基于两时段的WCVaR风险分析及其应用

针对随机变量的分布信息不完全的情况下,提出了两时段的Worst-Case Conditional Valueat-Risk(WCVaR)指标,并建立了两时段的风险-利润投资组合优化模型,该模型是一高维问题,具有复杂的优化结构.在损失函数为线性以及随机变量为离散界约束分布的假设下,运用最优化对偶理论将具有多层min-ma...     

多周期多目标条件风险值模型

在商业、工业、电力和房地产等行业中存在许多复杂的多周期风险决策问题,它的数学模型研究对于解决这些问题具有重要的作用.作者建立了一种新的多周期多目标条件风险值(CVaR)数学模型理论和方法.先定义了一种带时间段的多周期多目标损失函数下的α-VaR和α-CVaR值,给出了一类多周期多目标CVaR最优化模型.然后,证明了多目标意义下的对应模型的等价定理,给出了多周期多目标CVaR模型的近似求解等价模型.最后,建立了一种生产企业在供过于求和供不应求两种情形下产生的多周期双目标CVaR模型,针对一个电力生产企业进行的数值实验,表明了模型可以得到在最小供给的用电损失分布下的各周期下的相匹配供电策略,可以帮助供电部门各个时期供电不平衡状况下的风险控制.     

基于CVaR分解的市场信息结构研究

从证券投资风险发生的信息角度,利用风险分解方法研究了证券市场里的信息结构问题。使用了条件风险价值作为风险计量方法,并将风险发生的信息机制分解为公共信息效应和私有信息效应,利用信息分布函数来标识不同信息的分布状况。通过计算公共信息指数和私有信息指数,利用中国股票市场的实际数据,对中国的证券市场的信息结构问题进行了研究。     

Robust reward–risk ratio optimization with application in allocation of generation asset

In this article, we study reward–risk ratio models under partially known message of random variables, which is called robust (worst-case) performance ratio problem. Based on the positive homogenous and concave/convex measures of reward and risk, respectively, the new robust ratio model is reduced equivalently to convex optimization problems with a min–max optimization framework. Under some specially partial distribution situation, the convex optimization problem is converted into simple framework involving the expectation reward measure and conditional value-at-risk measure. Compared with the existing reward–risk portfolio research, the proposed ratio model has two characteristics. First, the addressed problem combines with two different aspects. One is to consider an incomplete information case in real-life uncertainty. The other is to focus on the performance ratio optimization problem, which can realize the best balance between the reward and risk. Second, the complicated optimization model is transferred into a simple convex optimization problem by the optimal dual theorem. This indeed improves the usability of models. The generation asset allocation in power systems is presented to validate the new models.     

基于Copula的供应链金融质物组合价格风险测度研究

本文选取白银、铝和铜三种供应链金融质物作为研究对象,在分析三种质物收益率统计特征的基础上,引入Copula模型刻画供应链金融业务中质物收益率的“尖峰厚尾”特征以及质物收益率之间的非线性相关结构;采用Monte Carlo模拟方法测度考虑到极端情况下的质物组合价格风险值CVaR;利用时间平方根法则测度长周期视角下质物组合的价格风险。将CVaR与VaR测度结果进行对比,比较分析短期价格风险与长期价格风险,将Copula模型与传统风险测度方法下计算出的风险值进行对比,以期选取最优测度供应链金融质物组合长期价格风险模型。研究结果表明:从单一质物价格波动特征来看,三种单一质物的收益率均存在非正态分布和“尖峰厚尾”特征,具有一般金融资产收益率分布的特点。从模型的有效性来看,第一,CVaR比VaR能够更好地、全面地测度供应链金融质物组合的价格风险;第二,基于Copula模型的风险测度结果比传统集成风险测度结果的准确性高;第三,平方欧式距离法结果表明在五种Copula模型中,t-Copula是最优刻画供应链金融质物组合收益率间的相依关系的模型。从长短期风险测度结果来看,随着风险期限的增加,质物组合的价格风险值随之增大,以往研究中用短期风险测度往往会低估商业银行所面临的价格风险,不利于商业银行资金信贷的优化配置。得到的结论对我国商业银行开展供应链金融业务防范价格风险提供了量化支持。     

Robust asset allocation with conditional value at risk using the forward search

The well‐known Markowitz approach to portfolio allocation, based on expected returns and their covariance, seems to provide questionable results in financial management. One motivation for the pitfall is that financial returns have heavier than Gaussian tails, so the covariance of returns, used in the Markowitz model as a measure of portfolio risk, is likely to provide a loose quantification of the effective risk. Additionally, the Markowitz approach is very sensitive to small changes in either the expected returns or their correlation, often leading to irrelevant portfolio allocations. More recent allocation techniques are based on alternative risk measures, such as value at risk (VaR) and conditional VaR (CVaR), which are believed to be more accurate measures of risk for fat‐tailed distributions. Nevertheless, both VaR and CVaR estimates can be influenced by the presence of extreme returns. In this paper, we discuss sensitivity to the presence of extreme returns and outliers when optimizing the allocation, under the constraint of keeping CVaR to a minimum. A robust and efficient approach, based on the forward search, is suggested. A Monte Carlo simulation study shows the advantages of the proposed approach, which outperforms both robust and nonrobust alternatives under a variety of specifications. The performance of the method is also thoroughly evaluated with an application to a set of US stocks.     

条件VaR和条件CVaR的核估计及其实证分析

VaR和CVaR是目前两种主流风险度量工具。条件VaR和条件CVaR是基于市场风险因子在已知条件(或信息)下的分布来计量和测算VaR和CVaR,能够及时地根据变化的条件来重新估计风险进而进行有效的风险管理,是对传统的基于边际分布的VaR和CVaR指标的有益补充。另外一方面,近年来非参数核估计方法因模型设定灵活、方便处理变量相依结构等优点备受关注。在本文,我们用条件VaR和条件CVaR的非参数核估计法,对我国A股市场的风险进行测算。结果得出:条件VaR和条件CVaR能揭示出深证成指和上证综指之间的不同风险特征;条件VaR和条件CVaR的测算结果并非总是一致;系统风险估计值对已知条件的敏感性高于深发展A和万科A两只股票的个股风险。以上风险特征在边际VaR和边际CVaR下无法得到。     

Stochastic Variational Inequality Approaches to the Stochastic Generalized Nash Equilibrium with Shared Constraints

In this paper, we consider the generalized Nash equilibrium with shared constraints in the stochastic environment, and we call it the stochastic generalized Nash equilibrium. The stochastic variational inequalities are employed to solve this kind of problems, and the expected residual minimization model and the conditional value-at-risk formulations defined by the residual function for the stochastic variational inequalities are discussed. We show the risk for different kinds of solutions for the stochastic generalized Nash equilibrium by the conditional value-at-risk formulations. The properties of the stochastic quadratic generalized Nash equilibrium are shown. The smoothing approximations for the expected residual minimization formulation and the conditional value-at-risk formulation are employed. Moreover, we establish the gradient consistency for the measurable smoothing functions and the integrable functions under some suitable conditions, and we also analyze the properties of the formulations. Numerical results for the applications arising from the electricity market model illustrate that the solutions for the stochastic generalized Nash equilibrium given by the ERM model have good properties, such as robustness, low risk and so on.     

Minimizing loss probability bounds for portfolio selection

In this paper, we derive a portfolio optimization model by minimizing upper and lower bounds of loss probability. These bounds are obtained under a nonparametric assumption of underlying return distribution by modifying the so-called generalization error bounds for the support vector machine, which has been developed in the field of statistical learning. Based on the bounds, two fractional programs are derived for constructing portfolios, where the numerator of the ratio in the objective includes the value-at-risk (VaR) or conditional value-at-risk (CVaR) while the denominator is any norm of portfolio vector. Depending on the parameter values in the model, the derived formulations can result in a nonconvex constrained optimization, and an algorithm for dealing with such a case is proposed. Some computational experiments are conducted on real stock market data, demonstrating that the CVaR-based fractional programming model outperforms the empirical probability minimization.     

鲁棒信用风险优化的线性锥优化模型

考虑了具有强健性的信用风险优化问题. 根据最差条件在值风险度量信用风险的方法,建立了信用风险优化问题的模型. 由于信用风险的损失分布存在不确定性,考虑了两类不确定性区间,即箱子型区间和椭球型区间. 把具有强健性的信用风险优化问题分别转化成线性规划问题和二阶锥规划问题. 最后,通过一个信用风险问题的例子来说明此模型的有效性.