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Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks

AIR-Worldwide, Financial Modeling Group, 131 Dartmouth St., Boston, MA 02116, USA
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Risks 2019, 7(2), 54; https://doi.org/10.3390/risks7020054
Received: 31 March 2019 / Revised: 28 April 2019 / Accepted: 5 May 2019 / Published: 8 May 2019
We present several fast algorithms for computing the distribution of a sum of spatially dependent, discrete random variables to aggregate catastrophe risk. The algorithms are based on direct and hierarchical copula trees. Computing speed comes from the fact that loss aggregation at branching nodes is based on combination of fast approximation to brute-force convolution, arithmetization (regriding) and linear complexity of the method for computing the distribution of comonotonic sum of risks. We discuss the impact of tree topology on the second-order moments and tail statistics of the resulting distribution of the total risk. We test the performance of the presented models by accumulating ground-up loss for 29,000 risks affected by hurricane peril. View Full-Text
Keywords: risk aggregation; numerical convolution; copula trees; Monte-Carlo simulation; spatial correlation risk aggregation; numerical convolution; copula trees; Monte-Carlo simulation; spatial correlation
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Wójcik, R.; Liu, C.W.; Guin, J. Direct and Hierarchical Models for Aggregating Spatially Dependent Catastrophe Risks. Risks 2019, 7, 54.

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