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Applications of Fuzzy Regression in Actuarial Analysis

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  • Jorge De Andrés Sánchez
  • Antonio Terceño Gómez

Abstract

In this article, we propose several applications of fuzzy regression techniques for actuarial problems. Our main analysis is motivated, on the one hand, by the fact that several articles in the financial and actuarial literature suggest using fuzzy numbers to model interest rate uncertainty but do not explain how to quantify these rates with fuzzy numbers. Likewise, actuarial literature has recently focused some of its attention in analyzing the Term Structure of Interest Rates (TSIR) because this is a key instrument for pricing insurance contracts. With these two ideas in mind, we show that fuzzy regression is suitable for adjusting the TSIR and discuss how to apply a fuzzy TSIR when pricing life insurance contracts and property‐liability policies. Finally, we reflect on other actuarial applications of fuzzy regression and develop with this technique the London Chain Ladder Method for obtaining Incurred But Not Reported Reserves.

Suggested Citation

  • Jorge De Andrés Sánchez & Antonio Terceño Gómez, 2003. "Applications of Fuzzy Regression in Actuarial Analysis," Journal of Risk & Insurance, The American Risk and Insurance Association, vol. 70(4), pages 665-699, December.
    • Handle: RePEc:bla:jrinsu:v:70:y:2003:i:4:p:665-699
    DOI: 10.1046/j.0022-4367.2003.00070.x
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    References listed on IDEAS

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    1. Chambers, Donald R. & Carleton, Willard T. & Waldman, Donald W., 1984. "A New Approach to Estimation of the Term Structure of Interest Rates," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 19(3), pages 233-252, September.
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    Cited by:

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    2. de Andres-Sanchez, Jorge, 2007. "Claim reserving with fuzzy regression and Taylor's geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 40(1), pages 145-163, January.
    3. Shapiro, Arnold F., 2004. "Fuzzy logic in insurance," Insurance: Mathematics and Economics, Elsevier, vol. 35(2), pages 399-424, October.
    4. Morillas, Antonio & Díaz, Bárbara, 2007. "Qualitative Answering Surveys And Soft Computing," Fuzzy Economic Review, International Association for Fuzzy-set Management and Economy (SIGEF), vol. 0(1), pages 3-19, May.
    5. Berry-Stölzle, Thomas R. & Koissi, Marie-Claire & Shapiro, Arnold F., 2010. "Detecting fuzzy relationships in regression models: The case of insurer solvency surveillance in Germany," Insurance: Mathematics and Economics, Elsevier, vol. 46(3), pages 554-567, June.
    6. Mbairadjim Moussa, A. & Sadefo Kamdem, J. & Shapiro, A.F. & Terraza, M., 2014. "CAPM with fuzzy returns and hypothesis testing," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 40-57.
    7. Brychykova, A., 2019. "Capital Asset Pricing Model Using Fuzzy Data and Application for the Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 43(3), pages 58-77.
    8. Apaydin, Aysen & Baser, Furkan, 2010. "Hybrid fuzzy least-squares regression analysis in claims reserving with geometric separation method," Insurance: Mathematics and Economics, Elsevier, vol. 47(2), pages 113-122, October.
    9. Koissi, Marie-Claire & Shapiro, Arnold F., 2006. "Fuzzy formulation of the Lee-Carter model for mortality forecasting," Insurance: Mathematics and Economics, Elsevier, vol. 39(3), pages 287-309, December.
    10. G. Maarten Bonnema, 2011. "Insight, innovation, and the big picture in system design," Systems Engineering, John Wiley & Sons, vol. 14(3), pages 223-238, September.
    11. Heberle, Jochen & Thomas, Anne, 2014. "Combining chain-ladder claims reserving with fuzzy numbers," Insurance: Mathematics and Economics, Elsevier, vol. 55(C), pages 96-104.

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