变保费率Cox风险模型的研究

研究了当保费率随理赔强度的变化而变化时C ox风险模型的折现罚金函数,利用后向差分法得到了折现罚金函数所满足的积分方程,进而得到了破产概率,破产前瞬时盈余、破产时赤字的各阶矩所满足的积分方程.最后给出当理赔额服从指数分布,理赔强度为两状态的马氏过程时破产概率的拉普拉斯变换,对一些具体数值计算出了破产概率的表达式.     

终极破产概率的双边界

本文对一类理赔额分布给出了Andersen风险过程终极破产概率的上、下界，并考查了理赔分布的平衡分布是NBU和NWU的破产概率的情况。     

带常利率的双Poisson模型的破产概率

本文在保费的收取和理赔都为复合Poisson过程的盈余过程的基础上,考虑盈余产生利息的双Pois-son模型,在保费收取量和理赔量都取整数值时,我们运用转移概率推导出了破产概率的近似计算公式及误差估计式,并且得到了破产概率的一个上界和一个下界.     

保险公司赔付及破产的随机模拟与分析

孙立娟、顾岚等．保险公司赔付及破产的随机模拟与分析．本文研究定期人寿保险的承保理赔及破产模型,其中保单到达和索赔出现服从相互独立的Ｐｏｉｓｏｎ过程。对此模型给出了破产概率的一个具体上界,通过随机模拟生成了持有保单数和理赔过程的样本轨道,分析研究破产概率与准备金和理赔额之间的关系     

一类盈余过程的破产概率及其应用

本文给出了复合Poisson盈余过程在其个体理赔量服从两个指数分布的混合 分布时破产概率的显示解,并研究了此情形下破产概率的Lundberg界.作为应用,给出 了一种计算一般复合Poisson盈余过程破产概率的近似方法.     

带常利率和相依结构更新风险模型的破产概率

研究了一类具有常利率及相依结构的Sparre Andersen模型,模型中假设理赔间隔时间决定下一次理赔额的分布情况.对一般分布情形,利用推广后的调节系数方程与递归更新技巧,得到了此模型的最终破产概率上界的估计.最后以理赔额和理赔间隔时间都服从指数分布的情况下的实例分析来说明该模型的有效性.     

关于一类Erlang(2)风险过程

考虑一类理赔间隔服从Erlang(2)分布,即Gamma(2)分布的精算风险模型.与理赔间隔服从指数分布的古典风险模型相比较,这种精算模型更易于模拟风险.首先,本文证明了生存概率R(u)满足一个积分-微分方程,然后,得到了生存概率R(u)所满足的一个指数型积分方程.最后,得到了关于生存概率R(u)的一个显示解.本文的工作可视为Dickson[1]和Dickson&Hipp[2,4]相应工作的继续和补充.     

带马氏利率的离散时间风险模型的破产概率

本文考虑一类保费和理赔额均为随机变量,且利率为马氏链的离散时间风险模型。推出了有限时间和最终时间破产概率的递归方程,并用归纳法得到了最终时间破产概率的上界表达式。     

离散时间的双Poisson模型的破产概率

本文在离散复合Poisson风险模型的基础上,研究保费的收取也为一个Poisson过程的模型, 在保费收取量和理赔量都离散取整数值时,我们运用转移概率推导出了保险公司在有限时间内破产的概率以及最终破产概率的级数表达式和矩阵表达式.     

相关风险和模型的破产概率

本文研究了两险种理赔到达过程相关的正风险和模型与负风险和模型.利用Lundberg指数是相关因子的单调递减函数的性质,证明了破产概率是随着相关因子的增加而增大的,从而将相应的结果推广到了两险种理赔到达过程相关的风险和模型.     

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.     

On the recursive evaluation of a certain multivariate compound distribution

In this paper, we extend to a multivariate setting the bivariate model A introduced by Jin and Ren in 2014 (Recursions and fast Fourier transforms for a new bivariate aggregate claims model, Scandinavian Actuarial Journal 8) to model insurance aggregate claims in the case when different types of claims simultaneously affect an insurance portfolio. We obtain an exact recursive formula for the probability function of the multivariate compound distribution corresponding to this model under the assumption that the conditional multivariate counting distribution (conditioned by the total number of claims) is multinomial. Our formula extends the corresponding one from Jin and Ren.     

Copula credibility for aggregate loss models

This paper develops credibility predictors of aggregate losses using a longitudinal data framework. For a model of aggregate losses, the interest is in predicting both the claims number process as well as the claims amount process. In a longitudinal data framework, one encounters data from a cross-section of risk classes with a history of insurance claims available for each risk class. Further, explanatory variables for each risk class over time are available to help explain and predict both the claims number and claims amount process.For the marginal claims distributions, this paper uses generalized linear models, an extension of linear regression, to describe cross-sectional characteristics. Elliptical copulas are used to model the dependencies over time, extending prior work that used multivariate t-copulas. The claims number process is represented using a Poisson regression model that is conditioned on a sequence of latent variables. These latent variables drive the serial dependencies among claims numbers; their joint distribution is represented using an elliptical copula. In this way, the paper provides a unified treatment of both the continuous claims amount and discrete claims number processes.The paper presents an illustrative example of Massachusetts automobile claims. Estimates of the latent claims process parameters are derived and simulated predictions are provided.     

Sarmanov family of multivariate distributions for bivariate dynamic claim counts model

To predict future claims, it is well-known that the most recent claims are more predictive than older ones. However, classic panel data models for claim counts, such as the multivariate negative binomial distribution, do not put any time weight on past claims. More complex models can be used to consider this property, but often need numerical procedures to estimate parameters. When we want to add a dependence between different claim count types, the task would be even more difficult to handle. In this paper, we propose a bivariate dynamic model for claim counts, where past claims experience of a given claim type is used to better predict the other type of claims. This new bivariate dynamic distribution for claim counts is based on random effects that come from the Sarmanov family of multivariate distributions. To obtain a proper dynamic distribution based on this kind of bivariate priors, an approximation of the posterior distribution of the random effects is proposed. The resulting model can be seen as an extension of the dynamic heterogeneity model described in Bolancé et al. (2007). We apply this model to two samples of data from a major Canadian insurance company, where we show that the proposed model is one of the best models to adjust the data. We also show that the proposed model allows more flexibility in computing predictive premiums because closed-form expressions can be easily derived for the predictive distribution, the moments and the predictive moments.     

On the Expected Present Value of Total Dividends in a Risk Model with Potentially Delayed Claims

In this paper, we consider a risk model in which two types of individual claims, main claims and by-claims, are defined. Every by-claim is induced by the main claim randomly and may be delayed for one time period with a certain probability. The dividend policy that certain amount of dividends will be paid as long as the surplus is greater than a constant dividend barrier is also introduced into this delayed claims risk model. By means of the probability generating functions, formulae for the expected present value of total dividend payments prior to ruin are obtained for discrete-type individual claims. Explicit expressions for the corresponding results are derived for K n claim amount distributions. Numerical illustrations are also given.     

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.     

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.     

一类索赔到达计数过程相依的二元风险模型

研究了一类索赔到达计数过程为相依点过程的双险种风险模型.先将两个相依索赔总额转化为相互独立的索赔总额,并得出在PO ISSON情形下,可以转化为古典风险模型,从而可以利用现成的结果给出破产概率.     

一类带干扰且Cox相关的双险种风险模型

在带有随机扰动的环境中,考虑保单到达及索赔到达均为Cox点过程且两类索赔到达过程相关的一类双险种风险模型.利用鞅技巧,将破产概率的指数上界推广到了更一般的情形.