Optimal Reinsurance in a Multivariate Case

Abstract

This Master's Thesis aims to optimize reinsurance contracts in both univariate and multivariate cases, contributing to the advancement of reinsurance optimization techniques. First, we review the optimization methodology and identify the parts that can be solved analytically. We then develop Monte Carlo simulation methods to optimize a set of reinsurance contracts, using value-at-risk as the risk measure and exploring importance sampling to obtain more stable results and illustrate the methods with symmetrical and asymmetrical examples. Our findings provide insights for practitioners and researchers in the field and demonstrate the potential of Monte Carlo simulation and the importance sampling in optimizing multivariate reinsurance contracts.