Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/257892 
Year of Publication: 
2019
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 7 [Issue:] 2 [Article No.:] 54 [Publisher:] MDPI [Place:] Basel [Year:] 2019 [Pages:] 1-22
Publisher: 
MDPI, Basel
Abstract: 
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.
Subjects: 
risk aggregation
numerical convolution
copula trees
Monte-Carlo simulation
spatial correlation
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
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