非寿险分类费率模型的比较研究和实证分析

非寿险分类费率的厘定通常采用的方法有单项分析法、最小偏差法和广义线性模型,特别是后面两种方法在非寿险实务中应用十分广泛,精算文献中对这两种方法的理论和应用研究也较多,但对二者的比较研究较少。本文首先对最小偏差模型和广义线性模型进行了简要介绍,之后对这两种分类费率模型进行了系统的比较研究,总结了它们各自的优缺点以及二者之间的一些等价关系,最后通过一组实际的汽车保险数据讨论了它们的应用。     

GAMLSS模型在车险定价中的应用

在系统梳理国内外非寿险产品费率厘定方法的基础上,详细介绍了GAMLSS模型,证明了在位置参数和尺度参数的预测中均引入随机效应的GAMLSS模型可更有效地解释纵向数据中个体间的异质性.最后将GAMLSS模型应用于一组纵向车辆保险数据,计算了先验保费、后验保费、后验风险保费和奖惩因子.实证结果表明,GAMLSS模型不仅可为非寿险产品的定价提供依据,而且使风险分类更加稳定、合理.     

混合效应模型及其在非寿险费率厘定中的应用

在非寿险分类费率厘定中,广义线性模型的应用十分普遍,但当某些费率因子的水平数很多时(本文称之为多水平因子),广义线性模型的估计结果将不可靠。解决此类问题的一种方法是把多水平费率因子作为随机效应处理。将多水平费率因子作为随机效应处理可以采取下述三种方法:(1)分别用广义线性模型和信度模型估计普通费率因子和多水平因子,通过广义线性模型与Buhlmann-Straub信度模型的迭代应用预测索赔频率和索赔强度;(2)应用广义线性混合模型分别预测索赔频率和索赔强度;(3)直接对经验纯保费数据建立Tweedie混合效应模型。本文把上述模型应用于中国车损险实际数据的研究结果表明,这三种方法比较接近,但从总体上看,广义线性混合模型的估计结果更加可取。     

市场约束条件下的非寿险费率厘定

在非寿险费率厘定中,经常遇到的一个实际问题是某些风险类别的费率不能过高或不能过低。在这种约束条件下,传统的广义线性模型将不能直接用于费率厘定。本文给出了一种在一般线性约束条件下,如何应用迭代算法对常用的广义线性模型进行调整,从而得到满足特定约束条件的费率厘定结果。本文的实证研究结果表明,该方法具有灵活性和现实可行性,能够解决非寿险费率厘定中常见的市场约束问题。     

基于个体公平原则的寿险产品定价模型

在无套利框架的基础上,讨论基于个体公平原则下的寿险产品定价问题,即运用倒向随机微分方程理论,将投保人和保险人置于同一系统中进行考虑:首先,根据双方的随机投资决策目标分别建立无套利寿险定价模型和动态资产份额定价模型,得出两个特殊线性倒向随机微分方程的显式解;然后,建立基于个体公平原则的寿险定价模型,从投保人和保险人双方的角度对寿险产品进行公平定价,得出了从供需双方考虑的投资回报定价公式;最后,利用所建立的模型进行案例分析,计算出基于个体公平原则的保费及保险公司的投资策略.该寿险产品定价模型不仅考虑了保险人的意愿,还同时考虑了投保人的实际情况,因此,按此定价理念开发出的保险产品,不仅可以提高产品研发的成功率,而且使得研发出的新产品更能在竞争激烈的保险市场中站稳脚步.     

偏t分布假设下的空间效应模型及其应用

在非寿险索赔强度预测中,目前使用最为广泛的是广义线性模型。索赔强度的广义线性模型假设因变量服从伽马分布或逆高斯分布,且在预测项中仅能考虑协变量的线性效应。这些限制性条件都有可能影响索赔强度预测结果的准确性。本文对索赔强度的广义线性模型进行了推广:用偏T分布代替常用的伽马分布和逆高斯分布;在预测项中引入惩罚样条函数来描述连续型协变量的非线性效应;考虑索赔强度在不同地区的差异性和相邻地区的相依性。最后基于一组实际的车损险数据进行了实证研究,结果表明,本文的推广模型可以明显提高索赔强度预测模型的拟合优度。     

非寿险分类费率模型及其参数估计

在非寿险分类费率厘定中,存在各种模型可供选择,如加法模型、乘法模型、混合模型和广义线性模型等,而在这些模型的参数估计中,还存在各种可供选择的估计方法,如最小二乘法、极大似然法、最小x2法、直接法和边际总和法等。这些模型和参数估计方法散见于各种精算学文献中,本文对这些模型和参数估计方法进行了系统的比较和分析,并揭示了它们之间存在的一些等价关系。     

考虑空间效应的贝叶斯分层模型与索赔频率预测

在非寿险索赔频率预测中,使用最为广泛的是广义线性模型.但是,如果观察数据呈现出明显的零膨胀特征,或者包含空间协变量,或者某些协变量之间具有分层结构,则广义线性模型的拟合优度往往欠佳.在零膨胀分布假设下,建立了考虑空间效应的贝叶斯分层模型,并将其应用于索赔频率预测.在模型中,用惩罚样条函数描述连续型协变量的非线性效应,用高斯马尔科夫随机场描述相邻地区在索赔频率上的空间相依性,用随机截距项描述不同地区在索赔频率上的分层关系和差异性.实证研究结果表明,考虑空间效应的贝叶斯分层模型的拟合优度明显优于传统的广义线性模型.     

广义线性模型在非寿险费率分析中的应用

本文首先简单分析了传统定价方法的局限性,然后介绍了广义线性模型的基本理论。作为应用,在本文中应用R软件,详细分析了一家欧洲保险公司1994-1998年的车险索赔数据,估计得到了费率结构,它与实际费率结构差异较大,说明原来的费率结构可能需要更新。     

带有不可忽略缺失数据的广义部分线性模型的贝叶斯分析

广义部分线性模型是广义线性模型和部分线性模型的推广,是一种应用广泛的半参数模型.本文讨论的是该模型在线性协变量和响应变量均存在非随机缺失数据情形下参数的Bayes估计和基于Bayes因子的模型选择问题,在分析过程中,采用了惩罚样条来估计模型中的非参数成分,并建立了Bayes层次模型;为了解决Gibbs抽样过程中因参数高度相关带来的混合性差以及因维数增加导致出现不稳定性的问题,引入了潜变量做为添加数据并应用了压缩Gibbs抽样方法,改进了收敛性;同时,为了避免计算多重积分,利用了M-H算法估计边缘密度函数后计算Bayes因子,为模型的选择比较提供了一种准则.最后,通过模拟和实例验证了所给方法的有效性.     

Severity modeling of extreme insurance claims for tariffication

Generalized linear models are common instruments for the pricing of non-life insurance contracts. They are used to estimate the expected frequency and severity of insurance claims. However, these models do not work adequately for extreme claim sizes. To accommodate for these extreme claim sizes, we develop the threshold severity model, that splits the claim size distribution in areas below and above a given threshold. More specifically, the extreme insurance claims above the threshold are modeled in the sense of the peaks-over-threshold methodology from extreme value theory using the generalized Pareto distribution for the excess distribution, and the claims below the threshold are captured by a generalized linear model based on the truncated gamma distribution. Subsequently, we develop the corresponding concrete log-likelihood functions above and below the threshold. Moreover, in the presence of simulated extreme claim sizes following a log-normal as well as Burr Type XII distribution, we demonstrate the superiority of the threshold severity model compared to the commonly used generalized linear model based on the gamma distribution.     

Generalized linear models for dependent frequency and severity of insurance claims

Traditionally, claim counts and amounts are assumed to be independent in non-life insurance. This paper explores how this often unwarranted assumption can be relaxed in a simple way while incorporating rating factors into the model. The approach consists of fitting generalized linear models to the marginal frequency and the conditional severity components of the total claim cost; dependence between them is induced by treating the number of claims as a covariate in the model for the average claim size. In addition to being easy to implement, this modeling strategy has the advantage that when Poisson counts are assumed together with a log-link for the conditional severity model, the resulting pure premium is the product of a marginal mean frequency, a modified marginal mean severity, and an easily interpreted correction term that reflects the dependence. The approach is illustrated through simulations and applied to a Canadian automobile insurance dataset.     

Risk aggregation in non-life insurance: Standard models vs. internal models

Standard models for capital requirements restrict the correlation between risk factors to the linear measure and disregard undertaking-specific parameters. We consider an alternative framework for risk aggregation in non-life insurance using vine copulas that allow non-linear dependence and are estimated with undertaking-specific parameters. We empirically compare our alternative risk model with three regulatory standard models (Korean risk-based capital, Solvency II, Swiss Solvency Test) and show that the standard models lead to more than 50% higher capital requirements on average. Half of the overestimation results from the uniform parameter selection imposed by regulations and the other half comes from the linear correlation assumption. The differences might distort competition when both standard models and internal risk models are used in a single market.     

Assessing inflation risk in non-life insurance

Inflation risk is of high relevance in non-life insurers’ long-tail business and can have a major impact on claims reserving. In this paper, we empirically study claims inflation with focus on automobile liability insurance based on a data set provided by a large German non-life insurance company. The aim is to obtain empirical insight regarding the drivers of claims inflation risk and its impact on reserving. Toward this end, we use stepwise multiple regression analysis to identify relevant drivers based on economic indices related to health costs and consumer prices, amongst others. We further study the impact of (implicitly and explicitly) predicting calendar year inflation effects on claims reserves using stochastic inflation models. Our results show that drivers for claims inflation can considerably vary for different lines of business and emphasize the importance of explicitly dealing with (stochastic) claims inflation when calculating reserves.     

Contagion modeling between the financial and insurance markets with time changed processes

This study analyzes the impact of contagion between financial and non-life insurance markets on the asset–liability management policy of an insurance company. The indirect dependence between these markets is modeled by assuming that the assets return and non-life insurance claims are led respectively by time-changed Brownian and jump processes, for which stochastic clocks are integrals of mutually self-exciting processes. This model exhibits delayed co-movements between financial and non-life insurance markets, caused by events like natural disasters, epidemics, or economic recessions.     

Solvency margins and equalization reserves

We indicate a simple solution to the evaluation of solvency margins and equalization reserves in a non-life insurance portfolio. One of our provision models is based on a combination of ruin and credibility theory. It takes characteristics such as variance of claims, correlation of claims and volume of portfolio into account.     

非寿险中巨额损失数据的拟合与精算

利用极值理论给出了一种新的解决非寿险精算中巨额损失保费厘定问题的方法。在建模过程首先给出了极值理论的最大吸引域检验问题,然后利用不同方法讨论了最优门限值的选取问题,并在POT模型下利用广义帕累托分布对巨额损失分布进行拟合。然后在假设损失次数服从泊松分布的条件下,在复合泊松分布的框架下讨论了险位超赔再保险的纯保费计算问题。