基于存量与增量全部组合收益最大的贷款优化决策模型

在既定组合收益范围内,以VaR风险控制为约束条件,以0-1规划为工具,建立了存量与增量全部贷款组合累计收益最大的决策优化模型.模型的主要特点一是综合反映贷款存量与增量组合累计收益最大对贷款决策的直接影响,合理地考虑了贷款存量组合与贷款增量组合的关系,真正地控制了银行全部贷款的组合风险和收益,改变了现有研究仅仅优化增量贷款组合的现状,开拓了金融资产组合优化理论的新思路.二是以VaR风险控制作为约束条件,用组合的VaR收益率最大损失来控制贷款收益率风险限额,直接反映了商业银行的风险承受能力.三是在贷款组合过程中,使用上下界限制,使得商业银行既能较好地进行风险控制,又可以充分利用贷款头寸.     

基于风险分析的商业银行项目贷款评估改进研究

项目贷款评估是商业银行风险防范的重要环节,对商业银行资产质量起着至关重要的作用.以项目评估理论为基础,对商业银行项目贷款评估存在的问题进行剖析,对影响项目贷款还款能力的关键风险因素进行分类和细化,以风险为导向重构项目贷款评估指标体系,建立评价指标的评语集和隶属度,设置了项目贷款评估标准,基于模糊综合评价对贷款项目进行定量评价和等级划分,最后选取实际项目进行实证研究,为商业银行项目贷款的科学决策和合理投放提供依据.     

考虑借款企业决策行为的供应链CVaR利率决策模型

贷款利率是银行和其他金融机构用于权衡风险和收益的重要指标之一.考虑借款企业的决策行为,采用CVaR的风险度量准则作为决策标准,建立了基于条件风险价值最小的银行贷款利率决策模型,得到了权衡风险和收益条件下的最优利率.通过模型求解和数值分析发现:决策者的风险容忍水平直接对利率决策产生影响,CVaR风险度量准则可以帮助银行权衡收益和风险:借款企业利润率和自有资金是银行规避风险时需要着重考虑的关键因素.     

国有商业银行投资收益影响因素的研究

商业银行是关系国家金融体系安全稳定的关键行业,而国有商业银行在商业银行的发展中占据了极其重要的位置.国有商业银行投资以其较低的风险和稳定的收益获得了投资者的青睐,因此其投资收益的影响因素成为投资者特别关注的对象.对中国银行、中国工商银行、中国建设银行、中国农业银行等四大商业银行2006年以来的季度数据利用excel进行了统计分析,然后借助Eviews6.0软件,运用普通最小二乘回归方法建立了多元线性回归模型,结果发现投资收益与考虑现金红利的股票回报率、每股收益、资产报酬率、股本报酬率、净利润增长率、股东权益比率有显著的相关关系,为投资者投资国有商业银行提供了参考.     

基于加权风险收益的中小科技企业贷款组合优化决策模型

建立了基于加权风险收益的中小科技企业贷款组合优化决策模型.该模型依据贷款组合单位风险权重收益最大的基本思想、结合科技企业特点、国家对科技企业扶持的政策等,采用CreditRisk+模型测量风险,有效地控制了对科技企业贷款的风险,为科技企业贷款决策提供了选择方法.     

基于网站和卖家的C2C消费者购买意愿模型:感知收益与风险的转移

基于在线购物意愿的研究已经证实了态度、主观规范和感知行为控制对购买意愿有显著的影响,但考虑媒介网站和在线卖家关系的研究并不多见。本文考虑C2C电子商务模式的特殊机制,发现消费者对网站的感知收益和风险会通过对网站的态度间接影响到对卖家收益和风险的判断上,表明收益和风险的感知能够在相近对象之间转移。同时证实,自我效能直接影响对卖家的感知风险,感知社会存在直接影响感知收益和风险,由此暗示传统交易中的消费者行为规律在网购中仍在发挥作用。     

基于模糊厌恶视角的最优固定利率与可变利率贷款组合决策行为研究

分析具有对边际融资成本不确定性存在模糊信念的竞争性商业银行的最优固定利率与可变利率贷款组合决策行为.采用银行的期望收益的正斜率和凹函数表示其模糊厌恶偏好.导出银行可变利率贷款占优于固定利率贷款的条件.表明,如果银行可以同时做出固定利率贷款和可变利率贷款组合决策,那么两类贷款的总额既不取决于银行的模糊厌恶偏好,也不取决于融资成本的不确定性风险.结论指出,银行的模糊厌恶成度的增加将引起其对企业发放更少固定利率贷款和更多可变利率贷款.因此,银行的模糊厌恶偏好在对企业的固定利率和可变利率贷款组合的最优决策行为中起着至关重要的作用.     

科技成果转让与收益分配率“β”科学计算的拓展

首先对科技成果转让中收益分配率确定的基本原则进行了分析 ,在此基础上 ,综合考虑合作伙伴的投资及所承担的风险 ,然后运用层次分析法和模糊综合评价法对科技成果转让中的风险进行了客观评价 ,提出了一种确定收益分配率科学计算方法 ,并进行实证分析说明其有效性 ,为科技成果转让的风险决策提供了具体、可行的依据 .     

运用聚类分析法对我国企业信贷风险的评估与预测

本文通过对企业财务指标的分析与选取,运用聚类分析法对我国企业的信用风险贷款质量进行了评估与预测。根据企业信用风险程度,将商业银行对企业的贷款质量分为正常、关注、次级、可疑、损失五类形态。以此对上市公司的运行状况作出评价,为商业银行是否对其贷款提供依据。     

一种市场风险条件的贷款组合优化决策模型

针对不确定投资情况下风险与收益的选择方法，提出一种贷款组合的优化决策模型，以解决不同风险，收益下的贷款组合决策问题，并给出实例分析以表明该模型的科学性。     

Credit risk optimization using factor models

We study portfolio credit risk management using factor models, with a focus on optimal portfolio selection based on the tradeoff of expected return and credit risk. We begin with a discussion of factor models and their known analytic properties, paying particular attention to the asymptotic limit of a large, finely grained portfolio. We recall prior results on the convergence of risk measures in this “large portfolio approximation” which are important for credit risk optimization. We then show how the results on the large portfolio approximation can be used to reduce significantly the computational effort required for credit risk optimization. For example, when determining the fraction of capital to be assigned to particular ratings classes, it is sufficient to solve the optimization problem for the large portfolio approximation, rather than for the actual portfolio. This dramatically reduces the dimensionality of the problem, and the amount of computation required for its solution. Numerical results illustrating the application of this principle are also presented. JEL Classification G11     

基于模糊子集的贷款组合优化方法

遵照国际银行业大多数银行的做法,信用风险评估包括对债务人和债项两个方面.以模糊集理论为基础,通过试算与比较,构造隶属函数,对各指标进行无量纲化处理,建立距离判别函数,评估债务人信用风险.根据债项特征,考察风险四因素:违约概率,特定违约损失,违约敞口,期限,建立0-1整数规划模型,对债项进行风险评估,确定最佳贷款组合,以解决组合贷款的优化决策问题.     

Credit contagion in a network of firms with spatial interaction

This contribution studies the effects of credit contagion on the credit risk of a portfolio of bank loans. To this aim we introduce a model that takes into account the counterparty risk in a network of interdependent firms that describes the presence of business relations among different firms. The location of the firms is simulated with probabilities computed using an entropy spatial interaction model. By means of a wide simulation analysis we investigate the behavior of the model proposed and study the effects of default contagion on the loss distribution of a portfolio of bank loans.     

On the parameterization of the CreditRisk model for estimating credit portfolio risk

The CreditRisk⁺ model is one of the industry standards for estimating the credit default risk for a portfolio of credit loans. The natural parameterization of this model requires the default probability to be apportioned using a number of (non-negative) factor loadings. However, in practice only default correlations are often available but not the factor loadings. In this paper we investigate how to deduce the factor loadings from a given set of default correlations. This is a novel approach and it requires the non-negative factorization of a positive semi-definite matrix which is by no means trivial. We also present a numerical optimization algorithm to achieve this.     

Credit risk optimization with Conditional Value-at-Risk criterion

This paper examines a new approach for credit risk optimization. The model is based on the Conditional Value-at-Risk (CVaR) risk measure, the expected loss exceeding Value-at-Risk. CVaR is also known as Mean Excess, Mean Shortfall, or Tail VaR. This model can simultaneously adjust all positions in a portfolio of financial instruments in order to minimize CVaR subject to trading and return constraints. The credit risk distribution is generated by Monte Carlo simulations and the optimization problem is solved effectively by linear programming. The algorithm is very efficient; it can handle hundreds of instruments and thousands of scenarios in reasonable computer time. The approach is demonstrated with a portfolio of emerging market bonds. Received: November 1, 1999 / Accepted: October 1, 2000?Published online December 15, 2000     

Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature

Since the pioneering work of Harry Markowitz, mean–variance portfolio selection model has been widely used in both theoretical and empirical studies, which maximizes the investment return under certain risk level or minimizes the investment risk under certain return level. In this paper, we review several variations or generalizations that substantially improve the performance of Markowitz’s mean–variance model, including dynamic portfolio optimization, portfolio optimization with practical factors, robust portfolio optimization and fuzzy portfolio optimization. The review provides a useful reference to handle portfolio selection problems for both researchers and practitioners. Some summaries about the current studies and future research directions are presented at the end of this paper.     

Portfolio selection with a minimax measure in safety constraint

In this paper, we attempt to design a portfolio optimization model for investors who desire to minimize the variation around the mean return and at the same time wish to achieve better return than the worst possible return realization at every time point in a single period portfolio investment. The portfolio is to be selected from the risky assets in the equity market. Since the minimax portfolio optimization model provides us with the portfolio that maximizes (minimizes) the worst return (worst loss) realization in the investment horizon period, in order to safeguard the interest of investors, the optimal value of the minimax optimization model is used to design a constraint in the mean-absolute semideviation model. This constraint can be viewed as a safety strategy adopted by an investor. Thus, our proposed bi-objective linear programming model involves mean return as a reward and mean-absolute semideviation as a risk in the objective function and minimax as a safety constraint, which enables a trade off between return and risk with a fixed safety value. The efficient frontier of the model is generated using the augmented -constraint method on the GAMS software. We simultaneously solve the ratio optimization problem which maximizes the ratio of mean return over mean-absolute semideviation with same minimax value in the safety constraint. Subsequently, we choose two portfolios on the above generated efficient frontier such that the risk from one of them is less and the mean return from other portfolio is more than the respective quantities of the optimal portfolio from the ratio optimization model. Extensive computational results and in-sample and out-of-sample analysis are provided to compare the financial performance of the optimal portfolios selected by our proposed model with that of the optimal portfolios from the existing minimax and mean-absolute semideviation portfolio optimization models on real data from S&P CNX Nifty index.     

二阶随机占优约束下考虑偏度的投资组合模型

在DentchevaRuszczynski(2006)模型的基础上,考虑偏度对构建投资组合的影响,建立了二阶随机占优约束下最大化组合收益率偏度的投资组合优化模型,并应用分段线性近似方法将模型转化为一个非线性混合整数规划问题.利用中国股票市场的历史数据对所建模型进行了实证分析,结果表明,所建新模型比均值-方差-偏度模型和市场指数具有更稳健的表现.     

基于双因子CIR强度式定价的信用债券投资组合优化

信用风险和利率风险是相互关联影响的。资产组合优化不能将这两种风险单独考虑或简单的相加,应该进行整体的风险控制,不然会造成投资风险的低估。本文的主要工作:一是在强度式定价模型的框架下,分别利用CIR随机利率模型刻画利率风险因素“无风险利率”和信用风险因素“违约强度”的随机动态变化,衡量在两类风险共同影响下信用债券的市场价值,从而构建CRRA型投资效用函数。以CRRA型投资效用函数最大化作为目标函数,同时控制利率和信用两类风险。弥补了现有研究中仅单独考虑信用风险或利率风险、无法对两种风险进行整体控制的弊端。二是将无风险利率作为影响违约强度的一个因子,利用“无风险利率因子”和“纯信用因子”的双因子CIR模型拟合违约强度,考虑了市场利率变化对于债券违约强度的影响,反映两种风险的相关性。使得投资组合模型中既同时考虑了信用风险和利率风险、又考虑了两种风险的交互影响。避免在优化资产组合时忽略两种风险间相关性、可能造成风险低估的问题。     

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。