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- Publisher Website: 10.1016/j.insmatheco.2018.10.002
- Scopus: eid_2-s2.0-85055756576
- WOS: WOS:000456756100006
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Article: Forecasting compositional risk allocations
| Title | Forecasting compositional risk allocations |
|---|---|
| Authors | |
| Keywords | Aitchison geometry Capital allocation Dynamic risk management Isometric logratio Simplex |
| Issue Date | 2019 |
| Citation | Insurance: Mathematics and Economics, 2019, v. 84, p. 79-86 How to Cite? |
| Abstract | We analyse models for panel data that arise in risk allocation problems, when a given set of sources are the cause of an aggregate risk value. We focus on the modelling and forecasting of proportional contributions to risk over time. Compositional data methods are proposed and the time-series regression is flexible to incorporate external information from other variables. We guarantee that projected proportional contributions add up to 100%, and we introduce a method to generate confidence regions with the same restriction. An illustration is provided for risk capital allocations. |
| Persistent Identifier | http://hdl.handle.net/10722/328752 |
| ISSN | 2023 Impact Factor: 1.9 2023 SCImago Journal Rankings: 1.113 |
| ISI Accession Number ID |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Boonen, Tim J. | - |
| dc.contributor.author | Guillen, Montserrat | - |
| dc.contributor.author | Santolino, Miguel | - |
| dc.date.accessioned | 2023-07-22T06:23:38Z | - |
| dc.date.available | 2023-07-22T06:23:38Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | Insurance: Mathematics and Economics, 2019, v. 84, p. 79-86 | - |
| dc.identifier.issn | 0167-6687 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/328752 | - |
| dc.description.abstract | We analyse models for panel data that arise in risk allocation problems, when a given set of sources are the cause of an aggregate risk value. We focus on the modelling and forecasting of proportional contributions to risk over time. Compositional data methods are proposed and the time-series regression is flexible to incorporate external information from other variables. We guarantee that projected proportional contributions add up to 100%, and we introduce a method to generate confidence regions with the same restriction. An illustration is provided for risk capital allocations. | - |
| dc.language | eng | - |
| dc.relation.ispartof | Insurance: Mathematics and Economics | - |
| dc.subject | Aitchison geometry | - |
| dc.subject | Capital allocation | - |
| dc.subject | Dynamic risk management | - |
| dc.subject | Isometric logratio | - |
| dc.subject | Simplex | - |
| dc.title | Forecasting compositional risk allocations | - |
| dc.type | Article | - |
| dc.description.nature | link_to_subscribed_fulltext | - |
| dc.identifier.doi | 10.1016/j.insmatheco.2018.10.002 | - |
| dc.identifier.scopus | eid_2-s2.0-85055756576 | - |
| dc.identifier.volume | 84 | - |
| dc.identifier.spage | 79 | - |
| dc.identifier.epage | 86 | - |
| dc.identifier.isi | WOS:000456756100006 | - |