农业天气风险管理及产品定价研究

为降低系统性风险的冲击,基于效用理论,构建了包括农户、保险人和债券投资者三方在内的一般模型,得出了相应的指数保险和天气债券的定价公式。采用绥化市样本数据,基于支持向量机预测方法,进行了产品的实例设计。结果表明,如果在债券市场上,投资者的风险厌恶水平低于农户或保险人时,则可将天气风险转移至资本市场,风险转换比取决于三方的风险厌恶水平。支持向量机预测模型的应用可以显著提高预测精度,有助于降低基差风险。     

区域产量保险效率评价模型与实证分析

区域产量保险的效率是决定其实际应用价值的关键。文章通过构建模型测算区域产量保险降低生产者收益风险的程度来评估其效率,并运用水稻产量数据进行实证分析。结果表明,区域产量保险的有效性是有前提条件的;生产者的个体产量与区域产量的相关性越高,区域产量保险的效率越高,区域产量保险降低风险的程度就越高于个体产量保险。     

气温天气指数保险的费率厘定——以粮食作物为例

天气指数保险是传统农业保险、区域产量保险的创新。选择天气指数保险,探讨其费率厘定,有助于克服道德风险和逆选择,确保农业保险快速、健康地发展。粮食作物气温天气指数保险的费率厘定,首先要测算低温(或高温)的严重程度,计算低温(或高温)测度值;再分析气候因素导致粮食作物减产的程度,计算气候减产率;然后利用计量经济分析方法,确立气候减产率与低温(或高温)测度值之间的定量关系;最后根据该定量关系以及低温(或高温)测度值的期望值,求得气温天气指数保险的费率。该费率厘定方法,科学地测定了低温(或高温)程度,把赔付概率、触发值、费率厘定有机地联系在一起。并以长沙县早稻保险为例进行了实证研究。     

基于生存分析法的中国政策性农作物保险费率研究

为探索生存分析法对中国政策性农业保险纯费率厘定的适用性,采用辽宁省盘锦市1987—2008年的水稻、玉米和高粱作物单产数据,分别运用生存分析模型计算出75％产量保障水平的基础风险与巨灾风险概率,进而厘定出三种作物保险纯费率分别为3．0％、3．7％、3．7％。结果表明：该方法可以有效解决国内作物保险纯费率厘定过程中的巨灾风险厘定难题,将对中国政策性农业保险定价与补贴政策提供科学的研究依据。     

天气衍生品定价模型的构建

文章以天气衍生品市场为前提,提出了基于温度动态过程的天气衍生品的定价模型.根据美国国家气候数据中心发布的北京日均气温数据,对温度过程进行统计模拟.在温度动态过程中引入了风险的市场价格的思想,推出温度指数所遵循的随机微分方程,最终得到以HDD指数为标的资产的期权定价的偏微分方程和有限差分法的数值结果.     

基于混合Copula模型的水稻保险费率厘定

模拟产量风险和价格风险的联合分布是农业收入保险费率厘定的重点和难点。借助核密度估计方法和混合Copula模型研究了水稻产量和价格风险因子的联合分布并厘定了产量、价格和收入三种保险的纯费率。研究表明:(1)与单一Copula函数相比,混合Copula模型更适合拟合多风险因子的联合分布。(2)早稻价格风险导致的收入损失高于产量风险导致的收入损失,中稻和晚稻产量风险导致的收入损失高于价格风险导致的收入损失。(3)由于产量风险和价格风险的对冲,江苏、安徽、江西、河南、贵州和广西具备了在中稻、晚稻主产区试点收入保险的客观条件。(4)在100%的保障水平下,中国水稻产区早稻收入险纯费率为8.60%～12.84%、中稻收入险纯费率为5.89%～12.07%、晚稻收入险纯费率为4.59%～7.94%。     

基于两阶段Expectile回归的风险保费定价

如何厘定风险保费是保险精算的核心研究内容之一。风险保费由纯保费和风险附加构成,通常采用广义线性模型厘定纯保费,应用各种保费原理计算风险附加。常用的保费原理包括期望值原理、标准差原理、分位数原理等。文章基于对Expectile理论性质的研究结果,提出Expectile保费原理,即通过两阶段Expectile回归预测每份保单的风险保费。类似于分位回归是对中位数回归的自然推广,Expectile回归是对均值回归的自然推广。应用分位回归相当于是在中位数的基础上计算风险附加,而应用Expectile回归可以看作是在均值（亦即纯保费）基础上计算风险附加,所以风险保费的计算结果更加合乎定价逻辑。基于一组公开数据的实证研究结果表明,两阶段Expectile回归在基尼系数指标下厘定风险保费要优于其他现有方法,对于提高保险定价的科学性和合理性具有重要的实际应用价值。     

干旱天气指数保险精算定价与衍生品定价的比较

精算定价法、衍生品定价法是干旱天气指数保险的两种定价方法。文章采用这两种方法探讨了粮食作物干旱保险的定价,并以长沙县早稻作物为样本,选取1987—2018年的相关数据进行了实证对比分析。研究表明,当干旱赔付概率为40%时,精算定价法得到3月至7月纯费率分别为0.63%、0.26%、0.03%、0.22%、0.29%,纯费率随赔付概率而变化;当取干旱测度指标均值作为其执行价格时,天气衍生品定价法得到3月至7月看涨期权的赔付额度分别为2.91元/亩、2.72元/亩、2.27元/亩、2.11元/亩、3.06元/亩,赔付额度随执行价格的变化而变化。     

带干扰保费随机收取具有多个副索赔的相依风险模型的构建

文章根据保险实务中的相关数据,经过数据整理分析,建立了一个带干扰保费随机收取具有多个副索赔的相依风险模型;通过文中所建立的相依风险模型得到随机变量的数字特征和性质,利用鞅方法求出了破产概率的上界和破产概率的一般表达式;最后将索赔相依与独立的情况进行了比较,得出了索赔相依时风险更大的结论。     

保险集团监管资本套利的理论与实证研究——基于CTE与VaR风险度量方法的分析

为加强保险集团监管,完善中国正在建设的第二代偿付能力管理体系,利用国际上应用广泛的CTE与VaR两种风险度量方法构建监管资本套利模型,对不同条件下保险集团套利策略进行理论研究。根据中国人民财产保险股份有限公司历史数据,运用R软件,首次对保险集团监管资本套利策略进行直观展示和实证检验。研究表明:监管资本套利可以为保险集团节约大量资本,但蕴含一定风险;若监管资本中考虑尾部极端风险,有利于降低监管资本套利风险。     

Using copulas for rating weather index insurance contracts

This study develops a methodology for a copula-based weather index insurance design. Because the copula approach is better suited for modeling tail dependence than the standard linear correlation approach, its use may increase the effectiveness of weather insurance contracts designed to provide protection against extreme weather events. In our study, we employ three selected Archimedean copulas to capture the left-tail dependence in the joint distribution of the farm yield and a specific weather index. A hierarchical Bayesian model is applied to obtain consistent estimates of tail dependence using relatively short time series. Our empirical results for 47 large grain-producing farms from Kazakhstan indicate that, given the choice of an appropriate weather index to signal catastrophic events, such as a severe drought, copula-based weather insurance contracts may provide significantly higher risk reductions than regression-based indemnification schemes.     

保险产品中最低收益保证的均衡定价

在同时考虑保险公司和投保人的利益下,研究保险产品中最低收益保证的均衡定价,给出了不同效用函数下的定价区间,最后从再保险风险交换的角度给出了Pareto最优下的最低收益保证需要满足的条件。     

农作物产量保险区域化差别费率厘定的可行性——基于非参数核密度估计实证

采用非参数核函数平滑法以辽宁省、黑龙江省以及大连市的水稻、玉米和大豆三种农作物历年单位面积产量为例拟合了单产损失分布,同时利用传统的正态概率密度对区域作物单产分布进行了拟合。在拟合损失分布的基础上,分别厘定出不同保险水平农作物区域产量保险的纯保险费率。经测算发现,传统的正态概率密度下厘定的纯保险费率均低于非参数核密度下测算的纯费率,正态法低估了农作物单产的风险。保险水平在70%⁸0%间的参数法及非参数法测算的纯保险费率均低于政策性农业保险的现行费率。另外,在数据可得的基础上,还应该确定适当的厘定保费费率的区域以充分识别风险,更精确的计算保费。     

引入所得税的Merton模型存款保险定价研究

存款保险定价是存款保险制度建设中的核心内容,合理的保费厘定关乎存款保险制度的建立和存款机构的参与程度。从传统的Merton模型入手,在模型中引入了存款机构和保险公司所得税因素,将税盾效应应用于存款保险的定价中,建立了拓展的Merton模型。选取国内10家上市银行为样本,测算存款保险的保费费率,通过费率间的比较分析,得出引入所得税的拓展的存款保险定价模型能够明显降低保费费率,税盾效应显著。从风险测算和分阶段建立存款保险制度两个方面提出相关建议。     

Predicting Insurance Losses Under Cross-Classification: A Comparison of Alternative Approaches

Various mathematical and statistical models for estimation of automobile insurance pricing are reviewed. The methods are compared on their predictive ability based on two sets of automobile insurance data for two different states collected over two different periods. The issue of model complexity versus data availability is resolved through a comparison of the accuracy of prediction. The models reviewed range from the use of simple cell means to various multiplicative-additive schemes to the empirical-Bayes approach. The empirical-Bayes approach, with prediction based on both model-based and individual cell estimates, seems to yield the best forecast.     

基于VaR方法的长寿风险自然对冲模型

随着死亡率的下降与预期寿命的提高,保险公司面对着不容忽视的长寿风险。基于VaR方法探讨了长寿风险管理中的自然对冲策略,然后在对中国男性人口死亡率预测的基础上,给出了保险公司自然对冲长寿风险所需的最优产品结构,并进一步考查了利率、签单年龄、缴费方式等因素对最优产品结构的影响。     

Modeling of soybean yield using symmetric,asymmetric and bimodal distributions: implications for crop insurance

Over the years, many papers used parametric distributions to model crop yields, such as: normal (N), Beta, Log-normal and the Skew-normal (SN). These models are well-defined, mathematically and also computationally, but its do not incorporate bimodality. Therefore, it is necessary to study distributions which are more flexible in modeling, since most of crop yield data in Brazil presents evidence of asymmetry or bimodality. Thus, the aim of this study was to model and forecast soybean yields for municipalities in the State of Paran, in the period from 1980 to 2014, using the Odd log normal logistic (OLLN) distribution for the bimodal data and the Beta, SN and Skew-t distributions for the symmetrical and asymmetrical series. The OLLN model was the one which best fit the data. The results were discussed in the context of crop insurance pricing.     

Bayesian ratemaking procedure of crop insurance contracts with skewed distribution

Over the years, crop insurance programs became the focus of agricultural policy in the USA, Spain, Mexico, and more recently in Brazil. Given the increasing interest in insurance, accurate calculation of the premium rate is of great importance. We address the crop-yield distribution issue and its implications in pricing an insurance contract considering the dynamic structure of the data and incorporating the spatial correlation in the Hierarchical Bayesian framework. Results show that empirical (insurers) rates are higher in low risk areas and lower in high risk areas. Such methodological improvement is primarily important in situations of limited data.     

Bayesian modelling of health insurance losses

The purpose of this paper is to build a model for aggregate losses which constitutes a crucial step in evaluating premiums for health insurance systems. It aims at obtaining the predictive distribution of the aggregate loss within each age class of insured persons over the time horizon involved in planning employing the Bayesian methodology. The model proposed using the Bayesian approach is a generalization of the collective risk model, a commonly used model for analysing risk of an insurance system. Aggregate loss prediction is based on past information on size of loss, number of losses and size of population at risk. In modelling the frequency and severity of losses, the number of losses is assumed to follow a negative binomial distribution, individual loss sizes are independent and identically distributed exponential random variables, while the number of insured persons in a finite number of possible age groups is assumed to follow the multinomial distribution. Prediction of aggregate losses is based on the Gibbs sampling algorithm which incorporates the missing data approach.