共查询到19条相似文献,搜索用时 109 毫秒
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针对期望收益率与风险损失率为区间值模糊数的特征,就证券组合投资问题建立了一种区间值模糊线性规划模型,运用一种对区间值模糊数排序的新算法,将模型转化为经典的线性规划问题进行求解,最后通过一个算例说明其有效性和可靠性,为证券组合投资优化问题的解决提供了一种新的方法,对证券组合的理性投资具有重要的指导意义. 相似文献
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基于区间证券组合的系统风险与非系统风险问题,建立一种新的含β约束的区间证券投资组合的多目标优化模型,使得证券组合投资更具柔性,最后,结合实例分析了该模型的现实应用价值. 相似文献
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基于区间数的证券组合投资模型研究 总被引:5,自引:1,他引:4
提出了证券组合投资的区间数线性规划模型.通过引入区间数线性规划问题中的目标函数优化水平α和约束水平β将目标函数和约束条件均为区间数的线性规划问题转化为确定型的线性规划问题.投资者可以根据自己的风险喜好程度和客观情况,对这两个参数做出不同的估计,从而得到相应情况下的有效投资方案,使证券组合投资决策更具柔性.最后通过实例分析说明了该模型的可行性. 相似文献
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均值-叉熵证券投资组合优化模型 总被引:4,自引:1,他引:3
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,进而提出用叉熵作为风险的度量方法,建立了均值-叉熵的投资组合优化模型.该模型计算简便,更易被一般投资人所使用. 相似文献
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熵—证券投资组合风险的一种新的度量方法 总被引:16,自引:0,他引:16
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 相似文献
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Christine Gregory Ken Darby-DowmanGautam Mitra 《European Journal of Operational Research》2011,212(2):417-428
Robust optimization is a tractable alternative to stochastic programming particularly suited for problems in which parameter values are unknown, variable and their distributions are uncertain. We evaluate the cost of robustness for the robust counterpart to the maximum return portfolio optimization problem. The uncertainty of asset returns is modelled by polyhedral uncertainty sets as opposed to the earlier proposed ellipsoidal sets. We derive the robust model from a min-regret perspective and examine the properties of robust models with respect to portfolio composition. We investigate the effect of different definitions of the bounds on the uncertainty sets and show that robust models yield well diversified portfolios, in terms of the number of assets and asset weights. 相似文献
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In typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not large enough, the realized returns of resulting portfolios will be outside of the uncertainty set when an extreme event such as market crash or a large shock of asset returns occurs. The goal of this paper is to propose robust portfolio selection models under so-called “ marginal+joint” ellipsoidal uncertainty set and to test the performance of the proposed models. A robust portfolio selection model under a “marginal + joint” ellipsoidal uncertainty set is proposed at first. The model has the advantages of models under the separable uncertainty set and the joint ellipsoidal uncertainty set, and relaxes the requirements on the uncertainty set. Then, one more robust portfolio selection model with option protection is presented by combining options into the proposed robust portfolio selection model. Convex programming approximations with second-order cone and linear matrix inequalities constraints to both models are derived. The proposed robust portfolio selection model with options can hedge risks and generates robust portfolios with well wealth growth rate when an extreme event occurs. Tests on real data of the Chinese stock market and simulated options confirm the property of both the models. Test results show that (1) under the “ marginal+joint” uncertainty set, the wealth growth rate and diversification of robust portfolios generated from the first proposed robust portfolio model (without options) are better and greater than those generated from Goldfarb and Iyengar’s model, and (2) the robust portfolio selection model with options outperforms the robust portfolio selection model without options when some extreme event occurs. 相似文献
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In this paper, we present a duality theory for fractional programming problems in the face of data uncertainty via robust optimization. By employing conjugate analysis, we establish robust strong duality for an uncertain fractional programming problem and its uncertain Wolfe dual programming problem by showing strong duality between the deterministic counterparts: robust counterpart of the primal model and the optimistic counterpart of its dual problem. We show that our results encompass as special cases some programming problems considered in the recent literature. Moreover, we also show that robust strong duality always holds for linear fractional programming problems under scenario data uncertainty or constraint-wise interval uncertainty, and that the optimistic counterpart of the dual is tractable computationally. 相似文献
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A robust optimization approach to closed-loop supply chain network design under uncertainty 总被引:1,自引:0,他引:1
The concern about significant changes in the business environment (such as customer demands and transportation costs) has spurred an interest in designing scalable and robust supply chains. This paper proposes a robust optimization model for handling the inherent uncertainty of input data in a closed-loop supply chain network design problem. First, a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then, the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally, to assess the robustness of the solutions obtained by the novel robust optimization model, they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems. 相似文献
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This paper extends the Log-robust portfolio management approach to the case with short sales, i.e., the case where the manager can sell shares he does not yet own. We model the continuously compounded rates of return, which have been established in the literature as the true drivers of uncertainty, as uncertain parameters belonging to polyhedral uncertainty sets, and maximize the worst-case portfolio wealth over that set in a one-period setting. The degree of the manager’s aversion to ambiguity is incorporated through a single, intuitive parameter, which determines the size of the uncertainty set. The presence of short-selling requires the development of problem-specific techniques, because the optimization problem is not convex. In the case where assets are independent, we show that the robust optimization problem can be solved exactly as a series of linear programming problems; as a result, the approach remains tractable for large numbers of assets. We also provide insights into the structure of the optimal solution. In the case of correlated assets, we develop and test a heuristic where correlation is maintained only between assets invested in. In computational experiments, the proposed approach exhibits superior performance to that of the traditional robust approach. 相似文献
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Solving Planning and Design Problems in the Process Industry Using Mixed Integer and Global Optimization 总被引:1,自引:0,他引:1
Josef Kallrath 《Annals of Operations Research》2005,140(1):339-373
This contribution gives an overview on the state-of-the-art and recent advances in mixed integer optimization to solve planning
and design problems in the process industry. In some case studies specific aspects are stressed and the typical difficulties
of real world problems are addressed.
Mixed integer linear optimization is widely used to solve supply chain planning problems. Some of the complicating features
such as origin tracing and shelf life constraints are discussed in more detail. If properly done the planning models can also
be used to do product and customer portfolio analysis.
We also stress the importance of multi-criteria optimization and correct modeling for optimization under uncertainty. Stochastic
programming for continuous LP problems is now part of most optimization packages, and there is encouraging progress in the
field of stochastic MILP and robust MILP.
Process and network design problems often lead to nonconvex mixed integer nonlinear programming models. If the time to compute
the solution is not bounded, there are already a commercial solvers available which can compute the global optima of such
problems within hours. If time is more restricted, then tailored solution techniques are required. 相似文献
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This paper addresses a new uncertainty set—interval random uncertainty set for robust optimization. The form of interval random uncertainty set makes it suitable for capturing the downside and upside deviations of real-world data. These deviation measures capture distributional asymmetry and lead to better optimization results. We also apply our interval random chance-constrained programming to robust mean-variance portfolio selection under interval random uncertainty sets in the elements of mean vector and covariance matrix. Numerical experiments with real market data indicate that our approach results in better portfolio performance. 相似文献
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范臻 《应用数学与计算数学学报》2006,20(1):56-62
本文对于信用资产组合的优化问题给出了一个稳健的模型,所建模型涉及了条件在险值(CVaR)风险度量以及具有补偿限制的随机线性规划框架,其思想是在CVaR与信用资产组合的重构费用之间进行权衡,并降低解对于随机参数的实现的敏感性.为求解相应的非线性规划,本文将基本模型转化为一系列的线性规划的求解问题. 相似文献
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In this paper, we examine duality for fractional programming problems in the face of data uncertainty within the framework
of robust optimization. We establish strong duality between the robust counterpart of an uncertain convex–concave fractional
program and the optimistic counterpart of its conventional Wolfe dual program with uncertain parameters. For linear fractional
programming problems with constraint-wise interval uncertainty, we show that the dual of the robust counterpart is the optimistic
counterpart in the sense that they are equivalent. Our results show that a worst-case solution of an uncertain fractional
program (i.e., a solution of its robust counterpart) can be obtained by solving a single deterministic dual program. In the
case of a linear fractional programming problem with interval uncertainty, such solutions can be found by solving a simple
linear program. 相似文献