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Estimating the Volatility of Non-Life Premium Risk Under Solvency II: Discussion of Danish Fire Insurance Data

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  • Rocco Roberto Cerchiara

    (Department of Economics, Statistics and Finance “Giovanni Anania”, University of Calabria, 87036 Arcavacata di Rende (CS), Italy)

  • Francesco Acri

    (Independent Researcher, 20135 Milan, Italy)

Abstract

We studied the volatility assumption of non-life premium risk under the Solvency II Standard Formula and developed an empirical model on real data, the Danish fire insurance data. Our empirical model accomplishes two things. Primarily, compared to the present literature, this paper innovates the fitting of Danish fire insurance data using a composite model with a random threshold. Secondly we prove, by fitting the Danish fire insurance data, that for large insurance companies the volatility of the standard formula is higher than the volatility estimated with internal models such as composite models, also taking into account the dependence between attritional and large claims.

Suggested Citation

  • Rocco Roberto Cerchiara & Francesco Acri, 2020. "Estimating the Volatility of Non-Life Premium Risk Under Solvency II: Discussion of Danish Fire Insurance Data," Risks, MDPI, vol. 8(3), pages 1-19, July.
    • Handle: RePEc:gam:jrisks:v:8:y:2020:i:3:p:74-:d:381072
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    References listed on IDEAS

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    1. M. Concepcion Ausin & Michael P. Wiper & Rosa E. Lillo, 2009. "Bayesian estimation of finite time ruin probabilities," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 25(6), pages 787-805, November.
    2. Guillotte, Simon & Perron, Francois & Segers, Johan, 2011. "Non-parametric Bayesian inference on bivariate extremes," LIDAM Reprints ISBA 2011011, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Pigeon, Mathieu & Denuit, Michel, 2011. "Composite Lognormal-Pareto model with random threshold," LIDAM Reprints ISBA 2011020, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    4. Mathieu Pigeon & Michel Denuit, 2011. "Composite Lognormal–Pareto model with random threshold," Scandinavian Actuarial Journal, Taylor & Francis Journals, vol. 2011(3), pages 177-192.
    5. Arthur Charpentier & Abder Oulidi, 2010. "Beta kernel quantile estimators of heavy-tailed loss distributions," Post-Print halshs-00425566, HAL.
    6. Drees, Holger & Müller, Peter, 2008. "Fitting and validation of a bivariate model for large claims," Insurance: Mathematics and Economics, Elsevier, vol. 42(2), pages 638-650, April.
    7. Simon Guillotte & François Perron & Johan Segers, 2011. "Non‐parametric Bayesian inference on bivariate extremes," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 73(3), pages 377-406, June.
    8. Weron, Rafał & Burnecki, Krzysztof, 2004. "Modeling the risk process in the XploRe computing environment," Papers 2004,08, Humboldt University of Berlin, Center for Applied Statistics and Economics (CASE).
    9. Marcello Galeotti, 2015. "Computing the distribution of the sum of dependent random variables via overlapping hypercubes," Decisions in Economics and Finance, Springer;Associazione per la Matematica, vol. 38(2), pages 231-255, October.
    10. Gauss Cordeiro & Saralees Nadarajah & Edwin Ortega, 2012. "The Kumaraswamy Gumbel distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 21(2), pages 139-168, June.
    11. Resnick, Sidney I., 1997. "Discussion of the Danish Data on Large Fire Insurance Losses," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 139-151, May.
    12. Esmaeili, Habib & Klüppelberg, Claudia, 2010. "Parameter estimation of a bivariate compound Poisson process," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 224-233, October.
    13. McNeil, Alexander J., 1997. "Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory," ASTIN Bulletin, Cambridge University Press, vol. 27(1), pages 117-137, May.
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