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林业科学 ›› 2016, Vol. 52 ›› Issue (7): 129-137.doi: 10.11707/j.1001-7488.20160716

• 论文与研究报告 • 上一篇    下一篇

森林火灾保险纯费率厘定模型及实证分析

张德成1, 陈绍志1, 白冬艳2   

  1. 1. 中国林业科学研究院林业科技信息研究所 北京 100091;
    2. 北京市经济管理学校财会金融系 北京 100142
  • 收稿日期:2015-06-08 修回日期:2016-01-22 出版日期:2016-07-25 发布日期:2016-08-16
  • 通讯作者: 陈绍志
  • 基金资助:
    林业软科学研究项目“我国森林保险制度设计及运行机制研究”(2014-R14)。

Model and Empirical Analysis on Forest Fire Insurance Pure Premium Rate Making

Zhang Decheng1, Chen Shaozhi1, Bai Dongyan2   

  1. 1. Research Institute of Forestry Policy and Information, CAF Beijing 100091;
    2. Department of Accounting and Finance, Beijing Economic and Management College Beijing 100142
  • Received:2015-06-08 Revised:2016-01-22 Online:2016-07-25 Published:2016-08-16

摘要: [目的] 研究适用于我国现行森林保险制度的森林火灾保险纯费率厘定模型,分析森林火灾保险纯费率的区域分布,为科学、合理厘定森林火灾保险费率提供参考。[方法] 改进Holecy等(2003;2006)模型中林火发生概率允许误差的估计方法,以参保率代替参保面积变量,并将费率计量单位由实际值改为千分率,以多年森林火灾发生概率均值为期望纯费率,以期望纯费率的允许误差为风险纯费率,构建森林火灾保险纯费率厘定模型;运用该模型估计2014年我国各地区不同参保率下的森林火灾保险纯费率;通过配对样本T检验,对比分析19个试点地区森林火灾保险估计纯费率和实际纯费率,以检验模型估计结果的准确性。[结果] 我国绝大部分地区的森林火灾期望概率都在1.00‰以下,但地区间的差异明显;各地区森林火灾保险纯费率随着参保率的降低而增加;按照目前的参保率计算,我国19个试点地区的森林火灾保险纯费率为0.05‰~7.20‰;19个试点地区2014年森林火灾保险的实际纯费率与模型估计纯费率无差异。[结论] 模型充分考虑了森林火灾发生的确定因素和不确定性因素,适用于我国现行森林保险制度,符合我国森林火灾风险现状,估计结果准确可靠。由于火灾发生与林区的自然条件和植被类型高度相关,因此模型应用时需根据时空变化对所采用的数据进行动态调整,可基于本模型运用纯保费法厘定森林火灾保险毛费率。

关键词: 森林保险, 纯费率, 费率厘定, 森林火灾

Abstract: [Objective] Developing forest fire insurance pure rates (P) model which adapt to China's current forest insurance system and making analysis on forest fire insurance rates distribution by regional in China can provide the method and reference for rating the forest fire insurance premium rate scientifically and reasonably.[Method] This paper developed a forest fire insurance pure premium rate model basing on Holecy's forest fire insurance premium rate model(Holecy et al., 2003; 2006). The Expected pure premium rate (EP)is mean of forest fire occurrence probability and the Risk pure premium rate(RP)is tolerable error of EP. The model involved the insurance rate fire occurrence probability and other factors. It replaced the Holecy model's insured area variable and changed the measurement units from the actual value to thousand points(‰).To check the model, it make an analysis on China's forest insurance pure premium rate under different insurance rates by regions in 2014 and compare the estimation results(PβM) and the actual rate(P*βM) of 19 forest insurance modeling regions by paired sample T test.[Result] It indicated that the EP of most regions in China were below 1.00‰ but regional differences was significant. The P shall increase with the decreasing of the insured rate. The P of 19 forest insurance modeling regions is 0.05% to 7.20% as the current insured rate. There was no significant difference between the actual pure premium rate and estimated pure premium rate according to the paired samples T test.[Conclusion] The model considered both the identified and uncertain factors and fitted current forest insurance system in China. The estimated result was accurate. It recommended that the data should be adjusted dynamically according to the time and spatial variation when using the model. This model can be use to determine the forest fire insurance gross premium rate.

Key words: forest insurance, pure premium rate, rate making, forest fire

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