具有现实约束的不确定均值—机会投资组合决策

本文将风险资产的收益看作梯形不确定变量,用机会约束表示风险控制(投资组合的实际收益低于预设的收益这一事件成立的机率不超过某一置信水平)。考虑最小交易量限制、基数约束、交易成本、借款约束和上下界约束,文章提出不确定均值——机会投资组合优化模型。该模型为全整数规划,精确算法难以在规定时间求出最优解,特别是当模型的规模较大时精确算法很难求解,文章设计一个遗传算法进行求解。最后通过实证研究分别对遗传算法进行可行性分析,对最大损失、置信度水平和投资组合中资产数量进行灵敏性分析,从而验证了模型和算法的有效性。     

带交易费用的离散多因素投资组合最优化(英文)

研究带有凹的交易费函数的离散多因素投资组合模型.与传统的投资组合模型不同的是,该模型中投资组合的决策变量是交易手数(整数),其最优化模型是一个非线性整数规划问题.为此本文提出了一个基于拉格朗日松弛和连续松弛的混合分枝定界算法,为测试算法的有效性,我们分别采用美国股票市场真实数据和随机产生的数据,数值结果表明该算法是有效的.     

带交易费用的离散多因素投资组合最优化

研究带有凹的交易费函数的离散多因素投资组合模型．与传统的投资组合模型不同的是,该模型中投资组合的决策变量是交易手数（整数）,其最优化模型是一个非线性整数规划问题．为此本文提出了一个基于拉格朗日松弛和连续松弛的混合分枝定界算法,为测试算法的有效性,我们分别采用美国股票市场真实数据和随机产生的数据,数值结果表明该算法是有效的．     

基于交易量限制的多阶段均值-CVaR投资组合模型

本文考虑资产收益率服从Laplace分布的多阶段均值-CVaR投资组合模型.结合摩擦市场对投资的一些限制因素,建立了带有最小交易量和交易费用限制的收益最大化多阶段投资组合模型,并利用绝对值函数的性质,将该模型转化为混合整数线性规划形式,用Lingo或Matlab求解.最后在证券市场上随机选取了四只股票进行了实证分析,验证了模型的可行性.     

离散单因素投资组合模型的对偶算法

本文研究金融优化中的离散单因素投资组合问题,该问题与传统投资组合模型的不同之处是决策变量为整数(交易手数),从而导致要求解一个二次整数规划问题．针对该模型的可分离性结构,我们提出了一种基于拉格朗日对偶和连续松弛的分枝定界算法。我们分别用美国股票市场的交易数据和随机产生的数据对算法进行了测试．数值结果表明该算法是有效的,可以求解多达150个风险证券的离散投资组合问题．     

基于两种模型的整车物流运输计划问题研究

运用2014年全国研究生数学建模竞赛E题的数据,针对乘用车整车物流运输计划问题的第三问展开研究.首先建立整数规划模型,得到要运输156辆Ⅰ型、102辆Ⅱ型和39辆Ⅲ型乘用车的1-1型和1-2型轿运车的最优数量分别为25和5.其次建立逐步转化模型,假设297辆乘用车全为Ⅱ型乘用车,使Ⅲ型乘用车数量满足要求,然后仅考虑Ⅰ型和Ⅱ型乘用车,使Ⅰ型和Ⅱ型乘用车数量满足要求,得到的结果与整数规划模型结果相一致.最后给出逐步转化模型的通用算法和程序.     

限制性卖空的均值-方差投资组合优化

本文提出了限制性卖空的均值-方差投资组合模型,通过变量替换将该模型转变为一般二次规划问题,从而运用不等式组的旋转算法进行求解.文章还以一个具体例子验证该算法的有效性,并证明在一定变化范围内,借入资产的资金与总资金的比例越大越有助于拓展投资机会空间.     

离散线性投资组合模型的分枝定界算法

本文提出了一类新的带整数交易手数和凹型交易费用的均值绝对偏差模型(MAD)和极大极小投资组合模型(Minmax),并给出了离散模型的分枝定界算法.我们分别用随机产生的数据和Nasdaq股票市场的真实数据进行了数值实验,数值分析表明在一定的收益水平下均值绝对偏差离散模型风险控制上优于极大极小投资组合离散模型,而计算效率上极大极小投资组合离散模型优于期望绝对偏差离散模型.     

线性约束下保险公司的最优投资策略

现实中,保险公司的投资行为会受到《保险法》及其自身风险管理条例的约束; 另外,保险公司必须提存一定数量的准备金以满足监管规定.鉴于此,本文将保险公司盈余首达最低准备金水平的时刻定义为``破产”时刻,以最小化``破产”概率为目标, 假设保险公司的盈余过程服从扩散模型,其可投资无风险资产与一种风险资产且投资受线性约束.我们通过求解相应的HJB方程得到了值函数与最优投资策略的解析式并给出了经济解释与数值算例.     

电动汽车充电站选址问题研究

研究了电动汽车作为通勤工具情况下的充电站选址问题.首先根据城市通勤道路长度、各条道路上的通勤人员拟使用电动汽车的数量,以及电动汽车的最大续航里程等信息,构建了包含两种边的赋权网络图,进一步将电动汽车充电站选址问题转化为赋权网络图的最大覆盖问题,并建立了以极大化满足用户需求为目标的充电站选址问题整数非线性规划模型,设计了求解模型的启发式算法.最后,通过一个具体算例对模型及算法进行了验证,结果显示,模型和算法是解决电动汽车充电站选址问题有效方法.     

Portfolio selection under downside risk measures and cardinality constraints based on DC programming and DCA

In this paper, we consider the case of downside risk measures with cardinality and bounding constraints in portfolio selection. These constraints limit the amount of capital to be invested in each asset as well as the number of assets composing the portfolio. While the standard Markowitz’s model is a convex quadratic program, this new model is a NP-hard mixed integer quadratic program. Realizing the computational intractability for this class of problems, especially large-scale problems, we first reformulate it as a DC program with the help of exact penalty techniques in Difference of Convex functions (DC) programming and then solve it by DC Algorithms (DCA). To check globality of computed solutions, a global method combining the local algorithm DCA with a Branch-and-Bound algorithm is investigated. Numerical simulations show that DCA is an efficient and promising approach for the considered problem.      

Solving knapsack problems with S-curve return functions

We consider the allocation of a limited budget to a set of activities or investments in order to maximize return from investment. In a number of practical contexts (e.g., advertising), the return from investment in an activity is effectively modeled using an S-curve, where increasing returns to scale exist at small investment levels, and decreasing returns to scale occur at high investment levels. We demonstrate that the resulting knapsack problem with S-curve return functions is NP-hard, provide a pseudo-polynomial time algorithm for the integer variable version of the problem, and develop efficient solution methods for special cases of the problem. We also discuss a fully-polynomial-time approximation algorithm for the integer variable version of the problem.     

Complexity of linear programming

The complexity of linear programming is discussed in the “integer” and “real number” models of computation. Even though the integer model is widely used in theoretical computer science, the real number model is more useful for estimating an algorithm's running time in actual computation.Although the ellipsoid algorithm is a polynomial-time algorithm in the integer model, we prove that it has unbounded complexity in the real number model. We conjecture that there exists no polynomial-time algorithm for the linear inequalities problem in the real number model. We also conjecture that linear inequalities are strictly harder than linear equalities in all “reasonable” models of computation.     

Portfolio selection with divisible and indivisible assets: Mathematical algorithm and economic analysis

We extend the classic mean-variance framework to a broad class of investment decisions under risk where investors select optimal portfolios of risky assets that include perfectly divisible as well as perfectly indivisible assets. We develop an algorithm for solving the associated mixed-integer nonlinear program and report on the results of a computational study. We then study the mean-variance structure of the investment frontier facing an individual investor in the presence of investment opportunities in both risky divisible and indivisible assets. Finally, we analyze the economic implications of the presence of investment opportunities in risky indivisible assets on the investor’s investment strategy and on his risk evaluation.     

Utility Indifference Pricing: A Time Consistent Approach

Abstract

This article considers the optimal portfolio selection problem in a dynamic multi-period stochastic framework with regime switching. The risk preferences are of exponential (CARA) type with an absolute coefficient of risk aversion that changes with the regime. The market model is incomplete and there are two risky assets: tradable and non-tradable. In this context, the optimal investment strategies are time inconsistent. Consequently, the subgame perfect equilibrium strategies are considered. The utility indifference ask price of a contingent claim written on the risky assets is computed through an indifference valuation algorithm. By running numerical experiments, we examine how this price varies in response to changes in model parameters.     

An integer programming dynamic farm-household model to evaluate the impact of agricultural policy reforms on farm investment behaviour

We develop a multi-objective farm-household dynamic integer programming model to simulate investment behaviour in different policy and price scenarios, with a particular focus on the decoupling of the Common Agricultural Policy (CAP). The model takes into account the characteristics of individual assets, including ageing and fixity through the explicit consideration of transaction costs. A case study application in the context of arable farming in Northern Italy is provided as an example. The results emphasise different patterns of reaction of different farm-household types over time, as an effect of the varying opportunity costs of resources and initial asset endowments. Overall, this application highlights the potentialities and limits of the methodology. In particular, the approach proved to be effective in providing a variety of results depending on the individual features of each farm-household, such as the differences between: (a) a ‘no reaction’ attitude; (b) an adaptation of farm activity and assets; and (c) a radical reaction pattern guided by high-income alternatives to farming. This highlights the potential of this tool as a generator of ideas and working hypotheses. We argue that, in view of the further developments of the CAP, the use of instruments able to account for multiple objectives, dynamics and investment choices will become even more relevant in the analysis of EU agricultural policy.     

Portfolio Optimization with Stochastic Volatilities and Constraints: An Application in High Dimension

In this paper we are interested in an investment problem with stochastic volatilities and portfolio constraints on amounts. We model the risky assets by jump diffusion processes and we consider an exponential utility function. The objective is to maximize the expected utility from the investor terminal wealth. The value function is known to be a viscosity solution of an integro-differential Hamilton-Jacobi-Bellman (HJB in short) equation which could not be solved when the risky assets number exceeds three. Thanks to an exponential transformation, we reduce the nonlinearity of the HJB equation to a semilinear equation. We prove the existence of a smooth solution to the latter equation and we state a verification theorem which relates this solution to the value function. We present an example that shows the importance of this reduction for numerical study of the optimal portfolio. We then compute the optimal strategy of investment by solving the associated optimization problem.     

考虑破产控制的多期模糊资产-负债组合优化模型

在实际的投资决策过程中,一些投资者需要同时管理资产和负债,因此本文研究考虑破产控制和偿债行为的资产-负债管理问题。假设风险资产的收益率和负债的增长率为模糊数,用资产-负债组合的可能性期望和下半绝对偏差度量其收益和风险,以最大化最终期望净财富和最小化最终累积风险为目标,建立了允许限制性卖空的多期模糊资产-负债组合优化模型。然后,设计了一个基于粒子群算法和模拟退火算法的混合智能算法对模型进行求解。最后,通过实例分析说明了所设计算法与传统粒子群算法相比具有更好的优化性能和稳定性。本文所提出策略可以为需要同时管理资产和负债的投资者提供决策支持。     

A Multi-Stage Stochastic Integer Programming Approach for Capacity Expansion under Uncertainty

This paper addresses a multi-period investment model for capacity expansion in an uncertain environment. Using a scenario tree approach to model the evolution of uncertain demand and cost parameters, and fixed-charge cost functions to model the economies of scale in expansion costs, we develop a multi-stage stochastic integer programming formulation for the problem. A reformulation of the problem is proposed using variable disaggregation to exploit the lot-sizing substructure of the problem. The reformulation significantly reduces the LP relaxation gap of this large scale integer program. A heuristic scheme is presented to perturb the LP relaxation solutions to produce good quality integer solutions. Finally, we outline a branch and bound algorithm that makes use of the reformulation strategy as a lower bounding scheme, and the heuristic as an upper bounding scheme, to solve the problem to global optimality. Our preliminary computational results indicate that the proposed strategy has significant advantages over straightforward use of commercial solvers.     

我国固定资产投资边际效率问题研究

将回归分析与边际效用理论有机结合,深入研究我国固定资产投资对GDP边际贡献问题.研究结果表明在整个正实数区间内高技术产业固定资产投资对GDP边际贡献高于全社会固定资产投资和城镇房地产开发固定资产投资对GDP边际贡献;在0～349578.8207亿元投资区间内,城镇房地产开发固定资产投资对GDP边际贡献高于全社会固定资产投资边际贡献平均水平;当投资额度大于349578.8207亿元时,城镇房地产开发固定资产投资对GDP边际贡献小于全社会固定资产投资边际贡献平均水平.研究结果印证了科技是第一生产力的论断,同时也与我国当前控制房地产开发投入、鼓励发展高新技术产业等宏观经济政策相吻合.