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- Publisher Website: 10.3390/risks8020047
- Scopus: eid_2-s2.0-85085915120
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Article: Implementing the rearrangement algorithm: An example from computational risk management
Title | Implementing the rearrangement algorithm: An example from computational risk management |
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Authors | |
Keywords | Bootstrap Computational risk management Implementation R Rearrangement algorithm Worst value-at-risk allocation |
Issue Date | 2020 |
Citation | Risks, 2020, v. 8, n. 2, article no. 47 How to Cite? |
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. |
Persistent Identifier | http://hdl.handle.net/10722/325479 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Hofert, Marius | - |
dc.date.accessioned | 2023-02-27T07:33:39Z | - |
dc.date.available | 2023-02-27T07:33:39Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Risks, 2020, v. 8, n. 2, article no. 47 | - |
dc.identifier.uri | http://hdl.handle.net/10722/325479 | - |
dc.description.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. | - |
dc.language | eng | - |
dc.relation.ispartof | Risks | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Bootstrap | - |
dc.subject | Computational risk management | - |
dc.subject | Implementation | - |
dc.subject | R | - |
dc.subject | Rearrangement algorithm | - |
dc.subject | Worst value-at-risk allocation | - |
dc.title | Implementing the rearrangement algorithm: An example from computational risk management | - |
dc.type | Article | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.3390/risks8020047 | - |
dc.identifier.scopus | eid_2-s2.0-85085915120 | - |
dc.identifier.volume | 8 | - |
dc.identifier.issue | 2 | - |
dc.identifier.spage | article no. 47 | - |
dc.identifier.epage | article no. 47 | - |
dc.identifier.eissn | 2227-9091 | - |
dc.identifier.isi | WOS:000551226600016 | - |