The Lee-Carter model has become a benchmark in stochastic mortality modeling. However, its forecasting performance can be significantly improved upon by modern machine learning techniques. We propose a convolutional neural network architecture for mortality rate forecasting, empirically compare...

We investigate worst-case optimal consumption and portfolio decisions under the threat of a market crash. In an infinite-horizon setting, we provide an explicit solution for constant relative risk aversion and establish a rigorous verification result. Moreover, we find a dual characterization of...

We control the surplus process of a non-life insurance company by dynamic proportional reinsurance. The objective is to maximize expected (utility of the) surplus under the worst-case claim development. In the large claim case with a worst-case upper limit on claim numbers and claim sizes, we...

We propose a novel generative model for multivariate discrete-time time series data. Drawing inspiration from the construction of neural spline flows, our algorithm incorporates linear transformations and the signature transform as a seamless substitution for traditional neural networks. This...

The COVID-19 pandemic interrupts the relatively steady trend of improving longevity observed in many countries over the last decades. We claim that this needs to be addressed explicitly in many mortality modeling applications, for example in the life insurance industry. To support this position,...