汽车保险的精算模型及其应用

本文应用我国一家保险公司的实际数据 ,对各种可以反映保单持有人索赔次数的模型 (包括负二项模型、泊松 -逆高斯模型、二元风险模型、三元风险模型、二项 -贝塔模型和负二项 -帆塔模型 )分别进行了拟合 ,结果表明三元风险模型拟合效果最好 ,因此利用三元风险模型构造了对保单持有人根据后验风险的大小调整其续期保费的系统     

中国人口死亡率建模比较及长寿风险度量

以我国颁布的3套保险行业经验生命表为基础，结合1995-2017年国家统计局发布的《中国统计年鉴》中的死亡率数据，首先分析了中国全年龄人口数据死亡率动静态变动特点，其次比较了LC，CBD和APC 3种模型对中国死亡率数据的拟合优劣，最后采用最优APC模型度量了不同生命表下的长寿风险.死亡率的动态变化会导致以经验生命表为依据的年金产品定价出现偏差，增加养老金管理机构的承保风险.     

保费随机收取的广义二元复合非齐次Poisson风险模型

研究了一类双险种风险模型,其中索赔到达计数过程和保费到达计数过程均为非齐次Po isson过程,用鞅方法得到了有限时间破产概率的一个上界,并给出了当两个险种的个体索赔均服从指数分布时,有限时间破产概率的上界估计.     

Error bounds for the compound poisson approximation

Explicit error bounds in terms of probabilities and stop-loss premiums are given for two kinds of compound Poisson approximations: the first concerns the difference between the individual and the collective model; the second is about the difference of the compound negative binomial and the compound Poisson distribution.     

带干扰风险的破产模型的研究

本文对古典风险模型中保险公司按单位时间常数率收到保险费的假设做了改进,将每次收到的保险费的次数看作是复合泊松过程,将每次收到的保费和每次的理陪额均看作是服从指数分布的随机变量,并引入带干扰风险的扰动项,从而对古典风险模型进行推广,且给出了相应的破产概率上界,分析了破产概率的上界与准备金,索赔额,净保费和扰动方差之间的关系.     

New insights on testing the efficiency of methods of pricing and hedging American options

With reference to the evaluation of the speed–precision efficiency of pricing and hedging of American Put options, we present and discuss numerical results obtained on the basis of four different large enough random samples according to the relevance of the American quality (relative importance of the early exercise opportunity) of the options. Here we provide a comparison of the best methods (lattice based numerical methods and an approximation of the American Premium analytical procedure) known in literature along with some key methodological remarks.     

分数跳-扩散环境下欧式期权定价的Ornstein-Uhlenbeck模型

假设股票价格遵循分数布朗运动和复合泊松过程驱动的随机微分方程,建立分数跳-扩散Ornstein-Uhlenbeck模型,利用价格过程的实际概率测度和公平保费原理,得到欧式看涨期权定价的解析表达式。推广了关于欧式期权定价的结论。     

Finite-Time Ruin Probabilities for Discrete, Possibly Dependent, Claim Severities

An important question in insurance is how to evaluate the probabilities of (non-) ruin of a company over any given horizon of finite length. This paper aims to present some (not all) useful methods that have been proposed so far for computing, or approximating, these probabilities in the case of discrete claim severities. The starting model is the classical compound Poisson risk model with constant premium and independent and identically distributed claim severities. Two generalized versions of the model are then examined. The former incorporates a non-constant premium function and a non-stationary claim process. The latter takes into account a possible interdependence between the successive claim severities. Special attention will be paid to a recursive computational method that enables us to tackle, in a simple and unified way, the different models under consideration. The approach, still relatively little known, relies on the use of remarkable families of polynomials which are of Appell or generalized Appell (Sheffer) types. The case with dependent claim severities will be revisited accordingly.      

Risk process with stochastic income and two-step premium rate

In this paper we deal with the risk reserve process with stochastic premium function. We assume that the premiums sizes have exponential distribution with the rate depending on some threshold level. The representation for the discounted defective joint density of surplus and deficit at ruin is obtained.     

Solving the response time variability problem by means of a genetic algorithm

The response time variability problem (RTVP) is a hard scheduling problem that has recently been defined in the literature and has a wide range of real-world applications in mixed-model assembly lines, multithreaded computer systems, network environments and others. The RTVP arises whenever products, clients or jobs need to be sequenced in such a way that the variability in the time between the points at which they receive the necessary resources is minimized. Since the RTVP is a complex problem, heuristic and metaheuristic techniques are needed to solve it. The best results in the literature for the RTVP have been obtained with a psychoclonal algorithm. We propose a genetic algorithm (GA) that is adapted to solve the RTVP. A computational experiment is carried out and it is shown that, on average, the GA produces better results than the psychoclonal algorithm.