上证指数收益率的极值研究

在极值理论广义极值分布模型的基础上,对上证指数日回报率的极值作了实证研究.给出了近两年间出现的极值的概率与等待时间,为风险的度量提供了量化的依据.     

索赔次数为复合Poisson-Geometric过程的风险模型及破产概率

本文引入一类复合Poisson-Geometric分布,这类分布包括两个参数,是普通Poisson分布的一种推广,并在保险中有其实际的应用背景;基于此分布产生一个计数过程,称之为复合Poisson-Geometric过程.本文着重研究了索赔次数为复合Poisson-Geometric过程的风险模型,这种模型是经典风险模型的一个推广.针对此模型,本文给出了破产概率公式及更新方程.作为特例,当索赔额服从指数分布时,给出了破产概率的显式表达式.     

二维风险模型中Copula 相依下的破产概率

在本文中, 我们把Copula 连结函数用到二维的风险模型中, 考虑两个模型索赔额之间基于Copula 的相依关系. 首先对二维复合Poisson 模型给出了最早破产时刻定义下的生存概率满足的偏微分方程; 然后对二维的复合二项模型, 分别在连续型索赔额分布和离散型索赔额分布下给出了不同定义的生存概率和破产概率的递归公式, 并且特别选择了FGM Copula 连结函数, 给出了相应的结果; 另外在离散型分布下, 对于其Copula 函数的不唯一性进行了说明.     

关于离散型随机变量次序统计量分布矩阵对称性猜想的探索

本文研究了离散型随机变量次序统计量的分布矩阵的对称性 ,获得了二个定理 .定理 1　服从等概率二点分布或等概率三点分布的离散型随机变量的次序统计量的分布矩阵是对称矩阵 .定理 2　取值有限且等概率的离散型随机变量的次序统计量的分布矩阵具有中心对称性 .     

一类具有正跳的L

本文利用风险分析中的破产概率与带正跳的levy过程的一类极值分布间的关系,求得了该极值分布的表达式.     

一类具有正跳的Levy过程的一个极值分布

本文利用风险分析中的破产概率与带正跳的levy过程的一类极值分布间的关系,求得了该极值分布的表达式.     

保险系统中一种推广风险模型的破产概率

将经典复合 Poisson风险模型推广至更为一般情况 ,其中保单以 Poisson分布流到达且收取的保费为随机变量 ,建立一种双复合 Poisson风险模型 .对此模型 ,得到了最终破产概率的一般表达式和破产概率的一个上界估计值 .     

离散随机序在复合二项破产模型中的应用

本文的内容由三部分组成 .首先 ,在简述复合二项破产模型近期已得的相关成果的基础上 ,给出了最终破产概率的复合几何分布表示 ;接着 ,在概述了离散随机优序与停止损失序的主要结果后 ,首次提出了幂序的概念 ;最后 ,借助上述离散随机序 ,在复合二项破产模型中探讨了个体索赔额对于最终破产概率与调节系数的影响     

复合更新风险模型下的破产概率

讨论了具有较一般意义的复合更新风险模型下的破产概率,在假定索赔分布属于重尾分布族的前提下,得到了我们所渴望的破产概率的尾等价形式.这一结果恰与经典的Cram啨r-Lundberg模型下的结论相一致.     

线性红利界下带干扰风险模型的破产概率

考虑到保险公司在实际经营中收益所具有的不确定性和分红策略,建立一类具有线性红利界和带随机扰动的双复合Poisson风险模型,利用鞅方法给出模型关于破产概率的一个定理及上界.     

定数截尾下极值分布的参数估计

本文利用极值分布的一些特性和不动点原理,讨论了定数截尾情形下极值分布参数的估计问题,并提出了一种新的迭代算法;模拟结果显示,这种方法收敛速度快,且不受初始值选取的限制.     

凸概率密度分布簇下的多损失鲁棒优化等价模型及在直营连锁企业中的应用

首先,证明了凸概率密度分布簇的单周期期望均值下单损失鲁棒优化等价模型定理,以及凸概率密度分布簇的单周期期望均值下多损失鲁棒优化等价模型。然后,提出了直营连锁企业的产品在凸概率密度分布簇下的期望均值的单周期生产分配供应问题,建立了直营连锁企业的单周期生产分配供应期望均值鲁棒模型,在获得近似周期概率分布簇情形下给出了单周期生产分配供应鲁棒模型,这种近似鲁棒模型等价于一个线性规划问题。最后,通过已知一个产品的4个周期构成的混合分布簇进行了数值实验,数值结果表明了期望均值准则下的生产分配供应鲁棒模型的生产分配供应策略更加稳健。     

A Predictive Approach to Tail Probability Estimation

One of the issues contributing to the success of any extreme value modeling is the choice of the number of upper order statistics used for inference, or equivalently, the selection of an appropriate threshold. In this paper we propose a Bayesian predictive approach to the peaks over threshold method with the purpose of estimating extreme quantiles beyond the range of the data. In the peaks over threshold (POT) method, we assume that the threshold identifies a model with a specified prior probability, from a set of possible models. For each model, the predictive distribution of a future excess over the corresponding threshold is computed, as well as a conditional estimate for the corresponding tail probability. The unconditional tail probability for a given future extreme observation from the unknown distribution is then obtained as an average of the conditional tail estimates with weights given by the posterior probability of each model.     

离散型随机变量的次序统计量的分布

本文论述了离散型随机变量的次序统计量的分布律及其有关推论 .     

Intensity‐based estimation of extreme loss event probability and value at risk

We develop a methodology for the estimation of extreme loss event probability and the value at risk, which takes into account both the magnitudes and the intensity of the extreme losses. Specifically, the extreme loss magnitudes are modeled with a generalized Pareto distribution, whereas their intensity is captured by an autoregressive conditional duration model, a type of self‐exciting point process. This allows for an explicit interaction between the magnitude of the past losses and the intensity of future extreme losses. The intensity is further used in the estimation of extreme loss event probability. The method is illustrated and backtested on 10 assets and compared with the established and baseline methods. The results show that our method outperforms the baseline methods, competes with an established method, and provides additional insight and interpretation into the prediction of extreme loss event probability. Copyright © 2012 John Wiley & Sons, Ltd.     

Practical Extreme Value Modelling of Hydrological Floods and Droughts: A Case Study

Estimation of flood and drought frequencies is important for reservoir design and management, river pollution, ecology and drinking water supply. Through an example based on daily streamflow observations, we introduce a stepwise procedure for estimating quantiles of the hydrological extremes floods and droughts. We fit the generalised extreme value (GEV) distribution by the method of block maxima and the generalised Pareto (GP) distribution by applying the peak over threshold method. Maximum likelihood, penalized maximum likelihood and probability weighted moments are used for parameter estimation. We incorporate trends and seasonal variation in the models instead of splitting the data, and investigate how the observed number of extreme events, the chosen statistical model, and the parameter estimation method effect parameter estimates and quantiles. We find that a seasonal variation should be included in the GEV distribution fitting for floods using block sizes less than one year. When modelling droughts, block sizes of one year or less are not recommended as significant model bias becomes visible. We conclude that the different characteristics of floods and droughts influence the choices made in the extreme value modelling within a common inferential strategy.This revised version was published online in March 2005 with corrections to the cover date.     

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。     

Testing Extreme Value Conditions

A test statistic is developed that checks the validity of the extreme value conditions without specifiying the shape parameter of the limiting extreme value distribution.     

连续型指数差分布

本文提出了一个连续型随机变量的概率分布:指数差分布。讨论了该分布的极值、拐点、数学期望和方差,推导了参数的矩估计公式,探讨了该分布与指数分布的关系,给出了该分布在药代动力学中的应用。     

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.