Research on the Estimation of The Scale of Catastrophe Insurance Funds in Various Provinces in China
A Case Study on Geological Disasters
DOI:
https://doi.org/10.54097/fbem.v10i2.10904Keywords:
Geological disasters, Cluster analysis, Generalized Pareto Distribution, Optimal reinsurance, Fund scale.Abstract
The catastrophe insurance fund, as an important means for the steady and robust development of catastrophe risk management, has attracted significant attention from various sectors of society. However, natural disasters in different regions of China vary significantly in terms of frequency and severity, the centralized management of a uniform catastrophe insurance fund severely affects the efficiency and fairness of catastrophe risk management. This study selects the geological disaster loss data from various provinces of mainland China from 2004 to 2021 as the research sample. Based on the characteristics of relative losses caused by geological disasters, the geological disaster losses in various provinces of China are divided into four categories using cluster analysis, and the Generalized Pareto Distribution (GPD) is utilized to describe the loss distributions of each category; The scale of geological disaster insurance funds in each province is estimated through the optimal reinsurance strategy based on the Conditional Value-at-Risk (CVaR) - Expected Premium principle; Provinces with similar characteristics of relative losses from geological disasters have significantly different requirements for the scale of catastrophe insurance funds. Compared to traditional catastrophe models, this study introduces cluster analysis to consider the heterogeneity of geological disaster occurrences. In this paper, a new approach to enhance the management of catastrophe risks by fitting the distribution of geological disaster losses more reasonably is provided.
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References
E. Kremer. More on the Probable Maximum Loss.Bltter Der Dgvfm, vol. 21 (1994) , p.319- 326.
Z.S. Ouyang. Fitting of Geological Hazard Loss Distribution and Risk Measurement. Statistical Research, vol. 11 (2011) , p.78-83.
L. Tian, P.Yao. Study on the Scale of Catastrophe Insurance Fund in China - Taking Earthquake Risk as an Example. Insurance Research, vol. 4 ( 2013) , p.13-21.
G. Geng, H.Y. Wang. Study on the Loss Distribution of Earthquake Catastrophes Based on the POT-GPD Model. Journal of Natural Disasters, vol. 3 (2016) , p.153-158.
L. Tian, Y.L. Wu, X.C. Shen. Estimation of Earthquake Catastrophe Insurance Fund Size Based on CVaR. Economic Review, vol. 4 (2016) , p.141-150.
L. Yang, S.W. Meng, T.T. Xu. Study on the Calculation of Catastrophe Insurance Fund Size in China. Statistical and Information Forum, vol. 3 (2022) , p.75-85.
B. Qiu, T.T. Xu. Regional Differences in the Funding Standards of Agricultural Catastrophic Risk Funds in China: Based on Provincial-level Crop Loss Data. Journal of Ningbo University (Humanities and Social Sciences), vol. 3 (2015) , p.74-78.
Y. Zhou, H.P. Tu. Design of Cross-Regional Typhoon Catastrophic Risk Insurance Fund. China Soft Science, vol. 6 (2017) , p.69-80.
L. Tian, N. Sun, C. Yang. Pareto Optimal Indemnity Ratio in Earthquake Index Insurance: A Case Study of EQ II Product. Insurance Research, vol. 6 (2019) , p.39-50.
Y.G. Li, Y. Dai, X.Q. He. Panel Data Clustering Method Based on Adaptive Weights. Systems Engineering Theory and Practice, vol. 2 (2013) , p.388-395.
J.J. Ma, G.F. Ma, F.Y. Bai, J.X. You, R. Lu. Analysis of Provincial Patent Output in China Based on Fuzzy C-means Panel Data Clustering. Systems Engineering Theory and Practice, vol. 9 (2015) , p.2304-2314.
Y.X. Liu. Research and Application of Panel Data Clustering Method Based on Dynamic Time Warping. Statistical Research, vol. 11(2016) , p.93-101.
Z.D. Wang, G.M. Deng. Discussion on Panel Data Clustering Method Based on Trend Distance. Statistics and Decision Making, vol. 8 (2019) , p.35-38.
A.J. Mcneil. Estimating the Tails of Loss Severity Distributions Using Extreme Value Theory.Astin Bulletin, vol. 1 (1997) , p.117-137.
S. Kang, J. Song. Parameter and Quantile Estimation for the Generalized Pareto Distribution in Peaks over Threshold Framework. Korean Statist. Soc. vol. 4 (2017) , p.487 -501.
F.M. Longin. From Value at Risk to Stress Testing:The Extreme Value Approach. J. Banking & Finance, vol. 24 ( 2000) , p.1097-1130.
S.S. Wang. A Class of Distortion Operators for Pricing Financial and Insurance Risks. Journal of Risk and Insurance, vol.1 (2000) , p.15-36.
W. Cui, J.P. Yang, L Wu. Optimal Reinsurance Minimizing the Distortion Risk Measure under General Reinsurance Premium Principles.Insurance: Mathematics and Economics,2013, p.49-56.
C.Z. Liu, W.Z. Shen, S.Huang. Strategic Considerations on China's Geological Disaster Prevention and Response, vol. 3 (2022) , p.1-4.
X. Pei. International Experience and Lessons for the Development of Catastrophe Insurance.Chinese insurance, vol. 9 (2021) , p.55-59.
L. Tian, J.Y. Peng, Z.W. Wang. Calculation of Catastrophe Insurance Fund Size in China under the Maximization of Underwriting Capacity. Insurance Research, vol.11 (2013), p.24-31.
C. Wang. Factors Influencing Residents' Willingness to Purchase Geological Disaster Insurance--Taking residents of Lanzhou city community as an example. (Ph.D., Lanzhou University,China 2017), p.71.
C.Q. Wang. Analysis of Difficulties in Catastrophe Insurance and Countermeasures. Fortune Life, vol. 10 (2022) , p.10-12.
Q.B. Zhao, Y. Hou. Catastrophe Risk Management in Mature Non-life Insurance Markets and the Development Status in China. Insurance Theory and Practice, vol. 6 (2021) , p.87-98.
Y.C. Chi, K.S. Tan. Optimal Reinsurance under VaR and CVaR Risk Measures.a Simplied Approach, vol.2 (2013) , p.487-509.