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A note on mixture prior distributions with applications in actuarial statistic/Sobre las Distribuciones a Priori Mixtas con Aplicaciones en la Estadística Actuarial

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  • GÓMEZ-DÉNIZ, E.

    () (Department of Quantitative Methods, University of Las Palmas de Gran Canaria Author-NameVÁZQUEZ-POLO, F. J, Department of Quantitative Methods, University of Las Palmas de Gran Canaria 35017-Las Palmas de G.C., Spain; 18011-Granada, Spain. Author-Name PÉREZ-SÁNCHEZ, J.M.)

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    Abstract

    The paper presents a Bayesian sensitivity analysis for the credibility theory related to the net premium principle. Thus, the mixture model in prior distribution is used for the separation of subpopulations. This construction is adapted to the usual robust Bayesian results and these are exploited to obtain lower and upper bounds for the premium. Two realistic examples illustrate the application of this method. El artículo presenta un análisis de sensibilidad Bayesiano aplicando el principio de prima neta para el cálculo de la prima. Para separar a la población en dos colectivos, se utiliza un modelo de mixturas de distribuciones a priori. Con este planteamiento, se realiza un análisis Bayesiano robusto obteniendo las cotas inferiores y superiores de la prima. Por último, se ilustran los resultados obtenidos con dos ejemplos numéricos.

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    Bibliographic Info

    Article provided by Estudios de Economía Aplicada in its journal Estudios de Economía Aplicada.

    Volume (Year): 22 (2004)
    Issue (Month): (Agosto)
    Pages: 372 (15 páginas)

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    Handle: RePEc:lrk:eeaart:22_2_14

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    Related research

    Keywords: Bayesian Robustness; Bimodal Distribution; Credibility Theory; Net Premium Principle; Good-risk/Bad-risk; å -Contaminated Classes of Priors.;

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    References

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    1. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-31, February.
    2. Bera, A.K & Lee, S., 1991. "Information Matrix Test, Parameter Heterogeneity and Arch : A Synthesis," Papers 9154, Tilburg - Center for Economic Research.
    3. Paolo Zaffaroni, 2000. "Contemporaneous Aggregation of GARCH Processes," STICERD - Econometrics Paper Series /2000/378, Suntory and Toyota International Centres for Economics and Related Disciplines, LSE.
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    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-70, March.
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    7. Sharpe, William F., 1967. "Portfolio Analysis," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 2(02), pages 76-84, June.
    8. Wolff, Christian C. P., 1988. "Autoregressive conditional heteroscedasticity: A comparison of ARCH and random coefficient models," Economics Letters, Elsevier, vol. 27(2), pages 141-143.
    9. Bjorn Hansson & Peter Hordahl, 1998. "Testing the conditional CAPM using multivariate GARCH-M," Applied Financial Economics, Taylor and Francis Journals, vol. 8(4), pages 377-388.
    10. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-68, July.
    11. Rabemananjara, R & Zakoian, J M, 1993. "Threshold Arch Models and Asymmetries in Volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 8(1), pages 31-49, Jan.-Marc.
    12. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
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