Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/167843
Authors: 
Dassios, Angelos
Zhao, Hongbiao
Year of Publication: 
2014
Citation: 
[Journal:] Risks [ISSN:] 2227-9091 [Volume:] 2 [Year:] 2014 [Issue:] 4 [Pages:] 434-455
Abstract: 
We introduce a bivariate Markov chain counting process with contagion for modelling the clustering arrival of loss claims with delayed settlement for an insurance company. It is a general continuous-time model framework that also has the potential to be applicable to modelling the clustering arrival of events, such as jumps, bankruptcies, crises and catastrophes in finance, insurance and economics with both internal contagion risk and external common risk. Key distributional properties, such as the moments and probability generating functions, for this process are derived. Some special cases with explicit results and numerical examples and the motivation for further actuarial applications are also discussed. The model can be considered a generalisation of the dynamic contagion process introduced by Dassios and Zhao (2011).
Subjects: 
risk model
contagion risk
bivariate point process
Markov chain model
discretised dynamic contagion process
dynamic contagion process
Persistent Identifier of the first edition: 
Creative Commons License: 
http://creativecommons.org/licenses/by/4.0/
Document Type: 
Article
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