基于凸概度分布簇下WCVaR模型的有限逼近性

如何求解实际问题中Worst条件风险值模型是一个非常困难的问题,研究了凸概率分布簇下的WCVaR(Worst Conditional Value-at-Risk)模型等价性及其在序列分布簇下的有限逼近性,根据概率分布簇的VaR测度值,定义了WCVaR风险测度值和对应的WCVaR模型,证明了WCVaR模型等价一个另一个数学规划问题求解.在一定条件下,证明了在损失有界情形用有限个分布簇就可以足够近似计算WCVaR模型的最优解,因此,对于解决稳健型条件风险值模型具重要的实际价值.     

基于半范数的风险测度

由于一致性风险测度公理中的平移不变性公理存在不合理性,故可将该该公理从一致性风险测度公理中去掉.将半范数的概念加以扩展,增加单调性要求,则去掉平移不变性公理之后一致性风险测度公理与半范数的要求就完全相同,这样风险度量从本质上讲就是定义在某空间上的半范数.本文发现F ishburn的风险测度是满足正齐次性、次可加性、单调性要求的.从这个意义讲,F ishburn的风险测度是一个比较科学的风险度量方法.     

证券投资组合优化模型的进一步研究

本提出了正离差负离差的概念；并运用它们分别来衡量风险回报和风险损失；并提出了一个反映风险回报率和风险损失率的信息量k。     

基于蒙特卡罗模拟的供应链风险估计研究

以市场需求波动风险为例,基于蒙特卡罗模拟研究了供应链风险估计问题.首先,对市场需求波动风险及其损失度量进行理论分析,利用市场需求波动风险情境下的供应链系统库存成本损失来度量市场需求波动风险的损失.其次,选择供应链末端需求为蒙特卡罗方法待模拟的随机变量,基于需求建立了市场需求波动风险概率测度模型和风险损失度量模型,确定了市场需求波动风险概率和风险损失为需求的相关量.然后,通过实例的仿真求解验证了模型.最后,给出了利用本模型方法进行供应链风险估计时需要注意的问题及进一步研究的问题.研究表明:蒙特卡罗方法对供应链风险估计具有较强的鲁棒性.     

摩擦市场的均值-离差证券组合选择模型

本文给出了基于历史收益率数据的均值 -平均绝对离差型证券组合投资模型 .该模型采用收益的平均绝对离差作为风险的尺度 ,可以通过求解线性规划来获的摩擦市场 (如具有税收和交易费 )最优投资组合 ,避免了均值 -方差模型求解二次规划的复杂性 .     

基于污染Gamma分布的聚合风险模型及其在风险分类中的应用

分析了污染Gamma分布及其性质,讨论了基于污染Gamma分布的聚合风险模型.对模型的概率特性和参数估计进行了分析,并对该模型在风险分类中的应用进行了讨论.为克服索赔总量的分布函数在计算上的困难,利用同单调性理论得到了随机凸序意义下索赔总量随机变量S的随机上界Sc,对Sc的分布函数及限额损失保费进行了讨论.通过一个例子对所述结论的有效性进行验证.     

经济代理人的最优损失控制

根据经济代理人对风险的厌恶程度高低将影响收益效用的事实,通过对经济代理人的风险厌恶系数引进阀值,使风险厌恶系数在不同取值区间对应不同的效用函数,对经济代理人具有常值绝对风险厌恶的效用函数,损失服从泊松分布、且保险市场被垄断的承保人控制的最优防损活动进行了研究.研究表明,风险厌恶系数、损失的大小以及防损效率将影响最优防损...     

多损失WCVaR模型的等价性定理

研究了多概率分布簇下的多损失下的WCVaR(Multi Worst Conditional Value-at-Risk)模型等价性定理, 根据概率分布簇的VaR测度值, 定义了多损失下的WCVaR风险测度值和对应的多目标优化模型(MWCVaR), 证明了多目标优化模型(MWCVaR)等价另一个多目标优化模型求解. 对于有限分布簇情形, 在一定条件下, 证明了用有限个分布簇就可以近似计算多损失(MWCVaR)优化模型.     

一个非对称线性风险函数与证券组合投资分析

在分析Jia&D yer的风险-价值理论基础上,给出了一个基于预先给定的目标收益的非对称线性风险函数.该风险函数是低于参考点的离差和高于参考点的离差的加权和,它利用一阶"上偏矩"来修正一阶下偏矩,进一步建立了在此非对称风险函数下的线性规划证券投资组合模型;并证明了该模型与二阶随机占优准则的一致性;最后通过上海证券市场的实际数据验证了该模型的有效性和实用性.     

竞争环境下多个损失规避零售商的供应链回购契约

研究由单个风险中性的供应商与多个竞争的损失厌恶零售商组成的两阶段供应链,在回购契约中考察竞争和零售商的损失厌恶态度对其最优订购决策和整个供应链协调性的影响.应用博弈论的方法,证明了该供应链博弈存在唯一的纯策略Nash均衡,而且竞争使得零售商的总订购量上升,而损失规避使得总订购量下降.竞争的存在削弱了损失厌恶效应对整个供应链协调性的影响.研究还发现,零售商的最优订购量随供应商的批发价增大而增大,随回购价格的增大而减少,并且在一定条件下回购契约可以使得供应链达到协调.     

An overview of representation theorems for static risk measures

In this paper, we give an overview of representation theorems for various static risk measures: coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity, law-invariant coherent or convex risk measures, risk measures with comonotonic subadditivity or convexity and respecting stochastic orders. This work was supported by National Natural Science Foundation of China (Grant No. 10571167), National Basic Research Program of China (973 Program) (Grant No. 2007CB814902), and Science Fund for Creative Research Groups (Grant No. 10721101)     

A new class of coherent risk measures based on p‐norms and their applications

To exercise better control on the lower tail of the loss distribution and to easily describe the investor's risk attitude, a new class of coherent risk measures is proposed in this paper by taking the minimization of p‐norms of lower losses with respect to some reference point. We demonstrate that the new risk measure has satisfactory mathematical properties such as convexity, continuity with respect to parameters included in its definition, the relations between two new risk measures are also examined. The application of the new risk measures for optimal portfolio selection is illustrated by using trade data from the Chinese stock markets. Empirical results not only support our theoretical conclusions, but also show the practicability of the portfolio selection model with our new risk measures. Copyright © 2006 John Wiley & Sons, Ltd.     

基于违约风险的Vasicek利率风险研究

本文将非瞬时利率作为状态变量,通过Vasicek双因素期限结构模型得到了随机久期和凸度,并且讨论了考虑违约风险的Vasicek随机久期和凸度,使得对债券进行投资时,用Vasicek模型进行利率风险管理更加符合实际情况。     

Risk aggregation with dependence uncertainty

Risk aggregation with dependence uncertainty refers to the sum of individual risks with known marginal distributions and unspecified dependence structure. We introduce the admissible risk class to study risk aggregation with dependence uncertainty. The admissible risk class has some nice properties such as robustness, convexity, permutation invariance and affine invariance. We then derive a new convex ordering lower bound over this class and give a sufficient condition for this lower bound to be sharp in the case of identical marginal distributions. The results are used to identify extreme scenarios and calculate bounds on Value-at-Risk as well as on convex and coherent risk measures and other quantities of interest in finance and insurance. Numerical illustrations are provided for different settings and commonly-used distributions of risks.     

Subdifferential representations of risk measures

Measures of risk appear in two categories: Risk capital measures serve to determine the necessary amount of risk capital in order to avoid ruin if the outcomes of an economic activity are uncertain and their negative values may be interpreted as acceptability measures (safety measures). Pure risk measures (risk deviation measures) are natural generalizations of the standard deviation. While pure risk measures are typically convex, acceptability measures are typically concave. In both cases, the convexity (concavity) implies under mild conditions the existence of subgradients (supergradients). The present paper investigates the relation between the subgradient (supergradient) representation and the properties of the corresponding risk measures. In particular, we show how monotonicity properties are reflected by the subgradient representation. Once the subgradient (supergradient) representation has been established, it is extremely easy to derive these monotonicity properties. We give a list of Examples.     

Robust risk management

Estimating the probabilities by which different events might occur is usually a delicate task, subject to many sources of inaccuracies. Moreover, these probabilities can change over time, leading to a very difficult evaluation of the risk induced by any particular decision. Given a set of probability measures and a set of nominal risk measures, we define in this paper the concept of robust risk measure as the worst possible of our risks when each of our probability measures is likely to occur. We study how some properties of this new object can be related with those of our nominal risk measures, such as convexity or coherence. We introduce a robust version of the Conditional Value-at-Risk (CVaR) and of entropy-based risk measures. We show how to compute and optimize the Robust CVaR using convex duality methods and illustrate its behavior using data from the New York Stock Exchange and from the NASDAQ between 2005 and 2010.     

Strong displacement convexity on Riemannian manifolds

Ricci curvature bounds in Riemannian geometry are known to be equivalent to the weak convexity (convexity along at least one geodesic between any two points) of certain functionals in the space of probability measures. We prove that the weak convexity can be reinforced into strong (usual) convexity, thus solving a question left open in Lott and Villani (Ann of Math, to appear). C. Villani is member of the Institut Universitaire de France.     

Newsvendor solutions via conditional value-at-risk minimization

In this paper, we consider the minimization of the conditional value-at-risk (CVaR), a most preferable risk measure in financial risk management, in the context of the well-known single-period newsvendor problem, which is originally formulated as the maximization of the expected profit or the minimization of the expected cost. We show that downside risk measures including the CVaR are tractable in the problem due to their convexity, and consequently, under mild assumptions on the probability distribution of products’ demand, we provide analytical solutions or linear programming (LP) formulation of the minimization of the CVaR measures defined with two different loss functions. Numerical examples are also exhibited, clarifying the difference among the models analyzed in this paper, and demonstrating the efficiency of the LP solutions.     

Credit portfolio risk and asset price cycles

It is a stylized fact that credit risk is high at the same time when asset values are depressed. However, most of the standard credit risk models ignore this kind of correlation, leading to underestimation of risk measures of portfolio credit risk such as Value at Risk and Expected Shortfall. In our paper we make an attempt to quantify the underestimation of these risk measures when the dependence between credit risk and asset values is ignored and show that credit risk is underestimated by a significant margin.      

DEA with efficiency classification preserving conditional convexity

We propose to relax the standard convexity property used in Data Envelopment Analysis (DEA) by imposing additional qualifications for feasibility of convex combinations. We specifically focus on a condition that preserves the Koopmans efficiency classification. This yields an efficiency classification preserving conditional convexity property, which is implied by both monotonicity and convexity, but not conversely. Substituting convexity by conditional convexity, we construct various empirical DEA approximations as the minimal sets that contain all DMUs and are consistent with the imposed production assumptions. Imposing an additional disjunctive constraint to standard convex DEA formulations can enforce conditional convexity. Computation of efficiency measures relative to conditionally convex production set can be performed through Disjunctive Programming (DP).