2013/10/31 by Matias Leppisaari, Leppisaari, Matias
Decision Sciences · Economics, Econometrics and Finance · Mathematics · Social Sciences · #Applications (stat.AP) #FOS: Computer and information sciences #FOS: Economics and business #Financial Risk and Volatility Modeling #Insurance and Financial Risk Management #Insurance, Mortality, Demography, Risk Management #Probability and Risk Models #Risk Management (q-fin.RM) #q-fin.RM #stat.AP
paper · pdf · doi:10.48550/arxiv.1310.8604
32 pages, 9 figures
arxiv created 2013/10/31 · openalex publication_date 2013/10/31 · arxiv updated 2013/11/01 · openalex created_date 2022/10/01 · openalex updated_date 2026/07/28
Recently, a marked Poisson process (MPP) model for life catastrophe risk was proposed in [6]. We provide a justification and further support for the model by considering more general Poisson point processes in the context of extreme value theory (EVT), and basing the choice of model on statistical tests and model comparisons. A case study examining accidental deaths in the Finnish population is provided. We further extend the applicability of the catastrophe risk model by considering small and big accidents separately; the resulting combined MPP model can flexibly capture the whole range of accidental death counts. Using the proposed model, we present a simulation framework for pricing (life) catastrophe reinsurance, based on modeling the underlying policies at individual contract level. The accidents are first simulated at population level, and their effect on a specific insurance company is then determined by explicitly simulating the resulting insured deaths. The proposed microsimulation approach can potentially lead to more accurate results than the traditional methods, and to a better view of risk, as it can make use of all the information available to the re/insurer and can explicitly accommodate even complex re/insurance terms and product features. As an example we price several excess reinsurance contracts. The proposed simulation model is also suitable for solvency assessment.