On finite-time ruin probabilities in a generalized dual risk model with dependence

Dimitrova, D. S., Kaishev, V. K. & Zhao, S. (2015). On finite-time ruin probabilities in a generalized dual risk model with dependence. European Journal of Operational Research, 242(1), pp. 134-148. doi: 10.1016/j.ejor.2014.10.007

[img]
Preview
Text - Accepted Version
Download (381kB) | Preview
[img]
Preview
Text (Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International Licence) - Other
Download (1MB) | Preview

Abstract

In this paper, we study the finite-time ruin probability in a reasonably generalized dual risK model, where we assume any non-negative non-decreasing cumulative operational cost function and arbitrary capital gains arrival process. Establishing an enlightening link between this dual risk model and its corresponding insurance risk model, explicit expressions for the finite-time survival probability in the dual risk model are obtained under various general assumptions for the distribution of the capital gains. In order to make the model more realistic and general, different dependence structures among capital gains and inter-arrival times and between both are also introduced and corresponding ruin probability expressions are also given. The concept of alarm time, as introduced in Das and Kratz (2012), is applied to the dual risk model within the context of risk capital allocation. Extensive numerical illustrations are provided.

Item Type: Article
Additional Information: © 2015, Elsevier. Licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International http://creativecommons.org/licenses/by-nc-nd/4.0/
Uncontrolled Keywords: dual risk model, finite-time ruin probability, dependent risk modelling, capital allocation, alarm time, (exponential) classical Appell polynomials
Subjects: H Social Sciences > HG Finance
Divisions: Cass Business School > Faculty of Finance
URI: http://openaccess.city.ac.uk/id/eprint/12426

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics