Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/258001 
Authors: 
Year of Publication: 
2020
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 8 [Issue:] 2 [Article No.:] 47 [Publisher:] MDPI [Place:] Basel [Year:] 2020 [Pages:] 1-28
Publisher: 
MDPI, Basel
Abstract: 
After a brief overview of aspects of computational risk management, the implementation of the rearrangement algorithm in R is considered as an example from computational risk management practice. This algorithm is used to compute the largest quantile (worst value-at-risk) of the sum of the components of a random vector with specified marginal distributions. It is demonstrated how a basic implementation of the rearrangement algorithm can gradually be improved to provide a fast and reliable computational solution to the problem of computing worst value-at-risk. Besides a running example, an example based on real-life data is considered. Bootstrap confidence intervals for the worst value-at-risk as well as a basic worst value-at-risk allocation principle are introduced. The paper concludes with selected lessons learned from this experience.
Subjects: 
computational risk management
rearrangement algorithm
implementation
R
bootstrap
worst value-at-risk allocation
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Document Type: 
Article
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