未决赔款准备金评估的随机性Munich链梯法

精算实务界通常采用链梯法等确定性方法评估未决赔款准备金,这些评估方法存在一定缺陷,一方面不能有效考虑保险公司历史数据中所包含的已决赔款和已报案赔款数据信息,另一方面只能得到未决赔款准备金的均值估计,不能度量不确定性。为了克服这些缺陷,本文结合Mack模型假设和非参数Bootstrap重抽样方法,提出了未决赔款准备金评估的随机性Munich链梯法,并应用R软件对精算实务中的实例给出了数值分析。     

SolvencyⅡ框架下非寿险一年期准备金风险的度量

在欧盟以风险为核心的Solvency II监管框架下,非寿险准备金传统评估问题正向准备金风险管理新问题转化,准备金风险的识别、度量与控制已成为非寿险精算理论和实务重点关注的前沿问题。本文系统讨论非寿险一年期准备金风险的概念及其度量模型与方法。首先,通过实例直观阐述一年期准备金风险与索赔进展结果(CDR)的内涵;其次,基于贝叶斯对数正态模型,利用MCMC方法和R软件,随机模拟CDR的预测分布,并用CDR预测分布的统计特征来度量非寿险一年期准备金风险;最后,将欧洲保险公司实际索赔数据代入以上模型和步骤进行实证分析。研究表明,基于MCMC随机模拟方法获得的CDR预测分布,能够更加稳健和有效地度量非寿险一年期准备金风险。     

基于三角模糊数的案均赔款模型

针对传统案均赔款法无法度量准备金评估的不确定性问题,提出基于模糊数的案均赔款法。把非对称的三角模糊数引入到案均赔款法之中,得到累计已报案件数和案均赔款的模糊进展因子,进而计算出各个事故年对应的最终赔款的预测区间及相对应的未决赔款;最后通过决策者风险参数和不确定性参数的不同取值,得到准备金的波动性度量。实证证明该方法可以有效度量准备金预测值的不确定性和波动性。     

基于广义线性模型的个体索赔RBNS准备金评估

基于个体索赔模型对准备金的评估已成为准备金评估研究的重要内容.本文基于广义线性模型,对个体索赔额及索赔数目建立责任准备金模型,给出未决赔款责任准备金的期望及方差.进而,根据样本数据对未知参数求解极大似然估计,并讨论了估计的强相合性和渐近正态性.并得到责任准备金的估计及其预测均方误差.最后,通过数值模拟的方法将本文得到的估计与链梯法进行比较,结果显示我们的估计明显优于链梯法估计.     

非寿险业务未决赔款准备金的两阶段广义线性模型估计

本文以非寿险业务未决赔款准备金估计的确定性方法-PPCI法的思想为基础,分两阶段建立广义线性模型,分别对索赔次数和已发生每案赔付额进行估计,进而得到未决赔款准备金的估计值,并对模型的预测误差进行估计。文中通过一个实例对所述方法进行验证,并从预测误差的角度与其它模型进行比较。最后对该模型特点进行了总结。     

基于有限服务排队系统的未决赔款准备金分布

若保险赔付工作中赔付人员有限,根据服务人员有限的排队系统的性质,可以研究保险公司所需计提的未决赔款准备金的分布函数.当假设赔付服务工作人员为c个,使用M/M/c/∞和G/M/c/∞排队系统的性质可以得到未决赔款准备金分布函数和年末所需增加计提的未决赔款准备金的分布及其界值.当假设赔付服务工作人员仅一个,使用M/G/1/∞排队系统的性质可以得到此时未决赔款准备金的分布函数.并且在假设损失赔付额取正整数的条件下,得到年末保险公司所需增加计提的未决赔款准备金分布的递推公式.而且通过计算实例表明结论的实用性,及所得到的递推公式在以往难以准确求解未决赔款准备金分布时是十分有效的.     

分层广义线性模型在准备金评估中的建模研究

考虑到赔付流量三角形数据同一事故年反复观测的纵向特征以及数据结构的层次性,建立了分层广义线性模型.与通常的随机模型相比,分层广义线性模型不但可以选择条件反应变量的分布而且风险参数分布范围也更加广泛.利用h-似然函数估计分层广义线性模型的模型参数,降低了计算量.为使模型具有可比性,评估模型的预测精度,推导了模型预测误差的估计式.为充分利用已知赔付信息,将赔付额和赔付次数两种赔付信息纳入未决赔款准备金评估模型,建立了两阶段分层广义线性模型.在线性预测量中考虑了各种固定效应和随机效应以及模型结构的散布参数,改进了线性预估量结构.研究表明:分层广义线性模型对于数据的各种分布及形式都具有很好的适应性,更加符合保险实务现实的赔付规律.     

广义加权损失函数下未决赔款准备金的信度估计

为了使得估计的准备金不依赖于先验分布的具体形式,在贝叶斯链梯模型中,采用信度理论的思想,在广义加权损失函数下得到链梯因子的信度估计,建立了案均赔款法下的未决赔款准备金模型.最后,给出保险公司的实际例子,将得到的信度估计与经典链梯法和随机链梯法估计进行了比较.结论显示,方法对未决赔款准备金是有效的.     

Hoerl曲线的改进及其在非寿险未决赔款准备金估计中的应用

对基于Hoerl曲线的非寿险未决赔款准备金估计模型的不足进行了讨论,并对其进行了改进.将改进的Hoerl曲线做为预测量而建立的指数族非线性模型具有更大的灵活性,因而更适用于未决赔款准备金的估计.通过模拟实验对改进的Hoerl曲线在未决赔款准备金估计中的应用进行了验证,并与经典泊松链梯模型以及基于Hoerl曲线的模型进行了对比分析.结论表明,对于先缓慢增长至顶点,然后快速回落的赔付模式,改进的Hoerl曲线具有更好的预测效果.     

稳定分布中偏度参数的一个新估计

当观测到数据出现Gauss分布无法捕捉的厚尾和非对称特征时,具有幂率尾行为与求和框架的稳定分布常被用作拟合模型.考虑到偏度参数是除特征指数之外另一个度量稳定分布尾行为的重要指标,本文首先使用逻辑函数连接偏度参数和由协变量组成的线性预测,构成一个尾回归模型.然后,使用近似对数似然函数获得偏度参数回归估计并给出估计的渐近正态性质.最后,通过一个实例阐明本文所给的估计不仅具有一定的解释经济意义的能力,预测表现也在预期范围内.     

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.     

Paid-incurred chain claims reserving method

We present a novel stochastic model for claims reserving that allows us to combine claims payments and incurred losses information. The main idea is to combine two claims reserving models (Hertig’s (1985) model and Gogol’s (1993) model ) leading to a log-normal paid-incurred chain (PIC) model. Using a Bayesian point of view for the parameter modelling we derive in this Bayesian PIC model the full predictive distribution of the outstanding loss liabilities. On the one hand, this allows for an analytical calculation of the claims reserves and the corresponding conditional mean square error of prediction. On the other hand, simulation algorithms provide any other statistics and risk measure on these claims reserves.     

Hybrid fuzzy least-squares regression analysis in claims reserving with geometric separation method

Claims reserving is obviously necessary for representing future obligations of an insurance company and selection of an accurate method is a major component of the overall claims reserving process. However, the wide range of unquantifiable factors which increase the uncertainty should be considered when using any method to estimate the amount of outstanding claims based on past data. Unlike traditional methods in claims analysis, fuzzy set approaches can tolerate imprecision and uncertainty without loss of performance and effectiveness. In this paper, hybrid fuzzy least-squares regression, which is proposed by Chang (2001), is used to predict future claim costs by utilizing the concept of a geometric separation method. We use probabilistic confidence limits for designing triangular fuzzy numbers. Thus, it allows us to reflect variability measures contained in a data set in the prediction of future claim costs. We also propose weighted functions of fuzzy numbers as a defuzzification procedure in order to transform estimated fuzzy claim costs into a crisp real equivalent.     

Combining chain-ladder claims reserving with fuzzy numbers

In this paper we extend the classical chain-ladder claims reserving method using fuzzy methods. Therefore, we derive new estimators for the claims development factors as well as new predictors for the ultimate claims. The advantage in using fuzzy numbers lies in the fact that the model uncertainty is directly included in and can be controlled by the “new” fuzzy claims development factors. We also provide an estimator for the uncertainty of the ultimate claims for single accident years and for aggregated accident years.     

Option pricing bounds with standard risk aversion preferences

For a theoretical valuation of a financial option, various models have been proposed that require specific hypotheses regarding both the stochastic process driving the price behaviour of the underlying security and market efficiency. When some of these assumptions are removed, we obtain an uncertainty interval for the option price. Up to now, the most restrictive intervals for option prices have been obtained using the decreasing absolute risk aversion (DARA) rule in a state-preference approach. Precautionary saving entails the concept of prudence; in particular, decreasing absolute prudence is a necessary and sufficient condition that guarantees that the saving of wealthier people is less sensitive to the risk associated to future incomes. If this condition is coupled with the DARA assumption we obtain standard risk aversion (SRA), which guarantees on the one hand that introducing a zero-mean background risk to wealth makes people less willing to accept another independent risk and on the other hand that an increase in the risk of the returns distribution of an asset reduces the demand for this asset. The main idea of this contribution is to apply decreasing absolute prudence and SRA rules in a state-preference context in order to obtain efficient bounds for the value of European-style options portfolio strategies. Lower and upper bounds for the options portfolio value are obtained by solving non-linear optimization problems. The numerical experiments carried out show the efficiency of the technique proposed.     

Evaluating Project Completion Times When Activity Times are Erlang Distributed

One aspect of project planning risk assessment is to do with the uncertainty of the project duration. This uncertainty can be quantified by determining the project completion time distribution. A brief review of the existing literature on project duration risk assessment methodologies is given and their advantages and disadvantages evaluated. A development of the moments method based on Erlang distribution of activity times provides an accurate estimate of a project completion time distribution for a large range of practical situations and also is the basis upon which multi-modal input distributions of activity times can be handled. The method is assessed by a number of illustrative examples.     

Uncertain insurance risk process with multiple classes of claims

Traditionally, an insurance risk process describes an insurance company’s risk through some criteria using the historical data under the framework of probability theory with the prerequisite that the estimated distribution function is close enough to the true frequency. However, because of the complexity and changeability of the world, economical and technological reasons in many cases enough historical data are unavailable and we have to base on belief degrees given by some domain experts, which motivates us to include the human uncertainty in the insurance risk process by regarding interarrival times and claim amounts as uncertain variables using uncertainty theory. Noting the expansion of insurance companies’ operation scale and the increase of businesses with different risk nature, in this paper we extend the uncertain insurance risk process with a single class of claims to that with multiple classes of claims, and derive expressions for the ruin index and the uncertainty distribution of ruin time respectively. As the ruin time can be infinite, we propose a proper uncertain variable and the corresponding proper uncertainty distribution of that. Some numerical examples are documented to illustrate our results. Finally our method is applied to a real-world problem with some satellite insurance data provided by global insurance brokerage MARSH.     

Premium rates based on genetic studies: How reliable are they?

Underwriting the risk of rare disorders in long-term insurance often relies on rates of onset estimated from quite small epidemiological studies. These estimates can have considerable sampling uncertainty and any function based upon them, such as a premium rate, is also an estimate subject to uncertainty. This is particularly relevant in the case of genetic disorders, because the acceptable use of genetic information may depend on establishing its reliability as a measure of risk. The sampling distribution of a premium rate is hard to estimate without access to the original data, which is rarely possible. From two studies of adult polycystic kidney disease (APKD) we obtain, not the original data, but the cases and exposures used for Kaplan-Meier estimates of the survival probability. We use three resampling methods with these data, namely: (a) the standard bootstrap; (b) the weird bootstrap; and (c) simulation of censored random lifetimes. Rates of onset were obtained from each simulated sample using kernel-smoothed Nelson-Aalen estimates, hence critical illness insurance premium rates for a mutation carrier or a member of an affected family. From 10,000 such samples we estimate the sampling distributions of the premium rates, finding considerable uncertainty. Very careful consideration should be given before using small-sample epidemiological data to deal with insurance problems.