证券投资组合理论的一种新模型及其应用

马科维茨(Markowitz)以证券收益率的方差作为投资风险的测度建立了组合证券投资模型，本基于熵的概念，在研究马科维茨(Markowitz)证券投资组合模型的基础上，分析了该模型用方差度量风险的不足，进而提出一种新的证券投资组合优化模型，并以实例作了说明。     

一种证券组合选择模型

本文在Ｍａｒｋｏｗｉｔｚ组合证券投资决策模型基础上提出了一种可产生更优组合证券投资策略的证券组合选择模型，研究了它的解的结构、它的有效边界的构成。     

最小风险证券组合的结构分析和迭代算法

本文分析了最小风险组合证券投资的结构特征，并提出了一种组合证券风险最小化的迭代算法，证明了其收敛性．该算法操作简单，且易于处理不允许卖空情况下的证券组合问题．     

具指数赋权指标的证券投资多目标线性规划模型

本文提出证券投资决策的指数赋权指标体系．在该指标体系中,建立风险证券组合投资决策和存在无风险证券或无风险贷款时证券组合投资决策的多目标线性规划模型．研究了有效风险证券组合集和有效证券组合集的结构和相互关系,市场证券组合以及证券均衡市场价格和投资风险分析．     

单位收益率风险最小的组合证券投资决策模型

章首先分析了组合证券投资的收益率和风险,根据组合证券投资的亏本概率上界最小的原则,建立了单位收益率风险最小的组合证券投资决策模型,并证明了该模型的有效性。     

熵—证券投资组合风险的一种新的度量方法

本文在研究马科维茨 ( Markowitz)证券投资组合模型的基础上 ,分析了该模型用方差度量风险的缺陷 ,进而提出用熵作为风险的度量方法 ,改进马科维茨 ( Markowitz)证券投资组合模型 ,并建立新的证券投资组合优化模型     

具有交易成本的证券投资组合选择：一种求解方法

本研究一类带交易成本证券投资组合选择的求解，在风险不超过某个阈值的假设下，我们给出一种求解方法，最后本通过实例计算表明该方法是有效的。     

投资比例非负约束的风险证券组合有效集及动态分析

本文提出了风险证券有效组合的决策模型 ,给出了投资比例非负约束的风险证券有效组合的解析表示 ,研究了证券个数变动对证券组合有效集的影响 .分析了它的漂移方向和漂移范围 ,给出了最小风险有效证券组合和最大收益有效证券组合的漂移距离及风险与收益的增加或减少程度     

目标规划法在证券组合投资中的应用

证券投资是目前我国经济中的一大热点。本以Markowitz证券组合投资理论为基础，运用目标规划的方法建立一种新的证券组合投资决策模型。在本模型中综合考虑了证券组合的收益，风险，交易费用等因素，对投资选择有效证券组合有一定的实用价值。     

最小风险组合证券非负投资比例系数的确定

研究非负投资比例系数约束条件下,实现风险最小化的组合证券投资问题.应用罚函数法,对最小风险组合证券的非负投资比例系数进行研究.实例表明:这一方法是可行的、有效的.     

The efficient frontier for bounded assets

This paper develops a closed form solution of the mean-variance portfolio selection problem for uncorrelated and bounded assets when an additional technical assumption is satisfied. Although the assumption of uncorrelated assets is unduly restrictive, the explicit determination of the efficient asset holdings in the presence of bound constraints gives insight into the nature of the efficient frontier. The mean-variance portfolio selection problem considered here deals with the budget constraint and lower bounds or the budget constraint and upper bounds. For the mean-variance portfolio selection problem dealing with lower bounds the closed form solution is derived for two cases: a universe of only risky assets and a universe of risky assets plus an additional asset which is risk free. For the mean-variance portfolio selection problem dealing with upper bounds, the results presented are for a universe consisting only of risky assets. In each case, the order in which the assets are driven to their bounds depends on the ordering of their expected returns.     

On the number of securities which constitute an efficient portfolio

The purpose of this paper is to discuss the relationship between the number of securities which constitute an efficient portfolio as defined by the standard mean-variance portfolio selection model and the number of periods used to compute the efficient portfolio. It is shown that the number of data gives the upper bound of the number of securities which constitute an efficient portfolio, when each efficient portfolio is unique for a given expected return. Empirical tests based on actual return data show that this upper bound is very tight when the number of data is small. However, when more data are used, the upper bound becomes looser. This result is incompatible with the market efficiency. These empirical tests also indicate that a very tight upper bound often causes a degenerate case ensuring zero-variance portfolios.     

An analytic derivation of admissible efficient frontier with borrowing

The admissible efficient portfolio selection problem for risky assets has been discussed by Zhang and Nie. In this paper, the admissible efficient portfolio model is proposed under the assumption that there exists the borrowing (money or a risk free asset) case. The admissible efficient frontiers are developed by the spreads of expected return and risk from admissible errors. The analytic forms of the admissible efficient frontiers when short sales are not allowed on all risky assets are derived from two cases: the borrowing with an upper bound constraint, or without an upper bound constraint. The influence on the admissible efficient frontier is explained under the different interest rates of the borrowing. The differences between the results with the borrowing and the results without the borrowing is revealed by a real numerical example.     

协方差矩阵退化情形均值-CVaR模型的有效边界

本文利用CVaR方法代替方差或CVaR来度量风险,建立了均值-CVaR模型,首先利用等CVaR线的方法研究了包含无风险资产的均值-CVaR模型的有效边界,然后在无套利假设下研究了当风险资产的协方差矩阵是奇异时的均值-CVaR模型,并得到了正态情形下模型的有效边界及其解析表达式.     

证券组合M-V有效前沿旋移分析的进一步研究

在市场上存在无风险资产且允许卖空的条件下,研究了新增加k种证券后对原有效前沿的影响.引入了有效证券和无效证券,给出了M-V证券组合有效前沿旋移的方向.研究结果表明新增加证券后有效前沿的斜率变大.     

具有交易费用和马氏调制的均值-方差组合选择

研究了均值-方差准则下,最优投资组合选择问题.投资者为了增加财富它可以在金融市场上投资.金融市场由一个无风险资产和n个带跳的风险资产组成,并假设金融市场具有马氏调制,买卖风险资产时,考虑交易费用.目标是,在终值财富的均值等于d的限制下,使终值财富的方差最小,即均值-方差组合选择问题.应用随机控制的理论解决该问题,获得了最优的投资策略和有效边界.     

A mixed R&D projects and securities portfolio selection model

The business environment is full of uncertainty. Allocating the wealth among various asset classes may lower the risk of overall portfolio and increase the potential for more benefit over the long term. In this paper, we propose a mixed single-stage R&D projects and multi-stage securities portfolio selection model. Specifically, we present a bi-objective mixed-integer stochastic programming model. Moreover, we use semi-absolute deviation risk functions to measure the risk of mixed asset portfolio. Based on the idea of moments approximation method via linear programming, we propose a scenario generation approach for the mixed single-stage R&D projects and multi-stage securities portfolio selection problem. The bi-objective mixed-integer stochastic programming problem can be solved by transforming it into a single objective mixed-integer stochastic programming problem. A numerical example is given to illustrate the behavior of the proposed mixed single stage R&D projects and multi-stage securities portfolio selection model.     

证券数目变化时受到扰动的M-V有效前沿分析

基于M-V证券组合模型,在证券市场上不存在无风险资产且允许卖空条件下,探讨了证券数增加k种后原n种证券协方差矩阵发生改变情形下M-V证券组合有效前沿的漂移问题。通过引入扰动因子和扰动矩阵,给出了M-V证券组合有效前沿的漂移方向及其开口大小的变化情况．研究结果表明证券数增加了k种后有效前沿向左漂移以及它的开口变大,原证券组合的有效前沿完全落在新的证券组合可行集内．     

证券数增加情形下证券组合有效边缘特征灵敏度分析

本文较为详细地讨论了当证券市场不存在无风险收益证券且允许卖空时证券数的增加对 M-V证券组合有效边缘及其特征的影响 ,给出了有效边缘、渐近线斜率、全局最小方差证券组合及其协方差、最小方差证券组合的投资权数等的变化模式