A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models

Övgücan Karadağ Erdemir

Makale detayları

A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models

Yazarlar:

Övgücan Karadağ Erdemir

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.161-171

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311548

pISSN:

2651-396X

Anahtar kelimeler:

Özet:

 In this study, compensation payments for Turkish motor vehicles compulsory third-party liability insurance between 2018 and 2022 are modeled from a comparative perspective using regression-based and copula-based multivariate statistical methods. The assumption of gamma distribution for logarithmic compensation payment variables is carried out in both approaches. Bivariate gamma regression is established using the bivariate gamma distribution, and the mixture of experts, one of the machine learning techniques, is employed to form the mixture of bivariate gamma regressions. The bivariate copula regression and finite mixture of copula regression models are designed using the Gumbel and Frank copula functions. The computational analyses were conducted using the mvClaim package in R. Based on the comparison of model results, a mixture of copula-based models is found to be more suitable for the multivariate modeling of insurance compensation payments.

 In this study, compensation payments for Turkish motor vehicles compulsory third-party liability insurance between 2018 and 2022 are modeled from a comparative perspective using regression-based and copula-based multivariate statistical methods. The assumption of gamma distribution for logarithmic compensation payment variables is carried out in both approaches. Bivariate gamma regression is established using the bivariate gamma distribution, and the mixture of experts, one of the machine learning techniques, is employed to form the mixture of bivariate gamma regressions. The bivariate copula regression and finite mixture of copula regression models are designed using the Gumbel and Frank copula functions. The computational analyses were conducted using the mvClaim package in R. Based on the comparison of model results, a mixture of copula-based models is found to be more suitable for the multivariate modeling of insurance compensation payments.

 In this study, compensation payments for Turkish motor vehicles compulsory third-party liability insurance between 2018 and 2022 are modeled from a comparative perspective using regression-based and copula-based multivariate statistical methods. The assumption of gamma distribution for logarithmic compensation payment variables is carried out in both approaches. Bivariate gamma regression is established using the bivariate gamma distribution, and the mixture of experts, one of the machine learning techniques, is employed to form the mixture of bivariate gamma regressions. The bivariate copula regression and finite mixture of copula regression models are designed using the Gumbel and Frank copula functions. The computational analyses were conducted using the mvClaim package in R. Based on the comparison of model results, a mixture of copula-based models is found to be more suitable for the multivariate modeling of insurance compensation payments.

 In this study, compensation payments for Turkish motor vehicles compulsory third-party liability insurance between 2018 and 2022 are modeled from a comparative perspective using regression-based and copula-based multivariate statistical methods. The assumption of gamma distribution for logarithmic compensation payment variables is carried out in both approaches. Bivariate gamma regression is established using the bivariate gamma distribution, and the mixture of experts, one of the machine learning techniques, is employed to form the mixture of bivariate gamma regressions. The bivariate copula regression and finite mixture of copula regression models are designed using the Gumbel and Frank copula functions. The computational analyses were conducted using the mvClaim package in R. Based on the comparison of model results, a mixture of copula-based models is found to be more suitable for the multivariate modeling of insurance compensation payments.

Makale detayları

A Comparative Perspective on Multivariate Modeling of Insurance Compensation Payments with Regression-Based and Copula-Based Models

Yazarlar:

Övgücan Karadağ Erdemir

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.161-171

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311548

pISSN:

2651-396X

Bivariate Gamma Distribution

Copula

Generalized Linear Model

Copula Regression

Insurance Compensation Payments

Machine Learning Techniques

Mixture of Experts Model

Sayı

Makale detayları

Yazarlar: Övgücan Karadağ Erdemir

Yayıncı: Istanbul University

Ana Başlıklar: Ekonometri (Diğer)

Dergi: EKOIST Journal of Econometrics and Statistics Issue 39, pp.161-171

Belge Tipi: Makale

Yayın Tarihi: 12/2023

Belge Dili: İngilizce

Document ID (Org No.): 2001311548

pISSN: 2651-396X

Sayı

Yazarlar:

Övgücan Karadağ Erdemir

Yayıncı:

Istanbul University

Ana Başlıklar:

Ekonometri (Diğer)

Dergi:

EKOIST Journal of Econometrics and Statistics Issue 39, pp.161-171

Belge Tipi:

Makale

Yayın Tarihi:

12/2023

Belge Dili:

İngilizce

Document ID (Org No.):

2001311548

pISSN:

2651-396X