Abstract We discuss recent developments around Hilbert's Sixth Problem about the passage from kinetic models to fluid equations. We employ the technique of slow spectral closure to rigorously establish the existence of hydrodynamic manifolds in the linear regime and derive new non-local fluid equations for rarefied flows independent of Knudsen number. We show the divergence of the Chapman--Enskog series for an explicit example and apply machine learning to learn the optimal hydrodynamic closure from DSMC data. The new dynamically optimal constitutive laws are applied to a rarefied flow problem. We then show how this geometric rational from dynamical systems theory can be applied analogously to the CEV model in finance and demonstrate how arbitrage opportunities can be related to bifurcations in market elasticity on the Fokker—Planck level.
Slow Spectral Manifolds in Kinetic Modelsread_more
HG G 43
23 October 2025
17:15-18:15 		Dr. Philipp Schmocker
ETH Zurich
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Talks in Financial and Insurance Mathematics

Title Deep inverse problem for double phase equation
Speaker, Affiliation Dr. Philipp Schmocker, ETH Zurich
Date, Time 23 October 2025, 17:15-18:15
Location HG G 43
Abstract We use neural operators to learn the coefficient-to-solution map for nonlinear elliptic equations of double phase type. Since the solution is the unique minimizer of the convex splitting problem involving the two double phase functionals over a uniformly convex and uniformly smooth Banach space, we approximate the coefficient-to-solution map by a generative equilibrium operator (GEO). For training, we concatenate the GEO with the reconstruction formula of the coefficient to learn its right-inverse on the coefficient space. Once trained, this approach allows us to efficiently predict solutions of the double phase equation for other coefficients. Further applications of these neural operators are presented in the context of portfolio optimization and quadratic hedging.
Deep inverse problem for double phase equationread_more
HG G 43
30 October 2025
17:15-18:15 		Andi Bodnariu
Stockholm University
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Talks in Financial and Insurance Mathematics

Title Time inconsistent control and stopping problems, blowing up intensities and rates
Speaker, Affiliation Andi Bodnariu, Stockholm University
Date, Time 30 October 2025, 17:15-18:15
Location HG G 43
Abstract The goal of this talk is to present recent results in the field of time-inconsistent stopping and control problems driven by stochastic differential equations. The talk considers the weak equilibrium approach. While solving these problems interesting novel type of strategies appear that are not present in the time consistent version. In particular for time-inconsistent stopping problems, a class of mixed (i.e., randomized) stopping times based on a local time construction of the stopping intensity appears. The counterpart for singular time-inconsistent control problems is given by a control which results in an absolutely continuous rate that creates an inaccessible boundary. Additionally, can be shown that the classical solution approach to these problems used in the time-consistent version (pure stopping times with no mixing and reflection for the SSC) does not always result in an equilibrium making these type of novel strategies necessary. This creates further insight into the strategy space that should be considered in order to prove general existence of equilibria, which is an open problem for most time-inconsistent problems.
Time inconsistent control and stopping problems, blowing up intensities and ratesread_more
HG G 43
6 November 2025
17:15-18:15 		Prof. Dr. Lukasz Delong
University of Warsaw
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Talks in Financial and Insurance Mathematics

Title Universal inference for testing calibration of mean estimates within the Exponential Dispersion Family
Speaker, Affiliation Prof. Dr. Lukasz Delong, University of Warsaw
Date, Time 6 November 2025, 17:15-18:15
Location HG G 43
Abstract The calibration of mean estimates, which requires that predictions are, on average, equal to the observed responses, is a critical property for reliable decision-making, particularly in actuarial and financial applications. In this presentation, first, we review classic approaches for validating the mean-calibration and introduce the Likelihood Ratio Test (LRT) within the Exponential Dispersion Family (EDF). Next, we investigate the framework of universal inference to test the mean-calibration. We develop a sub-sampled split Likelihood Ratio Test (sub-sampled split LRT) within the EDF that provides finite-sample guarantees with universally valid critical values. We investigate type I error, power and e-power of our sub-sampled split LRT, compare the sub-sampled split LRT to the classic LRT, study the effect of sub-sampling of training and test sets on the split LRT, and propose novel test statistics based on the sub-sampled split LRT to enhance performance the test. In our numerical experiments, we demonstrate that the sub-sampled split LRT and our modifications are appealing alternatives to the classic LRT and achieve high power in detecting miscalibration, offering a practical and powerful toolkit for validating the calibration of mean estimates.
Universal inference for testing calibration of mean estimates within the Exponential Dispersion Familyread_more
HG G 43
13 November 2025
17:15-18:15 		Prof. Dr. Damir Filipovic
EPFL
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Talks in Financial and Insurance Mathematics

Title Kernel Density Machines
Speaker, Affiliation Prof. Dr. Damir Filipovic, EPFL
Date, Time 13 November 2025, 17:15-18:15
Location HG G 43
Abstract We introduce kernel density machines (KDM), a nonparametric estimator of a Radon--Nikodym derivative, based on reproducing kernel Hilbert spaces. KDM applies to general probability measures on countably generated measurable spaces under minimal assumptions. For computational efficiency, we incorporate a low-rank approximation with precisely controlled error that grants scalability to large-sample settings. We provide rigorous theoretical guarantees, including asymptotic consistency, a functional central limit theorem, and finite-sample error bounds, establishing a strong foundation for practical use. Empirical results based on simulated and real data demonstrate the efficacy and precision of KDM.
Kernel Density Machinesread_more
HG G 43
20 November 2025
17:15-18:15 		Prof. Dr. Jonathan Ziveyi
University of New South Wales
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Talks in Financial and Insurance Mathematics

Title Efficient decumulation strategy with long-term care insurance and guaranteed minimum death benefit
Speaker, Affiliation Prof. Dr. Jonathan Ziveyi, University of New South Wales
Date, Time 20 November 2025, 17:15-18:15
Location HG G 43
Abstract With the global shift from defined benefit to defined contribution pension systems, retirement planning is now fully borne on individuals elevating their exposure to longevity, health, and market risks. This transition has prompted more precautionary saving behaviour, as retirees become more conservative in fully consuming their wealth. This research proposes a decumulation strategy which combines long-term care insurance (LTCI) and guaranteed minimum death benefit (GMDB) purchased at retirement with a withdrawal-then-rebalance investment approach. Within this framework, the retirement fund is modelled using a regime-switching structure, while a target volatility strategy detects asset allocation to smooth wealth dynamics and reduces likelihood of extreme losses. The LTCI covers late-life healthcare costs, whereas the GMDB secures a minimum bequest, thereby supporting both consumption confidence and legacy objectives. Numerical experiments compare consumption patterns under this strategy with default account-based pension drawdown strategies. Results reveal that the proposed strategy provides smoother long-term consumption and better resilience to adverse financial shocks. Sensitivity analyses exploring variations in insurance allocation ratios, health state transitions, and target volatility levels are performed. Preliminary results suggest that moderate volatility targets strike an effective balance between risk and sustainability, and that the strategy remains robust across different health scenarios. Joint work with Jennifer Alonso-Garcia, Mengdie Hu and Yuxin Zhou.
Efficient decumulation strategy with long-term care insurance and guaranteed minimum death benefitread_more
HG G 43
26 November 2025
17:15-18:15 		Dr. Valentin Tissot-Daguette
Bloomberg
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Talks in Financial and Insurance Mathematics

Title Relaxed MOT and Mocking Martingales
Speaker, Affiliation Dr. Valentin Tissot-Daguette, Bloomberg
Date, Time 26 November 2025, 17:15-18:15
Location HG G 19.1
Abstract Martingale Optimal Transport (MOT) offers a powerful, robust framework to price and hedge illiquid derivatives. The primal formulation requires admissible models to exactly match the market’s implied volatility (IV) surface, imposing hard constraints on the marginal distributions. However, in practice, the model’s IVs only need to fall within the observed bid-ask range. Mathematically, this translates into the model's marginal distributions being between the bid and ask marginals (when they exist) with respect to convex order, leading to a relaxation of MOT. By enlarging the set of admissible martingale couplings, Relaxed MOT generates wider, more realistic price bounds for illiquid instruments, including vanilla options whose payoffs exhibit mixed convexity. We finally introduce a weaker notion of mimicking martingales, coined mocking martingales, and extend Kellerer’s theorem to characterize their existence. Joint work with Shunan Sheng, Marcel Nutz (Columbia), and Bryan Liang (Bloomberg).
Relaxed MOT and Mocking Martingalesread_more
HG G 19.1
27 November 2025
17:15-18:15 		Prof. Dr. Filip Lindskog
Stockholm University
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Talks in Financial and Insurance Mathematics

Title Cost-of-capital valuation with risky assets
Speaker, Affiliation Prof. Dr. Filip Lindskog, Stockholm University
Date, Time 27 November 2025, 17:15-18:15
Location HG G 43
Abstract Cost-of-capital valuation is a well-established approach to the valuation of liabilities and is one of the cornerstones of current regulatory frameworks for the insurance industry. Standard cost-of-capital considerations typically rely on the assumption that the required buffer capital is held in risk-less one-year bonds. The aim of this work is to analyze the effects of allowing investments of the buffer capital in risky assets, e.g. in a combination of stocks and bonds. In particular, we make precise how the decomposition of the buffer capital into contributions from policyholders and investors varies as the degree of riskiness of the investment increases, and highlight the role of limited liability in the case of heavy-tailed insurance risks. We present a combination of general theoretical results, explicit results for certain stochastic models and numerical results that emphasize the key findings. The talk is based on joint work with Hansjörg Albrecher and Hervé Zumbach, available on arXiv: arxiv:2511.00895
Cost-of-capital valuation with risky assetsread_more
HG G 43
11 December 2025
17:15-18:15 		Dr. Aleksandar Arandjelovic
ETH Zurich
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Talks in Financial and Insurance Mathematics

Title Algorithmic strategies in continuous-time hedging and stochastic integration
Speaker, Affiliation Dr. Aleksandar Arandjelovic, ETH Zurich
Date, Time 11 December 2025, 17:15-18:15
Location HG G 43
Abstract We develop a rigorous framework for continuous-time algorithmic trading strategies from the point of view of mathematical finance. To this end, we first establish a universal approximation theorem for neural networks on locally convex spaces with respect to Orlicz-type topologies. When the underlying sigma-algebra is generated by a possibly uncountable family of random variables, we prove that neural networks, through functional representations, can approximate functions in these Orlicz spaces arbitrarily well. We then represent algorithmic strategies as simple predictable processes to establish their approximation capabilities in spaces of stochastic (integral) processes. As applications, we prove that algorithmic strategies can approximate mean-variance optimal hedging strategies arbitrarily well, we establish a no free lunch with vanishing risk condition for algorithmic strategies, and we obtain a 'neural' version of the Bichteler-Dellacherie theorem. This talk is based on joint work with Uwe Schmock (TU Wien).
Algorithmic strategies in continuous-time hedging and stochastic integrationread_more
HG G 43

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