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Bayesian solvency analysis with autocorrelated observations

Author

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  • M. Mendoza
  • L. E. Nieto‐Barajas

Abstract

Most financial institutions are required to comply with a minimum capital rule in order to face their obligations during a given period of time. Due to the random nature of the financial flows involved, the problem of assessing the amount of capital required must be analysed within a stochastic framework and the solution can be reduced to the estimation of a selected quantile. Given the financial impact of a specific capital requirement, a proper and careful choice of the underlying model is of great relevance. Here, we address the problem for insurance companies by proposing an autocorrelated model to describe the relative severity after a suitable transformation and compare the results with those of a model, which assumes independence among observations. We undertake a full Bayesian analysis and derive the reference priors for the models. Results are illustrated with a real data set from the Mexican insurance industry. Copyright © 2006 John Wiley & Sons, Ltd.

Suggested Citation

  • M. Mendoza & L. E. Nieto‐Barajas, 2006. "Bayesian solvency analysis with autocorrelated observations," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 22(2), pages 169-180, March.
    • Handle: RePEc:wly:apsmbi:v:22:y:2006:i:2:p:169-180
    DOI: 10.1002/asmb.626
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    Cited by:

    1. Luis Nieto-Barajas & Eduardo Gutiérrez-Peña, 2022. "General dependence structures for some models based on exponential families with quadratic variance functions," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 699-716, September.

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