Please use this identifier to cite or link to this item: http://hdl.handle.net/2445/156040
Title: Aggregation of dependent risks with heavy-tail distributions
Author: Guillén, Montserrat
Sarabia Alegría, José María
Prieto, Faustino
Jordá, Vanesa
Keywords: Risc (Assegurances)
Avaluació del risc
Distribució (Teoria de la probabilitat)
Mètode de Montecarlo
Risk (Insurance)
Risk assessment
Distribution (Probability theory)
Monte Carlo method
Issue Date: Dec-2019
Publisher: World Scientific Publishing
Abstract: Straightforward methods to evaluate risks arising from several sources are specially difficult when risk components are dependent and, even more if that dependence is strong in the tails. We give an explicit analytical expression for the probability distribution of the sum of non-negative losses that are tail-dependent. Our model allows dependence in the extremes of the marginal beta distributions. The proposed model is flexible in the choice of the parameters in the marginal distribution. The estimation using the method of moments is possible and the calculation of risk measures is easily done with a Monte Carlo approach. An illustration on data for insurance losses is presented.
Note: Versió postprint del document publicat a: https://doi.org/10.1142/S021848851940004X
It is part of: International Journal of Uncertainty Fuzziness and Knowledge-Based Systems, 2019, vol. 27, num. Supp 01, p. 77-88
URI: http://hdl.handle.net/2445/156040
Related resource: https://doi.org/10.1142/S021848851940004X
ISSN: 0218-4885
Appears in Collections:Articles publicats en revistes (Econometria, Estadística i Economia Aplicada)

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