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In this paper, we present a conceptual framework for modelling clustered natural hazards that makesuse of historical event data as a starting point. We review a methodology for modelling clusterednatural hazard processes called Poisson mixtures. This methodology is suited to the applicationwe have in mind as it naturally models processes that yield cross-event correlation (unlike homogeneousPoisson models), has a high degree of tunability to the problem at hand and is analytically tractable.Using European windstorm data as an example, we provide evidence that thehistorical data show strong evidence of clustering. We then develop Poisson and Clusteredsimulation models for the data, demonstrating clearly the superiority of the Clustered model whichwe have implemented using the Poisson mixture approach. We then discuss the implicationsof including clustering in models of prices of catXL contracts, one of the most commonly usedmechanisms for transferring risk between primary insurers and reinsurers. This paper providesa number of unique insights into the impact clustering has on modelled catXL contract prices.The simple modelling example in this paper provides a clear and insightful startingpoint for practitioners tackling more complex natural hazard risk problems

Modelling clustering of natural hazard phenomena and the effect on re/insurance loss perspectives