A claims persistence process and insurance

The purpose of this paper is to introduce and construct a state dependent counting and persistent random walk. Persistence is imbedded in a Markov chain for predicting insured claims based on their current and past period claim. We calculate for such a process, the probability generating function of the number of claims over time and as a result are able to calculate their moments. Further, given the claims severity probability distribution, we provide both the claims process generating function as well as the mean and the claim variance that an insurance firm confronts over a given period of time and in such circumstances. A number of results and applictions are then outlined (such as a Compound Claim Persistence Process).     

Multivariate negative binomial models for insurance claim counts

It is no longer uncommon these days to find the need in actuarial practice to model claim counts from multiple types of coverage, such as the ratemaking process for bundled insurance contracts. Since different types of claims are conceivably correlated with each other, the multivariate count regression models that emphasize the dependency among claim types are more helpful for inference and prediction purposes. Motivated by the characteristics of an insurance dataset, we investigate alternative approaches to constructing multivariate count models based on the negative binomial distribution. A classical approach to induce correlation is to employ common shock variables. However, this formulation relies on the NB-I distribution which is restrictive for dispersion modeling. To address these issues, we consider two different methods of modeling multivariate claim counts using copulas. The first one works with the discrete count data directly using a mixture of max-id copulas that allows for flexible pair-wise association as well as tail and global dependence. The second one employs elliptical copulas to join continuitized data while preserving the dependence structure of the original counts. The empirical analysis examines a portfolio of auto insurance policies from a Singapore insurer where claim frequency of three types of claims (third party property damage, own damage, and third party bodily injury) are considered. The results demonstrate the superiority of the copula-based approaches over the common shock model. Finally, we implemented the various models in loss predictive applications.     

Robust and bias-corrected estimation of the coefficient of tail dependence

We introduce a robust and asymptotically unbiased estimator for the coefficient of tail dependence in multivariate extreme value statistics. The estimator is obtained by fitting a second order model to the data by means of the minimum density power divergence criterion. The asymptotic properties of the estimator are investigated. The efficiency of our methodology is illustrated on a small simulation study and by a real dataset from the actuarial context.     

可持续的中国城乡居民养老保险体系的数学模型研究

考虑了替代率、缴费率、人口结构、分年龄段死亡率、经济增速、财政补贴、工资水平、投资效益,引入收缴率、通货膨胀率等当今影响养老保险的活跃因素,建立了"城乡结合"的中国城乡基本养老保险收支跨期叠代模型,并在此基础上进行优化.通过仿真,探讨了替代率和缴费率的合理区间.     

Optimal Tracking for an Insurance Model

Abstract

In this article, we assume that we have a number of candidate insurance models for describing a risk process. Suppose that in each model the risk process is a function of the states of some Markov chains. Based on observing the history of the premiums and claims processes we propose dynamics whose solutions indicate the likelihoods of each candidate model.     

A linear algebraic method for pricing temporary life annuities and insurance policies

We recast the valuation of annuities and life insurance contracts under mortality and interest rates, both of which are stochastic, as a problem of solving a system of linear equations with random perturbations. A sequence of uniform approximations is developed which allows for fast and accurate computation of expected values. Our reformulation of the valuation problem provides a general framework which can be employed to find insurance premiums and annuity values covering a wide class of stochastic models for mortality and interest rate processes. The proposed approach provides a computationally efficient alternative to Monte Carlo based valuation in pricing mortality-linked contingent claims.     

常数比例投资组合保险(CPPI)策略 --捐赠型基金投资策略的最优选择

本文采用Merton提出的处理捐赠型基金的连续时间模型的一般框架，分析了在风险资产为几何布朗运动，效用函数为CRRA效用函数，且捐赠型基金有动态最低支出时的最优支出策略和最优投资策略，结果表明存在一条策略基准线，当基金的总资产在策略基准线之上时，基金管理人关于基金支出与投资策略的选择与不存在最低支出的要求时所作出的决策是一样的，但是一旦基金的总资产低于这条策略基准线时，基金管理人便需要考虑到基金将来必要的支出，并实际影响到他对投资策略的选择，此时基金管理人可作的最优选择是：最低的支出和一种为复制幂收益函数期权的CPPI投资策略。     

Moment Bounds on Discrete Expected Stop-Loss Transforms, with Applications

This paper shows how to make the best possible use of the information contained in the first few moments (mean, variance and skewness, say) of an integer-valued random variable when one is interested in expected stop-loss transforms. This allows to bound various quantities in applied probability, including the ruin probabilities, for instance.      

Strategies for detecting fraudulent claims in the automobile insurance industry

Some property and casualty insurers use automated detection systems to help to decide whether or not to investigate claims suspected of fraud. Claim screening systems benefit from the coded experience of previously investigated claims. The embedded detection models typically consist of scoring devices relating fraud indicators to some measure of suspicion of fraud. In practice these scoring models often focus on minimizing the error rate rather than on the cost of (mis)classification. We show that focusing on cost is a profitable approach. We analyse the effects of taking into account information on damages and audit costs early on in the screening process. We discuss several scenarios using real-life data. The findings suggest that with claim amount information available at screening time detection rules can be accommodated to increase expected profits. Our results show the value of cost-sensitive claim fraud screening and provide guidance on how to render this strategy operational.     

Optimal decision rule in forming an insurance portfolio

The paper is devoted to finding an optimal decision rule for accepting/rejecting potential insureds when the demand for the insurance provision is a stochastic variable. A criterion to be maximized is the mean-variance utility function of the insurer. It is shown that the optimal decision rule is a stopping rule with some finite protection level.