随机利率下的年金的计算

我们考虑随机利率下的一类延付年金在n年后的积累值的计算问题,目的在于研究积累值的期望和方差.本文给出两种方法计算在某些年内一类延付年金的积累值的期望和方差,获得了积累值的方差的递推关系,并且给出了计算公式.     

随机利率下的一类特殊年金

研究在随机利率相互独立条件下的某些延付年金的积累值的计算问题,目的在于研究积累值的期望和方差.研究了在随机利率相互独立条件下的期末付虹式年金,期末付平顶虹式年金,期末付倒虹式年金和期末付倒平顶虹式年金的积累值的期望和方差,并且给出了积累值的期望和方差的计算公式.     

模糊随机环境下的连续型变额生命年金

采用模糊随机理论,构建连续支付型变额生命年金模型.假定利率为三角模糊数,死亡率为随机变量.结合精算理论,给出了连续支付型变额生命年金精算现值的期望、方差以及分布函数和分位数的模糊表达式.最后,通过实证分析计算出一个在养老保险中常见的生命年金的相关值,验证模型的可行性.     

一类随机利率下特殊年金的计算

研究在随机利率相互独立条件下年金初值的计算问题,主要是研究各类年金初值的期望和方差的计算公式.文章给出了随机利率相互独立条件下期末付平顶虹式年金,期末付虹式年金,期末付倒平顶虹式年金和期末付倒虹式年金的初值公式及其简化关系,推导出了这类年金初值的各阶矩的简化公式,进而获得了这类年金初值的期望计算公式和方差计算公式.     

随机利息力下的一类年金的时间价值

对于年金的时间价值的研究,往往假定利率在整个期间内是固定不变的,但事实上,由于受到多种因素的影响,利率通常具有不确定性.因此,本文采用可逆MA(1)模型对随机利息力进行建模,在此基础上,研究了期末付虹式年金和期末付平顶虹式年金的时间价值问题,给出了上述两种形式年金现值的期望和方差的递推公式.通过数值仿真分析了相关参数对...     

随机利息力下的期末付倒平顶虹式年金

对于年金的定价问题的研究,传统精算理论假定利率是恒定不变的．但事实上,由于受到多种因素的影响,利率往往具有不确定性．因此,本文采用可逆MA（1）模型来刻画利率期限机构,在此基础上,研究了期末付倒平顶虹式年金的各阶矩问题,推导出了其年金现值的期望和方差的简洁公式．通过数值模拟分析了此年金面临的利率风险,其结论对年金定价有一定的参考价值．     

基于ARMA(p,q)利息力生存年金精算现值模型

企业年金是养老保险体系的重要组成部分,其定价的合理性正受到越来越多的关注.主要是基于一般的ARM A(p,q)模型得到了随机利率下生存年金的精算现值模型,分别给出了年金给付的一阶矩和二阶矩,这对年金保险的合理收费和避免收不抵支情况的出现具有重要的指导意义.     

随机利率下的增额寿险模型研究

在实际的保险精算中,保单保险金现值函数的期望就是该种保单的纯保费,而方差常用来度量该种保单的风险.对随机利率采用W iener过程建模,得到了增额寿险保险金现值函数的期望和方差.     

随机利率下年金的时间价值

本文首先在常数利率下讨论了递增年金、递减年金和固定增长年金的终值.进而在随机利率条件下研究了递增年金、递减年金和固定增长年金,得到了递增年金、递减年金和固定增长年金终值的期望与方差,推广了Zaks(2001)的结果.     

变额年金与变额寿险给付额的计算

目前在人寿保险中，如何对付通货膨胀的不良影响，使对被保人的实际保障不致降低是一个重要问题。变额年金与变额寿险是因应通货膨胀的寿险产品中最有效的，但其给付额的计算比较复杂。本文扰某些变额年金与交额寿险保单的给付额的确定做了推导，并分析了利差在克服通货膨胀影响中的作用。     

Mean and variance optimization of non–linear systems and worst–case analysis

In this paper, we consider expected value, variance and worst–case optimization of nonlinear models. We present algorithms for computing optimal expected value, and variance policies, based on iterative Taylor expansions. We establish convergence and consider the relative merits of policies based on expected value optimization and worst–case robustness. The latter is a minimax strategy and ensures optimal cover in view of the worst–case scenario(s) while the former is optimal expected performance in a stochastic setting. Both approaches are used with a small macroeconomic model to illustrate relative performance, robustness and trade-offs between the alternative policies.     

Using fuzzy random variables in life annuities pricing

This paper develops life annuity pricing with stochastic representation of mortality and fuzzy quantification of interest rates. We show that modelling the present value of annuities with fuzzy random variables allows quantifying their expected price and risk resulting from the uncertainty sources considered. So, we firstly describe fuzzy random variables and define some associated measures: the mathematical expectation, the variance, distribution function and quantiles. Secondly, we show several ways to estimate the discount rates to price annuities. Subsequently, the present value of life annuities is modelled with fuzzy random variables. We finally show how an actuary can quantify the price and the risk of a portfolio of annuities when their present value is given by means of fuzzy random variables.     

Gini indices and the moments of the share density function

The expected value of the share density of the income distribution can be expressed in terms of the Gini index. The variance of the share density of the income distribution is interesting because it gives a relationship between the first and the second order Gini indices. We find an expression for this variance and, as a result, we obtain some nontrivial bounds on these Gini indices. We propose new statistics on the income distribution based on the higher moments of the share density function. These new statistics are easily computable from the higher order Gini indices. Relating these moments to higher order Ginis suggests new estimates on these quantities.     

Generalizing variance to allow the inclusion of decision attitude in decision making under uncertainty

The problem of decision making under uncertainty is considered. It is noted that an alternative is described in terms of an uncertainty profile. We observe that a major difficulty in the decision process is the comparison of these uncertainty profiles. We discuss the need for introducing some features of an uncertainty profile to help simplify this comparison. We note that the quantification of these simplifying features involves some subjective considerations about the decision makers preferences. We introduce the idea of the decision maker’s attitudinal character to help in the formulation of these considerations. We then investigate two important features associated with an uncertainty profile. The first, the representative value, is a generalization of expected value commonly used under probabilistic uncertainty. The second, called the measure of deviation, provides a generalization of the concept of variance. We show how these new measures allows us to consider uncertainty profiles other then just the probabilistic one. They also allow us introduce other decision maker attitudes then the one implicitly assumed with the expected value and variance.     

Optimal reinsurance and investment policies with the CEV stock market

In this paper, under the criterion of maximizing the expected exponential utility of terminal wealth, we study the optimal proportional reinsurance and investment policy for an insurer with the compound Poisson claim process. We model the price process of the risky asset to the constant elasticity of variance (for short, CEV) model, and consider net profit condition and variance reinsurance premium principle in our work. Using stochastic control theory, we derive explicit expressions for the optimal policy and value function. And some numerical examples are given.     

Monte Carlo methods for mean-risk optimization and portfolio selection

Stochastic programming is a well-known instrument to model many risk management problems in finance. In this paper we consider a stochastic programming model where the objective function is the variance of a random function and the constraint function is the expected value of the random function. Instead of using popular scenario tree methods, we apply the well-known sample average approximation (SAA) method to solve it. An advantage of SAA is that it can be implemented without knowing the distribution of the random data. We investigate the asymptotic properties of statistical estimators obtained from the SAA problem including examining the rate of convergence of optimal solutions of the SAA problem as sample size increases. By using the classical penalty function technique and recent results on uniform exponential convergence of sample average random functions, we show that under some mild conditions the statistical estimator of the optimal solution converges to its true counterpart at an exponential rate. We apply the proposed model and the numerical method to a portfolio management problem and present some numerical results.     

方差保费准则下的最优脉冲控制

本文研究保险公司在有再保险控制下的最优脉冲分红问题. 对保险公司的理赔损失, 假定有两家再保险公司参与分保, 且保险公司与两家再保险公司采取不同参数下的方差保费准则. 进一步, 假定保险公司有股东红利分配, 且每次分红有固定交易费和比例税收, 即脉冲分红. 在扩散逼近模型下, 本文应用随机动态规划方法研究破产前的最大期望折现分红, 给出值函数的解析表达式, 进而获得最优再保险策略和分红策略的具体形式.     

The covariance of uncertain variables: definition and calculation formulae

Uncertainty theory as a branch of axiomatic mathematics has been widely used to deal with human uncertainty. The two commonly used numerical characteristics of uncertain variables, the expected value and the variance together with their mathematical properties have been discussed and applied to real optimization problems in an uncertain environment. As a further study, in this paper, we focus on the covariance and correlation coefficient of uncertain variables. The definitions and calculation formulae of covariance and correlation coefficient of two uncertain variables are suggested by means of their inverse distributions. Then we show that the correlation coefficient of uncertain variables is essentially a measure of the relevance of distributions of uncertain variables. Finally, the relation between variance and covariance is analysed and represented with some equalities and inequalities.     

A Three-Field Finite Element Method for Elliptic Partial Differential Equations Driven by Stochastic Loads

Abstract

This paper is concerned with the application of nonconforming finite element methods to stochastic partial differential equations. We present a mixed formulation of a three-field finite element method applied to an elliptic model problem involving stochastic loads. We then derive the exact form for the expected value and variance of the solution. Additionally, the rate of convergence for the stochastic error is presented. Finally, we demonstrate through numerical experiments that the method is robust and reliable.