Graduate Seminar Financial and Actuarial Mathematics LMU and TUM

Loss underreporting in insurance refers to the practice that insureds intentionally hide the full extent of losses to insurance companies (insurers). This behavior is driven by the “hunger for bonus” embedded in experience rating systems for pricing insurance policies, which reward insureds who do not report a claim but penalize those who do. In this talk, we first formulate optimal loss reporting problems under both discrete-time and continuous-time models and offer a rigorous analysis of such problems. Next, we discuss how strategic underreporting affects the insured’s demand for insurance contracts. Finally, we extend the analysis to a game framework to study how insureds’ loss reporting impacts the insurer’s pricing strategies

The exposure of sovereigns to climate risks is priced and can affect credit ratings and debt servicing costs. I will discuss how integrated assessment models (IAMs) can be linked with stochastic debt sustainability analysis to inform our understanding of climate risks to sovereign debt dynamics and assess the available fiscal space to finance climate policies. The narrative scenario architecture developed within the IPCC and the transition scenarios of NGFS can be adopted to bring structure and transparency to the analysis. The analysis is complicated by deep uncertainty —risks, ambiguity, and mis-specifications— of climate change. Using scenario trees, narrative scenarios, and model ensembles, respectively, we can address these three challenges. I will discuss two applications combining well-known climate models with a high-realism debt sustainability analysis model, and discuss (i) sovereign debt effects of the transition to low-carbon economies on a large panel of countries, and (ii) effects of damages and adaptation. Results show that adaptation pays, but raises the question of who will pay for it.

Real-life financial time series exhibit heavy tails and clusters of extreme values. In this talk we will address models that exhibit these stylized facts. This is the class of regularly varying time series, introduced by Davis and Hsing (1995, AoP) and further developed by Basrak and Segers (2009, SPA). The marginal distribution of a regularly varying time series has tails of power-law type, and the dynamics caused by an extreme event in this time series is described by the spectral tail process. The perhaps best known financial time series models of this kind are Engle’s (1982) ARCH process, Bollerslev’s (1986) GARCH process and Engle’s and Russell’s (1998) Autoregressive Conditional Duration (ACD) model. The length and magnitude of extremal clusters in such a series can be described by an analog of the autocorrelation function for extreme events: the extremogram. The extremal index is another useful tool for describing expected extremal cluster sizes. Both objects can be expressed in terms of the spectral tail process and allow for statistical estimation. The probabilistic and statistical aspects of regularly varying time series are summarized in the recent monograph by Mikosch and Wintenberger (2024) “Extreme Value Theory for Time Series. Models with Power-Law Tails”. The talk is based on joint work with Olivier Wintenberger (Sorbonne).

Let π∈Π(μ,ν) be a coupling between two probability measures μ and ν on a Polish space. In this talk we propose and study a class of nonparametric measures of association between μ and ν, which we call Wasserstein correlation coefficients. These coefficients are based on the Wasserstein distance between ν and the disintegration πx1 of π with respect to the first coordinate. We also establish basic statistical properties of this new class of measures: we develop a statistical theory for strongly consistent estimators and determine their convergence rate in the case of compactly supported measures μ and ν. Throughout our analysis we make use of the so-called adapted/bicausal Wasserstein distance, in particular we rely on results established in [Backhoff, Bartl, Beiglböck, Wiesel. Estimating processes in adapted Wasserstein distance. 2022]. Our approach applies to probability laws on general Polish spaces.

We study how to construct a stochastic process on a finite interval with given `roughness'. We first extend Ciesielski's isomorphism along a general sequence of partitions, and provide a characterization of Hölder regularity of a function in terms of its Schauder coefficients. Using this characterization, we provide a better (path wise) estimator of Hölder exponent. Furthermore, we study the concept of (generalized) p-th variation of a real-valued continuous function along a sequence of partitions. We show that the finiteness of the p-th variation of a given function is closely related to the finiteness of ℓp-norm of the coefficients along a Schauder basis. As an additional application, we construct fake (fractional) Brownian motions with some path properties and finite moments of marginal distributions same as (fractional) Brownian motions. These belong to non-Gaussian families of stochastic processes which are statistically difficult to distinguish from real (fractional) Brownian motions.

The Wasserstein distance W is an important instance of an optimal transport cost. Its numerous mathematical properties as well as applications to various fields such as mathematical finance and statistics have been well studied in recent years. The adapted Wasserstein AW distance extends this theory to laws of discrete time stochastic processes in their natural filtrations, making it particularly well suited for analyzing time-dependent stochastic optimization problems. While the topological differences between AW and W are well understood, their differences as metrics remain largely unexplored beyond the trivial bound W

Stochastic optimal control problems naturally arise in contexts such as optimal investment, optimal consumption, and economic growth. Moreover, many fundamental models in robust finance - such as G-Brownian motion, G-diffusions, or G-semimartingales - can be translated to frameworks of stochastic control. A central aspect of these problems is the connection between value functions and Hamilton-Jacobi-Bellman (HJB) equations. For controlled diffusions with sufficiently regular coefficients, this link is typically established either through the comparison method, relying on a comparison principle for discontinuous viscosity solutions, or via the verification approach, which requires the existence of classical or Sobolev solutions. In this talk, we consider a general class of controlled diffusions for which these traditional methods break down. We present a new approach that connects stochastic control problems and HJB equations by combining probabilistic and analytic techniques. Furthermore, we discuss uniqueness results, leading to stochastic representations of HJB equations in terms of control problems, and provide stability results for associated value functions.

In this talk, we study dependence uncertainty and the resulting effects on tail risk measures, which play a fundamental role in modern risk management. We introduce the notion of a regular dependence measure, defined on multimarginal couplings, as a generalization of well-known correlation statistics such as the Pearson correlation. The first main result states that even an arbitrarily small positive dependence between losses can result in perfectly correlated tails beyond a certain threshold and seemingly complete independence before this threshold. In a second step, we focus on the aggregation of individual risks with known marginal distributions by means of arbitrary nondecreasing left-continuous aggregation functions. In this context, we show that under an arbitrarily small positive dependence, the tail risk of the aggregate loss might coincide with the one of perfectly correlated losses. A similar result is derived for expectiles under mild conditions. In a last step, we discuss our results in the context of credit risk, analyzing the potential effects on the value at risk for weighted sums of Bernoulli distributed losses.

Martingales associated with path-dependent payoff functions are intrinsically linked to path-dependent PDEs. While this connection is typically established via a functional Itô formula, in this talk we present a semigroup-theoretic framework for the analytic characterization of martingales with path-dependent terminal conditions. Specifically, we show that a measurable adapted process of the form V(t) - ∫_0^t Ψ(s)ds is a martingale if and only if a time-shifted version of V is a mild solution to a final value problem (FVP) involving a path-dependent differential operator. We establish existence and uniqueness of solutions to such FVPs using the concept of evolutionary semigroups on path space. We also discuss the relationship between semigroups on path space, nonlinear expectations and their penalty functions. The talk is based on joint work with David Criens, Robert Denk and Markus Kunze.

We present a multi-agent and mean-field formulation of a game between investors who receive private signals informing their investment decisions and who interact through relative performance concerns. A key tool in our model is a Poisson random measure which drives jumps in both market prices and signal processes and thus captures common and idiosyncratic noise. Upon receiving a jump signal, an investor evaluates not only the signal's implications for stock price movements but also its implications for the signals received by her peers and for their subsequent investment decisions. A crucial aspect of this assessment is the distribution of investor types in the economy. These types determine their risk aversion, performance concerns, and the quality and quantity of their signals. We demonstrate how these factors are reflected in the corresponding HJB equations, characterizing an agent's optimal response to her peers' signal-based strategies. The existence of equilibria in both the multi-agent and mean-field game is established using Schauder's Fixed Point Theorem under suitable conditions on investor characteristics, particularly their signal processes. Finally, we present numerical case studies that illustrate these equilibria from a financial-economic perspective. This allows us to address questions such as how much investors should care about the information known by their peers.

We provide a unified framework to obtain numerically certain quantities, such as the distribution function, absolute moments and prices of financial options, from the characteristic function of some (unknown) probability density function using the Fourier-cosine expansion (COS) method. The classical COS method is numerically very efficient in one-dimension, but it cannot deal very well with certain integrands in general dimensions. Therefore, we introduce the damped COS method, which can handle a large class of integrands very efficiently. We prove the convergence of the (damped) COS method and study its order of convergence. The method converges exponentially if the characteristic function decays exponentially. To apply the (damped) COS method, one has to specify two parameters: a truncation range for the multivariate density and the number of terms to approximate the truncated density by a cosine series. We provide an explicit formula for the truncation range and an implicit formula for the number of terms. Numerical experiments up to five dimensions confirm the theoretical results.

Please register for the workshop. More information can be found at s. https://www.fm.math.lmu.de/en/news/events-overview/event/frontiers-in-mathematical-finance-between-theory-and-applications.html

Please register for the workshop. More information can be found at s. https://www.fm.math.lmu.de/en/news/events-overview/event/frontiers-in-mathematical-finance-between-theory-and-applications.html

We study opportunistic traders that try to detect and exploit the order flow of dealers hedging their net exposure to the FX fix. We also discuss how dealers can take this into account to balance not only risk and trading costs but also information leakage in an appropriate manner. It turns out that information leakage significantly expands the set of scenarios where both dealers and the clients whose orders they execute benefit from hedging part of the exposure before the fixing window itself. (Joint work in progress with Roel Oomen (Deutsche Bank) and Mateo Rodriguez Polo (ETH Zurich))

Credibility methods in insurance provide a linear approximation, formulated as a weighted average of claim history, making them highly interpretable for estimating the predictive mean of the a posteriori rate. In this presentation, we extend the credibility method to a generalized coefficient regression model, where credibility factors—interpreted as regression coefficients—are modeled as flexible functions of claim history. This extension, structurally similar to the attention mechanism, enhances both predictive accuracy and interpretability. A key challenge in such models is the potential issue of non-identifiability, where credibility factors may not be uniquely determined. Without ensuring the identifiability of the generalized coefficients, their interpretability remains uncertain. To address this, we first introduce a state-space model (SSM) whose predictive mean has a closed-form expression. We then extend this framework by incorporating neural networks, allowing the predictive mean to be expressed in a closed-form representation of generalized coefficients. We demonstrate that this model guarantees the identifiability of the generalized coefficients. As a result, the proposed model not only offers flexible estimates of future risk—matching the expressive power of neural networks—but also ensures an interpretable representation of credibility factors, with identifiability rigorously established. This presentation is based on joint work with Mario Wuethrich (ETH Zurich) and Hong Beng Lim (Chinese University of Hong Kong).