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Bayesian Estimation for the Markov-Modulated Diffusion Risk Model

  • F. Baltazar-LariosEmail author
  • Luz Judith R. Esparza
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 301)

Abstract

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.

Keywords

Ruin probability Bayesian estimation Markov-modulated 

Notes

Acknowledgements

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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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Facultad de CienciasUniversidad Nacional Autónoma de MéxicoMexico CityMexico
  2. 2.Catedra CONACyTUniversidad Autonoma ChapingoTexcocoMexico

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