Quotation Miljkovic, Tatjana, Grün, Bettina. 2021. Using Model Averaging to Determine Suitable Risk Measure Estimates. North American Actuarial Journal. 25 (4), 562-579.




Recent research in loss modeling resulted in a growing number of classes of statistical models as well as additional models being proposed within each class. Empirical results indicate that a range of models within or between model classes perform similarly well, as measured by goodness-of-fit or information criteria, when fitted to the same data set. This leads to model uncertainty and makes model selection a challenging task. This problem is particularly virulent if the resulting risk measures vary greatly between and within the model classes. We propose an approach to estimate risk measures that accounts for model selection uncertainty based on model averaging. We exemplify the application of the approach considering the class of composite models. This application considers 196 different left-truncated composite models previously used in the literature for loss modeling and arrives at point estimates for the risk measures that take model uncertainty into account. A simulation study highlights the benefits of this approach. The data set on Norwegian fire losses is used to illustrate the proposed methodology.


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Publication's profile

Status of publication Published
Affiliation WU
Type of publication Journal article
Journal North American Actuarial Journal
WU-Journal-Rating new VW-C
Language English
Title Using Model Averaging to Determine Suitable Risk Measure Estimates
Volume 25
Number 4
Year 2021
Page from 562
Page to 579
Reviewed? Y
URL https://www.tandfonline.com/doi/full/10.1080/10920277.2021.1911668
DOI https://doi.org/10.1080/10920277.2021.1911668
Open Access N


Grün, Bettina (Details)
Miljkovic, Tatjana (Miami University, United States/USA)
Institute for Statistics and Mathematics IN (Details)
Research areas (ÖSTAT Classification 'Statistik Austria')
1105 Computer software (Details)
1113 Mathematical statistics (Details)
5701 Applied statistics (Details)
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