Bayesian Estimation for the Markov-Modulated Diffusion Risk Model
We consider the Markov-modulated diffusion risk model in which the claim inter-arrivals, claim sizes, premiums, and volatility diffusion process are influenced by an underlying Markov jump process. We propose a method for obtaining the maximum likelihood estimators of its parameters using a Markov chain Monte Carlo algorithm. We present simulation studies to estimate the ruin probability in finite time using the estimators obtained with the method proposed in this paper.
KeywordsRuin probability Bayesian estimation Markov-modulated
Luz Judith Rodriguez Esparza is supported by a Catedra CONACyT. The research of F. Baltazar-Larios was supported by PAPIIT-IA105716. Both authors are thankful to the reviewers for their invaluable comments and suggestions, which improve the paper substantially.
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