# A ruin model with a resampled environment

## TL;DR

This paper extends classical ruin theory to a model where environment-driven parameters are resampled at Poisson epochs, providing new asymptotic approximations, bounds, and an efficient importance-sampling method for risk assessment.

## Contribution

It introduces a resampled environment model in ruin theory, extending classical results and deriving explicit bounds and an importance-sampling algorithm for complex, environment-dependent risk processes.

## Key findings

- Extended Cramér-Lundberg approximation for resampled environments
- Derived explicit bounds on ruin probability without spectral restrictions
- Developed an importance-sampling algorithm with bounded relative error

## Abstract

This paper considers a Cram\'er-Lundberg risk setting, where the components of the underlying model change over time. These components could be thought of as the claim arrival rate, the claim-size distribution, and the premium rate, but we allow the more general setting of the cumulative claim process being modelled as a spectrally positive L\'evy process. We provide an intuitively appealing mechanism to create such parameter uncertainty: at Poisson epochs we resample the model components from a finite number of $d$ settings. It results in a setup that is particularly suited to describe situations in which the risk reserve dynamics are affected by external processes (such as the state of the economy, political developments, weather or climate conditions, and policy regulations). We extend the classical Cram\'er-Lundberg approximation (asymptotically characterizing the all-time ruin probability in a light-tailed setting) to this more general setup. In addition, for the situation that the driving L\'evy processes are sums of Brownian motions and compound Poisson processes, we find an explicit uniform bound on the ruin probability, which can be viewed as an extension of Lundberg's inequality; importantly, here it is not required that the L\'evy processes be spectrally one-sided. In passing we propose an importance-sampling algorithm facilitating efficient estimation, and prove it has bounded relative error. In a series of numerical experiments we assess the accuracy of the asymptotics and bounds, and illustrate that neglecting the resampling can lead to substantial underestimation of the risk.