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Risk measures in a quantile regression credibility framework with Fama/French data applications

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  • Pitselis, Georgios

Abstract

In this paper we extend the idea of embedding the classical credibility model into risk measures, as was presented by Pitselis (2016), to the idea of embedding regression credibility into risk measures. The resulting credible regression risk measures capture the risk of individual insurer’s contract (in finance, the individual asset return portfolio) as well as the portfolio risk consisting of several similar but not identical contracts (in finance, several similar portfolios of asset returns), which are grouped together to share the risk. In insurance, credibility plays a special role of spreading the risk. In financial terminology, credibility plays a special role of diversification of risk. For each model, regression credibility models are established and the robustness of these models is investigated. Applications to Fama/French financial portfolio data are also presented.

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  • Pitselis, Georgios, 2017. "Risk measures in a quantile regression credibility framework with Fama/French data applications," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 122-134.
    • Handle: RePEc:eee:insuma:v:74:y:2017:i:c:p:122-134
    DOI: 10.1016/j.insmatheco.2017.02.008
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    1. James W. Taylor, 2008. "Using Exponentially Weighted Quantile Regression to Estimate Value at Risk and Expected Shortfall," Journal of Financial Econometrics, Oxford University Press, vol. 6(3), pages 382-406, Summer.
    2. Kim, Joseph H.T. & Jeon, Yongho, 2013. "Credibility theory based on trimming," Insurance: Mathematics and Economics, Elsevier, vol. 53(1), pages 36-47.
    3. Neykov, N.M. & Filzmoser, P. & Neytchev, P.N., 2012. "Robust joint modeling of mean and dispersion through trimming," Computational Statistics & Data Analysis, Elsevier, vol. 56(1), pages 34-48, January.
    4. Moshe Buchinsky, 1998. "Recent Advances in Quantile Regression Models: A Practical Guideline for Empirical Research," Journal of Human Resources, University of Wisconsin Press, vol. 33(1), pages 88-126.
    5. Koenker,Roger, 2005. "Quantile Regression," Cambridge Books, Cambridge University Press, number 9780521845731.
    6. Pitselis, Georgios, 2016. "Credible risk measures with applications in actuarial sciences and finance," Insurance: Mathematics and Economics, Elsevier, vol. 70(C), pages 373-386.
    7. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    8. Pitselis, Georgios, 2013. "Quantile credibility models," Insurance: Mathematics and Economics, Elsevier, vol. 52(3), pages 477-489.
    9. Powell, James L., 1986. "Censored regression quantiles," Journal of Econometrics, Elsevier, vol. 32(1), pages 143-155, June.
    10. Furman, Edward & Landsman, Zinoviy, 2006. "Tail Variance Premium with Applications for Elliptical Portfolio of Risks," ASTIN Bulletin, Cambridge University Press, vol. 36(2), pages 433-462, November.
    11. Kudryavtsev, Andrey A., 2009. "Using quantile regression for rate-making," Insurance: Mathematics and Economics, Elsevier, vol. 45(2), pages 296-304, October.
    12. Fama, Eugene F & French, Kenneth R, 1992. "The Cross-Section of Expected Stock Returns," Journal of Finance, American Finance Association, vol. 47(2), pages 427-465, June.
    13. Landsman, Zinoviy & Valdez, Emiliano A., 2005. "Tail Conditional Expectations for Exponential Dispersion Models," ASTIN Bulletin, Cambridge University Press, vol. 35(1), pages 189-209, May.
    14. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: theory," Insurance: Mathematics and Economics, Elsevier, vol. 31(1), pages 3-33, August.
    15. Dhaene, J. & Denuit, M. & Goovaerts, M. J. & Kaas, R. & Vyncke, D., 2002. "The concept of comonotonicity in actuarial science and finance: applications," Insurance: Mathematics and Economics, Elsevier, vol. 31(2), pages 133-161, October.
    16. Maritz, J. S., 1989. "Linear empirical Bayes estimation of quantiles," Statistics & Probability Letters, Elsevier, vol. 8(1), pages 59-65, May.
    17. Wang, Shaun S. & Young, Virginia R. & Panjer, Harry H., 1997. "Axiomatic characterization of insurance prices," Insurance: Mathematics and Economics, Elsevier, vol. 21(2), pages 173-183, November.
    18. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    19. Fama, Eugene F. & French, Kenneth R., 1993. "Common risk factors in the returns on stocks and bonds," Journal of Financial Economics, Elsevier, vol. 33(1), pages 3-56, February.
    20. Philippe Artzner & Freddy Delbaen & Jean‐Marc Eber & David Heath, 1999. "Coherent Measures of Risk," Mathematical Finance, Wiley Blackwell, vol. 9(3), pages 203-228, July.
    21. Włodzimierz Ogryczak & Mariusz Zawadzki, 2002. "Conditional Median: A Parametric Solution Concept for Location Problems," Annals of Operations Research, Springer, vol. 110(1), pages 167-181, February.
    22. James W. Taylor, 2008. "Estimating Value at Risk and Expected Shortfall Using Expectiles," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 231-252, Spring.
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    Cited by:

    1. Wei Wang & Limin Wen & Zhixin Yang & Quan Yuan, 2020. "Quantile Credibility Models with Common Effects," Risks, MDPI, vol. 8(4), pages 1-10, September.
    2. Cheung, Ka Chun & Yam, Sheung Chi Phillip & Zhang, Yiying, 2022. "Satisficing credibility for heterogeneous risks," European Journal of Operational Research, Elsevier, vol. 298(2), pages 752-768.
    3. Pitselis, Georgios, 2020. "Multi-stage nested classification credibility quantile regression model," Insurance: Mathematics and Economics, Elsevier, vol. 92(C), pages 162-176.
    4. Chen, Yongzhao & Cheung, Ka Chun & Choi, Hugo Ming Cheung & Yam, Sheung Chi Phillip, 2020. "Evolutionary credibility risk premium," Insurance: Mathematics and Economics, Elsevier, vol. 93(C), pages 216-229.

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