森林火灾保险巨灾损失评估研究

发布时间：2018-04-27 21:15

本文选题：森林火灾保险 + 巨灾损失评估　；参考：《湖南大学》2014年硕士论文

【摘要】：近年来自然灾害频繁发生、损失程度不断加大以及人为因素不断增加等综合作用，促使森林火灾极端事件发生频率及强度上升，，造成的大量直接经济损失严重威胁着保险公司的稳定经营。在此背景下，选择合适的方法及数据对森林火灾巨灾保险进行风险量化显得尤为重要。与此同时，森林火灾风险集中，难以分散，一旦发生火灾往往要跨多个区域，危及成千上万的林农及大面积的林地。其极易上升为巨灾的特点使得森林火灾巨灾风险的损失评估更加重要。目前，广泛使用的参数方法对于巨灾损失评估的扭曲影响尤为严重。其假设条件会导致估计值的分位数严重偏向一侧，并不适用于森林火灾保险的研究。与此相对应，非参数统计方法为巨灾风险研究提供了新的研究思路。但不可避免的缺陷是非参数核密度估计方法倾向于低估峰值处的概率且高估低谷处的概率。数据锐化方法可将其数据处理的比以前轻微的紧促一些，并替代原始数据按照正常的非参数核密度估计方法进行评估。该方法既保留了普通估计的优点，而且通过数据的预处理能够弥补普通估计的某些缺陷。本文基于统计学理论，选取非参数核密度估计方法和数据锐化技术，解决森林火灾巨灾风险评估中损失分布确定和降低风险曲线拟合偏差两个难题。文章旨在通过技术创新，形成森林火灾巨灾定量损失评估的技术方法，破解我国目前森林火灾保险损失评估中人为因素主导和缺失科学依据的问题，从而有效提高我国在森林火灾保险领域的承保风险管理水平。本文以收集到的森林火灾社会损失数据和国内某大型财险公司的损失数据为例进行实证分析。从理论上可以论证获得的风险曲线比传统非参数统计方法的评估偏差有所降低；从评估数据上可以看出改进的巨灾损失评估结果按照理论上的方向有所改变，且可以证明保险损失数据和社会数据的拟合结果随着承保范围扩大，拟合度也在不断上升。因此，本文从方法和数据上全面的提高了森林保险巨灾损失评估的准确性。
[Abstract]:In recent years, the frequent occurrence of natural disasters, the increasing degree of loss and the increasing of man-made factors have promoted the increase of the frequency and intensity of extreme events in forest fires. A large number of direct economic losses pose a serious threat to the stable operation of insurance companies. Under this background, it is very important to select appropriate methods and data to quantify the risk of forest fire catastrophe insurance. At the same time, the risk of forest fire is concentrated and difficult to disperse. Once a fire occurs, it often spans many areas, endangering thousands of forest farmers and large areas of forest land. It is easy to rise to catastrophe, which makes the loss assessment of forest fire catastrophe more important. At present, the distortion effect of widely used parameter method on catastrophe loss assessment is especially serious. The hypothetical condition will cause the quantile of the estimated value to deviate seriously to one side, which is not suitable for the study of forest fire insurance. The nonparametric statistical method provides a new approach for the study of catastrophe risk. But the non-parametric kernel density estimation methods tend to underestimate the probability of peak value and overestimate the probability of low point. The data sharpening method can process the data slightly more tightly than before and evaluate the original data according to the normal nonparametric kernel density estimation method. This method not only preserves the advantages of ordinary estimation, but also makes up for some defects of common estimation by data preprocessing. Based on the statistical theory, this paper selects the nonparametric kernel density estimation method and the data sharpening technique to solve the two problems of determining the loss distribution in forest fire catastrophe risk assessment and reducing the deviation of risk curve fitting. The purpose of this paper is to form the technical method of quantitative loss assessment of forest fire catastrophe through technological innovation, and to solve the problem that human factors dominate and lack scientific basis in forest fire insurance loss assessment in China. Therefore, the level of underwriting risk management in the field of forest fire insurance in China is improved effectively. In this paper, the data of forest fire social loss and a large property insurance company in China are taken as an example to make an empirical analysis. It can be proved theoretically that the risk curve obtained is lower than that of the traditional non-parametric statistical method, and from the evaluation data, it can be seen that the improved assessment results of catastrophe losses have changed according to the theoretical direction. It can also be proved that the fitting results of insurance loss data and social data are increasing with the expansion of insurance coverage. Therefore, this paper improves the accuracy of forest insurance catastrophe loss assessment in terms of methods and data.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：F842.64

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