Im Rahmen des Forschungsseminars findet ein d-fine day statt. Neben einer allgemeinen Vorstellung des Unternehmens mit anschliessendem get-together wird es einen Vortrag zum Thema "Quantum Machine Learning – Approaches, Applications and Results" geben. Interessierte Studierende sind herzlich eingeladen.

## Quantum Computing in Machine Learning

## Efficient Allocations under Ambiguous Model Uncertainty

We investigate consequences of model uncertainty (or ambiguity) on ex ante efficient allocations in an exchange economy. The ambiguity we consider is embodied in the model uncertainty perceived by the decision maker: they are unsure what would be the appropriate probability measure to apply to evaluate contingent consumption contingent plans and keep in consideration a set of alternative probabilistic laws. We study the case where the typical consumer in the economy is ambiguity-averse with smooth ambiguity preferences and the set of priors $\mathcal{P}$ is point identified, i.e., the true law $p\in \mathcal{P}$ can be recovered empirically from observed events. Differently from the literature, we allow for the case where the aggregate risk is ambiguous and agents are heterogeneously ambiguity averse. Our analysis addresses, in particular, the full range of set-ups where under expected utility the Pareto efficient consumption sharing rule is a linear function of the aggregate endowment. We identify systematic differences ambiguity aversion introduces to optimal sharing arrangements in these environments and also characterize the representative consumer. Furthermore, we investigate the implications for the state-price function, in particular, the effect of heterogeneity in ambiguity aversion.

## Understanding the Time-Space Evolution of Economic Activities: Recent Mathematical Models and their application

The goal of this talk is to present some recent models on the time evolution of most important economic variables (e.g. consumption and capital) across different locations, taking into account space heterogeneity. In particular we focus on two recent papers looking at the macro level where there is one planner which, in a spatial Ramsey setting, maximizes utility across space with heterogeneous productivity in a deterministic (paper with R. Boucekkine, G. Fabbri, S. Federico) or in a stochastic setting (paper with M. Leocata). If time allows we will also introduce a mean field game model looking at the micro level where the agents move across space maximizing their own utility which also depends on the choices of the other agents.

## Mean-field Langevin dynamics and neural networks

## tba

## A synthetic model for ALM in life insurance and numerical methods for SCR computation

We introduce a synthetic ALM model that catches the main specificity of life insurance contracts. First, it keeps track of both market and book values to apply the regulatory profit sharing rule. Second, it introduces a determination of the crediting rate to policyholders that is close to the practice and is a trade-off between the regulatory rate, a competitor rate and the available profits. Third, it considers an investment in bonds that enables to match a part of the cash outflow due to surrenders, while avoiding to store the trading history. We use this model to evaluate the Solvency Capital Requirement (SCR) with the standard formula, and illustrate the importance of matching cash-flows.

Then, we focus on the problem of evaluating the SCR at future dates. For this purpose, we study the multilevel Monte-Carlo estimator for the expectation of a maximum of conditional expectations. We obtain theoretical convergence results that complements the recent work of Giles and Goda. We then apply the MLMC estimator to the calculation of the SCR at future dates and compare it with estimators obtained with Least Squares Monte-Carlo or Neural Networks. Last, we discuss the effect of the portfolio allocation on the SCR at future dates.

## Robust Uncertainty Analysis

In this talk, we will showcase how methods from optimal transport and distributionally robust optimisation allow to capture and quantify sensitivity to model uncertainty for a large class of problems. We consider a generic stochastic optimisation problem. This could be a mean-variance or a utility maximisation portfolio allocation problem, a risk measure computation, a standard regression or a deep learning problem. At the heart of the optimisation is a probability measure, or a model, which describes the system. It could come from data, simulations or a modelling effort for which there is always exists a degree of uncertainty. We take a non-parametric approach and capture model uncertainty using Wasserstein balls around the postulated measure. Our main results provide explicit formulae for the first order correction to both the value function and the optimiser. We further extend our results to optimisation under linear constraints. Our sensitivity analysis of the distributionally robust optimisation problems finds applications in statistics, machine learning, mathematical finance and uncertainty quantification. In the talk, we will discuss several financial examples anchored in a one-step financial model and compute their sensitivity to model uncertainty. These include: option pricing, mean-variance portfolio selection, optimised certainty equivalent and similar risk assessments. We will also address briefly some other applications, such as explicit formulae for first-order approximations of square-root LASSO and square-root Ridge optimisers and measures of NN architecture robustness wrt to adversarial data.

This talk is based on joint works with Daniel Bartl, Jan Obloj and Johannes Wiesel.