| 正文 | 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). |
