Concave distortion risk minimizing reinsurance design under adverse selection

This article makes use of the well-known Principal–Agent (multidimensional screening) model commonly used in economics to analyze a monopolistic reinsurance market in the presence of adverse selection, where the risk preference of each insurer is guided by its concave distortion risk measure of the terminal wealth position; while the reinsurer, under information asymmetry, aims to maximize its expected profit by designing an optimal policy provision (menu) of “shirt-fit” stop-loss reinsurance contracts for every insurer of either type of low or high risk. In particular, the most representative case of Tail Value-at-Risk (TVaR) is further explored in detail so as to unveil the underlying insight from economics perspective.     

Risk optimization with p-order conic constraints: A linear programming approach

The paper considers solving of linear programming problems with p-order conic constraints that are related to a certain class of stochastic optimization models with risk objective or constraints. The proposed approach is based on construction of polyhedral approximations for p-order cones, and then invoking a Benders decomposition scheme that allows for efficient solving of the approximating problems. The conducted case study of portfolio optimization with p-order conic constraints demonstrates that the developed computational techniques compare favorably against a number of benchmark methods, including second-order conic programming methods.     

Efficient risk simulations for linear asset portfolios in the t-copula model

We consider the problem of calculating tail probabilities of the returns of linear asset portfolios. As a flexible and accurate model for the logarithmic returns we use the t-copula dependence structure and marginals following the generalized hyperbolic distribution. Exact calculation of the tail-loss probabilities is not possible and even simulation leads to challenging numerical problems. Applying a new numerical inversion method for the generation of the marginals and importance sampling with carefully selected mean shift we develop an efficient simulation algorithm. Numerical results for a variety of realistic portfolio examples show an impressive performance gain.     

Fuzzy multi-objective programming for supplier selection and risk modeling: A possibility approach

Selection of supply chain partners is an important decision involving multiple criteria and risk factors. This paper proposes a fuzzy multi-objective programming model to decide on supplier selection taking risk factors into consideration. We model a supply chain consisting of three levels and use simulated historical quantitative and qualitative data. We propose a possibility approach to solve the fuzzy multi-objective programming model. Possibility multi-objective programming models are obtained by applying possibility measures of fuzzy events into fuzzy multi-objective programming models. Results indicate when qualitative criteria are considered in supplier selection, the probability of a certain supplier being selected is affected.     

A multicriteria model for risk sorting of natural gas pipelines based on ELECTRE TRI integrating Utility Theory

This paper proposes a multicriteria model for assessing risk in natural gas pipelines, and for classifying sections of pipeline into risk categories. The model integrates Utility Theory and the ELECTRE TRI method. It aims to help transmission and distribution companies, when engaged in risk management and decision-making, to consider the multiple dimensions of risk that may arise from pipeline accidents. Pipeline hazard scenarios are presented, and it is argued that the assessment of risk in natural gas pipelines should not be based solely on probabilities of human fatalities, but should involve a wider perspective that simultaneously takes into consideration the human, environmental and financial dimensions of impacts of pipeline accidents. Finally, in order to verify the effectiveness of the model set out, a numerical application based on a real case study is presented.     

Risk management strategies via minimax portfolio optimization

Recent extreme economic developments nearing a worst-case scenario motivate further examination of minimax linear programming approaches for portfolio optimization. Risk measured as the worst-case return is employed and a portfolio from maximizing returns subject to a risk threshold is constructed. Minimax model properties are developed and parametric analysis of the risk threshold connects this model to expected value along a continuum, revealing an efficient frontier segmenting investors by risk preference. Divergence of minimax model results from expected value is quantified and a set of possible prior distributions expressing a degree of Knightian uncertainty corresponding to risk preference determined. The minimax model will maximize return with respect to one of these prior distributions providing valuable insight regarding an investor’s risk attitude and decision behavior. Linear programming models for financial firms to assist individual investors to hedge against losses by buying insurance and a model for designing variable annuities are proposed.     

基于复杂网络理论的含分布式发电的电力网络脆弱度评估

基于复杂网络理论研究含分布式发电(DG, Distributed Generation)的电力网络脆弱度评估问题,有针对性地提出三类脆弱度评估指标,其中基于结构的脆弱度指标能够体现网络拓扑和节点功率对系统供电效率的影响;攻击脆弱度指标可用于评估系统抵御节点和线路移除的能力;基于运行方式的脆弱度指标能够反映整个电网有功功率在传输距离上的均衡度.仿真算例验证了所提指标的有效性和DG对于改善系统功率传输性能与提高抗干扰能力方面的作用.     

一种改进的DRASTIC模型及其在地下水脆弱性评价中的应用

针对DRASTIC模型在地下水脆弱性评价应用中存在的不足,将模糊分析评价理论及三标度分两步的层次分析法引入地下水脆弱性评价中,构建了一套改进的DRASTIC模型,并将其运用到祁县东观镇地下水脆弱性评价的实例研究中,计算结果表明东观镇地下水脆弱性大致呈正态分布,偏低脆弱性地区占评价区域的38.3%,偏高脆弱性地区占61.7%,整体上,地下水系统易受污染、脆弱性偏高,与实际情况基本吻合.与传统DRASTIC模型相比,改进模型算得的地下水脆弱性等级变化更灵敏、分布范围更精确,更值得推广应用.     

基于ELECTRE-IN方法的装备战场威胁评估

针对ELECTRE法处理不确定的模糊信息能力弱的缺点,将ELECTRE-IN法拓展到了模糊环境下,用来进行装备战场的威胁评估,决策评估中涉及到的属性值与属性权重值等模糊信息以三角模糊数形式给出.首先根据三角模糊数互补判断矩阵完全一致性的概念,通过建立一个多层次非线性规划,求解出三角模糊数型的属性权重向量;其次集结决策者对各方案属性的评价,确定出模糊决策矩阵;最后按照模糊ELECTRE-IN方法的步骤进行威胁评估,得到决策方案的排序.     

考虑供应链影响时基于粗糙集方法的企业信贷风险评估

对于考虑供应链时的企业信贷风险评估问题,提出基于粗糙集的解决办法.首先,根据样本数据建立决策信息表;然后采用等间距法对决策信息表的连续属性值进行离散化,并且应用辨识矩阵求出最小约简;最后,应用启发式值约简算法求出决策规则.试验计算结果表明,所提出的方法对企业的信贷等级能够进行有效的预测.