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Çapraz Sınıflama Kredibilite Modeli İçin R Paketi: Takipteki Kredilerle İlgili Uygulama

Year 2023, Volume: 8 Issue: 2, 132 - 148, 10.07.2023
https://doi.org/10.23834/isrjournal.1253490

Abstract

References

  • Altan S.T., Özdemir M. & Ebegil M. cccm: An R Package for Crossed Classification Credibility Model, 2022. Title. [Avaible online at: https://cran.r-project.org/web/packages/cccm/index.html], R package version 1.2.
  • Antonio, K., & Beirlant, J. (2007). Applications of generalized linear mixed models in actuarial statistics. Insurance: Mathematics and Economics. 40, 58–76
  • Bailey, A.L., (1950). Credibility Procedures - La Place's Generalization of Bayes' Rule and the Combination of Collateral Knowledge with Observed Data. New York State Insurance Department. 37, 7-23, 94-115.
  • Bozikas, A., & Pitselis, G. (2020). Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data. Insurance: Mathematics and Economics.. 93, 353-368.
  • Bozikas, A. E. (2019). Actuarial models in demography. (Doctoral dissertation, Piraeus University).
  • Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin: The Journal of the IAA. 4(3), 199-207.
  • Bühlmann, H. & Straub, E., (1970) Glaubwürdigkeit für Schadensätze. Bulletin of the Swiss Association 70, 111-133. English translation by C.E. Brooks.
  • Dannenburg, D., (1995). “Crossed classification credibility models”, In. Trans. 25th Int. Congress of Actuaries. 4: 1–36 Dannenburg, D. R., Kaas, R., & Goovaerts, M. J. (1996). Practical actuarial credibility models. Institute of Actuarial Science and Econometrics of the University of Amsterdam.
  • Fellingham, G. W., Dennis Tolley, H., & Herzog, T. N. (2005). Comparing credibility estimates of health insurance claims costs. North American Actuarial Journal. 9(1), 1-12.
  • Frees, E. W., Young, V. R., & Luo, Y. (1999). A longitudinal data analysis interpretation of credibility models. Insurance: Mathematics and Economics. 24(3), 229-247.
  • Frees, E. W., Young, V. R., & Luo, Y. (2001). Case studies using panel data models. North American Actuarial Journal. 5(4), 24-42.
  • Fung, W. K., & Xu, X. (2008). Estimation of Structural Parameters in Crossed Classification Credibility Model Using Linear Mixed Models. In COMPSTAT (241-251). Physica-Verlag HD.
  • Goulet, V. (2001). A generalized crossed classification credibility model. Insurance: Mathematics and Economics. 28(2), 205-216.
  • Hachemeister, C. (1975). Credibility for regression models with application to trend (reprint). Credibility: Theory and Applications. Edited by P. Kahn. New York: Academic Press, Inc. 307-48.
  • Jewell, W.S. (1975) The Use of Collateral Data in Credibility Theory: A Hierarchical Model. Giornale dell'Istituto Italiano degli Attuari. 38:1-16.
  • Klugman, S. (1987). Credibility for classification ratemaking via the hierarchical normal linear model. In Proceedings of the Casualty Actuarial Society. Vol. 74, 272-321.
  • Longley-Cook, L. H. (1962). An introduction to credibility theory. Casualty Actuarial Society. 49, 194-221.
  • Mowbray, A. H. (1914). How extensive a payroll exposure is necessary to give a dependable pure premium. In Proceedings of the Casualty Actuarial society. Vol. 1, No. 1, 24-30.
  • Perryman, F.S., (1932). Some Notes on Credibility. PCAS, 19, 65-84.
  • Poon, J., & Lu, Y. (2015). A spatial cross-sectional credibility model with dependence among risks. North American Actuarial Journal. 19(4), 289-310.
  • Rosenberg, M. A., & Farrell, P. M. (2008). Predictive modeling of costs for a chronic disease with acute high-cost episodes. North American Actuarial Journal. 12(1), 1-19.
  • Šoltés, E., & Šoltésová, T. (2006). Application of Crossed Classification Credibility Model in Third-party Auto Insurance in Slovak Republic. Management information systems. 59.
  • Turkey Banking Regulation and Supervisory Board Banking Sector Data by Province Title. [Available online at: https://www.bddk.org.tr/BultenFinTurk], Retrieved on October 6, 2022
  • Wang, X. (2005). On the numerical evaluation of optimal variance components estimators in crossed classification credibility (Doctoral dissertation, Concordia University).
  • Wen, L. & Wu, X. (2011). The credibility estimator with general dependence structure over risks. Communications in Statistics-Theory and Methods, 40:1893-1910.
  • Whitney, A., The Theory of Experience Rating. Proceedings of the Casualty Actuarial Society, 4: 274–292 (1918).

An R Package For Crossed Classification Credibility Model: Application Regarding Non-Performing Loan

Year 2023, Volume: 8 Issue: 2, 132 - 148, 10.07.2023
https://doi.org/10.23834/isrjournal.1253490

Abstract

Credibility theory, which is used to determine the premium in the non-life branches of insurance, is a calculation method which is used for making weighted estimation of balanced allocation between past and recent period data. The procedure of weighting is done with the Z credibility factor. There are miscellaneous methods which are named as credibility models to determine Z value. One of these models is Crossed Classification Credibility Model, which is introduced by Dannenburg (1995). In this model, an insurance portfolio is subdivided by two qualitative risk factors, modeled in symmetrical way. Especially, this model offers an alternative method when data are unclassifiable hierarchically. Simultaneously, this model considers the joint and separates the effects of risk factors. To predict the premiums in this model, variance components are obtained by solving the linear equation system must be calculated. However, this system cannot be solved explicitly. Also, too many parameters must be calculated for the premium estimation. Here, calculation errors can occur, and it is very difficult to find the correct results. Moreover, there is no tool that can easily perform these operations on a computer. In this study, the R package cccm has been developed to calculate the structural parameters easily, quickly, and accurately for Crossed Classification Credibility Model. Package cccm explained step by step for the users interested in to solve Crossed Classification Credibility problems.

References

  • Altan S.T., Özdemir M. & Ebegil M. cccm: An R Package for Crossed Classification Credibility Model, 2022. Title. [Avaible online at: https://cran.r-project.org/web/packages/cccm/index.html], R package version 1.2.
  • Antonio, K., & Beirlant, J. (2007). Applications of generalized linear mixed models in actuarial statistics. Insurance: Mathematics and Economics. 40, 58–76
  • Bailey, A.L., (1950). Credibility Procedures - La Place's Generalization of Bayes' Rule and the Combination of Collateral Knowledge with Observed Data. New York State Insurance Department. 37, 7-23, 94-115.
  • Bozikas, A., & Pitselis, G. (2020). Incorporating crossed classification credibility into the Lee–Carter model for multi-population mortality data. Insurance: Mathematics and Economics.. 93, 353-368.
  • Bozikas, A. E. (2019). Actuarial models in demography. (Doctoral dissertation, Piraeus University).
  • Bühlmann, H. (1967). Experience rating and credibility. ASTIN Bulletin: The Journal of the IAA. 4(3), 199-207.
  • Bühlmann, H. & Straub, E., (1970) Glaubwürdigkeit für Schadensätze. Bulletin of the Swiss Association 70, 111-133. English translation by C.E. Brooks.
  • Dannenburg, D., (1995). “Crossed classification credibility models”, In. Trans. 25th Int. Congress of Actuaries. 4: 1–36 Dannenburg, D. R., Kaas, R., & Goovaerts, M. J. (1996). Practical actuarial credibility models. Institute of Actuarial Science and Econometrics of the University of Amsterdam.
  • Fellingham, G. W., Dennis Tolley, H., & Herzog, T. N. (2005). Comparing credibility estimates of health insurance claims costs. North American Actuarial Journal. 9(1), 1-12.
  • Frees, E. W., Young, V. R., & Luo, Y. (1999). A longitudinal data analysis interpretation of credibility models. Insurance: Mathematics and Economics. 24(3), 229-247.
  • Frees, E. W., Young, V. R., & Luo, Y. (2001). Case studies using panel data models. North American Actuarial Journal. 5(4), 24-42.
  • Fung, W. K., & Xu, X. (2008). Estimation of Structural Parameters in Crossed Classification Credibility Model Using Linear Mixed Models. In COMPSTAT (241-251). Physica-Verlag HD.
  • Goulet, V. (2001). A generalized crossed classification credibility model. Insurance: Mathematics and Economics. 28(2), 205-216.
  • Hachemeister, C. (1975). Credibility for regression models with application to trend (reprint). Credibility: Theory and Applications. Edited by P. Kahn. New York: Academic Press, Inc. 307-48.
  • Jewell, W.S. (1975) The Use of Collateral Data in Credibility Theory: A Hierarchical Model. Giornale dell'Istituto Italiano degli Attuari. 38:1-16.
  • Klugman, S. (1987). Credibility for classification ratemaking via the hierarchical normal linear model. In Proceedings of the Casualty Actuarial Society. Vol. 74, 272-321.
  • Longley-Cook, L. H. (1962). An introduction to credibility theory. Casualty Actuarial Society. 49, 194-221.
  • Mowbray, A. H. (1914). How extensive a payroll exposure is necessary to give a dependable pure premium. In Proceedings of the Casualty Actuarial society. Vol. 1, No. 1, 24-30.
  • Perryman, F.S., (1932). Some Notes on Credibility. PCAS, 19, 65-84.
  • Poon, J., & Lu, Y. (2015). A spatial cross-sectional credibility model with dependence among risks. North American Actuarial Journal. 19(4), 289-310.
  • Rosenberg, M. A., & Farrell, P. M. (2008). Predictive modeling of costs for a chronic disease with acute high-cost episodes. North American Actuarial Journal. 12(1), 1-19.
  • Šoltés, E., & Šoltésová, T. (2006). Application of Crossed Classification Credibility Model in Third-party Auto Insurance in Slovak Republic. Management information systems. 59.
  • Turkey Banking Regulation and Supervisory Board Banking Sector Data by Province Title. [Available online at: https://www.bddk.org.tr/BultenFinTurk], Retrieved on October 6, 2022
  • Wang, X. (2005). On the numerical evaluation of optimal variance components estimators in crossed classification credibility (Doctoral dissertation, Concordia University).
  • Wen, L. & Wu, X. (2011). The credibility estimator with general dependence structure over risks. Communications in Statistics-Theory and Methods, 40:1893-1910.
  • Whitney, A., The Theory of Experience Rating. Proceedings of the Casualty Actuarial Society, 4: 274–292 (1918).
There are 26 citations in total.

Details

Primary Language English
Journal Section Articles
Authors

Seda Tuğçe Altan 0000-0003-0431-5256

Muhlis Özdemir 0000-0002-4921-8209

Meral Ebegil 0000-0003-4798-3422

Publication Date July 10, 2023
Submission Date March 3, 2023
Published in Issue Year 2023 Volume: 8 Issue: 2

Cite

APA Altan, S. T., Özdemir, M., & Ebegil, M. (2023). An R Package For Crossed Classification Credibility Model: Application Regarding Non-Performing Loan. The Journal of International Scientific Researches, 8(2), 132-148. https://doi.org/10.23834/isrjournal.1253490