Risk Analysis and Estimation of a Bimodal Heavy-Tailed Burr XII Model in Insurance Data: Exploring Multiple Methods and Applications

Actuarial risks can be analyzed using heavy-tailed distributions, which provide adequate risk assessment. Key risk indicators, such as value-at-risk, tailed-value-at-risk (conditional tail expectation), tailed-variance, tailed-mean-variance, and mean excess loss function, are commonly used to evalua...

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Published inMathematics (Basel) Vol. 11; no. 9; p. 2179
Main Authors Yousof, Haitham M., Ansari, S. I., Tashkandy, Yusra, Emam, Walid, Ali, M. Masoom, Ibrahim, Mohamed, Alkhayyat, Salwa L.
Format Journal Article
LanguageEnglish
Published Basel MDPI AG 01.05.2023
Subjects
Burr XII distribution
Cramer-Von-Mises
Data analysis
Indicators
Insurance
Insurance claims
Insurance industry
Insurance policies
Insurance premiums
Kaplan-Meier
Least squares
Literature reviews
Mathematics
maximum likelihood
Maximum likelihood estimation
Methods
Monte Carlo method
ordinary least square
Policyholders
Probability distribution
Random variables
Risk analysis
Risk assessment
Risk exposure
Simulation methods
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Summary:Actuarial risks can be analyzed using heavy-tailed distributions, which provide adequate risk assessment. Key risk indicators, such as value-at-risk, tailed-value-at-risk (conditional tail expectation), tailed-variance, tailed-mean-variance, and mean excess loss function, are commonly used to evaluate risk exposure levels. In this study, we analyze actuarial risks using these five indicators, calculated using four different estimation methods: maximum likelihood, ordinary least square, weighted least square, and Cramer-Von-Mises. To achieve our main goal, we introduce and study a new distribution. Monte Carlo simulations are used to assess the performance of all estimation methods. We provide two real-life datasets with two applications to compare the classical methods and demonstrate the importance of the proposed model, evaluated via the maximum likelihood method. Finally, we evaluate and analyze actuarial risks using the abovementioned methods and five actuarial indicators based on bimodal insurance claim payments data.
ISSN:2227-7390
2227-7390
DOI:10.3390/math11092179