基于生命周期和交叠世代分析的养老风险管理文献综述及其启示

随着长寿风险的加剧以及人口死亡率的降低，养老风险管理的研究逐渐受到政府和学术界的广泛关注。本文系统梳理了养老风险相关驱动因素(死亡率和长寿风险)的研究，并介绍了生命周期框架与交叠世代模型(OLG)的建模过程，还对生命周期框架和OLG模型的应用研究进行了文献综述。从文献回顾来看，当前研究存在一些不足，未来的研究应基于我国人口生存特征去研究死亡率；同时，需要从生命周期框架和OLG模型研究研究应对养老风险的稳健的投资-消费决策，并在考虑对上一代老人赡养的情形下研究寿险决策。此外，随着养老风险逐步加剧，建议政府采取改进我国的多支柱养老保险体系、进一步放开生育、延迟退休及发展养老服务业等方式应对养老风险，保证我国养老金体系的可持续发展。     

Finite Time Ruin Probabilities and Large Deviations for Generalized Compound Binomial Risk Models

In this paper, we extend the classical compound binomial risk model to the case where the premium income process is based on a Poisson process, and is no longer a linear function. For this more realistic risk model, Lundberg type limiting results for the finite time ruin probabilities are derived. Asymptotic behavior of the tail probabilities of the claim surplus process is also investigated.     

一类离散时间带随机收入风险模型的带壁分红问题

我们给出了一类离散时间的具有随机收入的非寿险风险模型,研究了该模型的常数壁分红问题.得到了该模型破产发生时Gerber-Shiu折扣惩罚函数.考虑了破产时的期望,有限时间破产概率.最后我们给出了一个例.     

开放式基金流动性指标研究

本文主要介绍了开放式投资基金的流动性概念和指标体系设计，并运用指标进行实证检验，分析基金的流动性及其实际意义。     

多类型复发间隔时间下广义半参数风险模型

本文在多类型复发间隔时间数据下,研究了一类广义半参数风险回归模型的参数估计问题,给出了该模型中未知参数和非参数函数的一种估计方法,并证明了估计量的相合性和渐近正态性.最后利用数值模拟来评估估计量在有限样本下的表现.     

Risk models for the Prize Collecting Steiner Tree problems with interval data

Given a connected graph G=(V,E)with a nonnegative cost on each edge in E,a nonnegative prize at each vertex in V,and a target set V′V,the Prize Collecting Steiner Tree(PCST)problem is to find a tree T in G interconnecting all vertices of V′such that the total cost on edges in T minus the total prize at vertices in T is minimized.The PCST problem appears frequently in practice of operations research.While the problem is NP-hard in general,it is polynomial-time solvable when graphs G are restricted to series-parallel graphs.In this paper,we study the PCST problem with interval costs and prizes,where edge e could be included in T by paying cost xe∈[c e,c+e]while taking risk(c+e xe)/(c+e c e)of malfunction at e,and vertex v could be asked for giving a prize yv∈[p v,p+v]for its inclusion in T while taking risk(yv p v)/(p+v p v)of refusal by v.We establish two risk models for the PCST problem with interval data.Under given budget upper bound on constructing tree T,one model aims at minimizing the maximum risk over edges and vertices in T and the other aims at minimizing the sum of risks over edges and vertices in T.We propose strongly polynomial-time algorithms solving these problems on series-parallel graphs to optimality.Our study shows that the risk models proposed have advantages over the existing robust optimization model,which often yields NP-hard problems even if the original optimization problems are polynomial-time solvable.     

A class of weighted estimators for additive hazards model in case-cohort studies

Case-cohort sampling is a commonly used and efficient method for studying large cohorts. In many situations, some covariates are easily measured on all cohort subjects, and surrogate measurements of the expensive covariates also may be observed. In this paper, to make full use of the covariate data collected outside the case-cohort sample, we propose＇a class of weighted estimators with general time-varying weights for the additive hazards model, and the estimators are shown to be consistent and asymptotically normal. We also identify the estimator within this class that maximizes efficiency, and simulation studies show that the efficiency gains of the proposed estimator over the existing ones can be substantial in practical situations. A real example is provided.     

金融风险中违约传染效应的研究

市场中的金融资产作为一个整体,往往具有相当复杂的相依风险。本文对信用风险违约的相依性进行研究,提出一种传染效应下违约预报模型,运用加权平均的极大似然方法获得违约强度的估计。并通过对2004年至2008年美国银行业、汽车业和房地产业的数据进行实证分析,得出三大行业间存在着明显的违约风险传染的结论。     

折现率离散时间风险模型下最大赤字问题

在引入折现率的条件下研究离散时间风险模型,运用递推方法和全概率公式,得到了破产前盈余,破产后赤字以及它们的联合分布所满足的微分积分方程,作为推论得到了破产概率所满足的微积分方程并得出结论.     

连锁超市供应链风险评价模型及实证研究

随着经济的飞速发展,供应链风险管理问题越来越受到企业的重视．但国内外还缺乏对连锁超市供应链风险的深入研究．事实上,很多连锁超市的供应链上很多环节并不完善．本文分析了连锁超市供应链上所面临的风险,构建了连锁超市供应链风险评价模型,以某连锁超市为例,通过调查问卷与所得数据,并利用熵权法确定了各指标的权重,最后基于模糊综合评价法得出的结果,提出了降低连锁超市供应链风险的对策建议．