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．     

基于ANP的装备质量管理风险评估模型

根据装备质量管理的特点对装备质量管理的风险因素进行了分析,依据指标体系的设计原则建立了装备质量管理风险评估的指标体系,并给出了基于网络层次分析法和粒子群优化算法的的装备质量管理风险评估指标权重值的确定方法.并给出了实例分析.     

基于TGARCH-t的混合Copula投资组合风险测度研究

在分析了现有Copula函数在测度投资组合风险不足的情况下,首先充分考虑资产波动的时变性、杠杆效应等特征,选择了TGARCH-t模型进行边缘分布建模.接着引入混合Copula模型来描述投资组合的复杂相关结构,同时利用构造的主对角线距离统计量等方法验证了混合Copula模型的优势.最后通过VaR的蒙特卡洛模拟结果看到,这种方法能更为精确的测度投资组合风险值.     

情绪对股票投资收益及后续投资决策的影响

金融学视角的研究使用市场变量代表投资者情绪,未能触及情绪本质.从心理学视角出发,使用行为金融实验方法,研究股票投资者的真实情绪对收益率及后续决策风险偏好的影响.结果发现:1)股价单边下跌时,产生适度消极情绪的个体收益最高.2)对于后续盈利情境的决策,前期产生积极情绪的个体倾向于规避风险,产生消极情绪的个体倾向于寻求风险;对于后续亏损情境的决策,个体均表现为寻求风险.3)股价单边上涨或下跌时,个体最容易出现的具体情绪分别是"倍受鼓舞"、"内疚"等.     

常利率复合二项双险种风险模型的研究

提出了含利率因素的复合二项双险种风险模型,并在有关假设的基础上,给出了此模型下保险公司稳定经营的必要条件;证明了索赔时刻的盈余过程是一马氏过程和调节系数的存在性,并采用递归方法得到了模型的破产概率的上界估计.     

中证开放式基金指数和GARCH-M模型

基于开放式基金指数周收益率时间序列的非正态性和厚尾特性,以中证开放式基金指数为例,运用GARCH-M模型进行研究,系统地分析我国不同类型的开放式基金的投资风险.实证分析表明:GARCH-M模型对中证开放式基金指数周收益率的拟合效果较好,并为预测我国开放式基金的投资风险提供了科学依据.