arXiv subjects

Markus Johannes Maier

Publications and source records attributed to Markus Johannes Maier.

3 recordsLinked to original sources

arXiv2026

Predicting future insurance markets: Simulation of technology adoption paths with a Bayesian Bass model

Technological progress can give rise to new loss exposures or reduce existing risks. In either case, it strongly affects insurance companies' existing lines of business. As the magnitude of such effects is often closely connected to the innovation's market penetration, reliably predicting its adoption process is vital in enabling insurers to adapt their business strategy accordingly. To tackle this challenge, we propose a novel combination of contributions from the domain of innovation diffusion. Our methodology generates adoption scenarios that holistically account for different sources of uncertainty, offers a structured way to incorporate expert judgment and insights from pre-existing technologies, and updates its predictions as new information on the technology's diffusion becomes available. Its modular nature ensures that components can be easily replaced by suitable alternatives. Using publicly accessible data on privately-owned battery electric vehicles in Germany, the application of our approach for predicting changes to insurance losses and for supporting insurers' sustainability strategies is visualized in a motor insurance case study.

stat.AP↗

arXiv2026

Optimal basis risk weighting in expectile-based parametric insurance

Parametric insurance contracts translate index measurements to compensation for policyholders' losses using predefined payment schemes. These need to be designed carefully to keep basis risk, i.e. the disparity between payouts and true damages, small. Previous research has motivated the use of conditional expectiles as payment schemes, whose compensation is impacted by the policyholder's potentially unknown attitude towards basis risk. To alleviate this model uncertainty and to investigate the impact of (hidden) influencing factors, we characterize existence and uniqueness of the optimal basis risk weighting in a utility-maximization framework through a set of boundary conditions. In the absence of an optimal solution, we provide comparisons to the utility of no insurance and full indemnity coverage. We establish a link between location-scale distributions and separability of conditional expectiles' derivatives, thus improving the understanding of these statistical functionals. A simulation study on parametric hurricane insurance visualizes our results, investigates the influence of premium loading and risk aversion on the optimal weighting, and comments on the challenge of (spatial) loss dependence.

stat.AP↗

arXiv2025

Expectiles as basis risk-optimal payment schemes in parametric insurance

Payments in parametric insurance solutions are linked to an index and thus decoupled from policyholders' true losses. While this principle has appealing operational benefits compared to traditional indemnity coverage, i.e. is very efficient and cost effective, a downside is the discrepancy between payouts and actual damage, called basis risk. We show that in an asymmetrically weighted mean square error framework, the basis risk-minimizing payment schemes for pure parametric and parametric index insurance contracts can be expressed as conditional expectiles of policyholders' true loss given a compensation-triggering incident. We provide connections to stochastic orderings and demonstrate that regression approaches allow easy implementation in practice. Our results are visualized in parametric coverage for cyber risks and agricultural insurance.

stat.AP↗