多阶段均值-半绝对偏差模糊投资组合优化研究

考虑交易成本和交易量限制,提出投资组合的收益率的隶属函数为梯形的多阶段均值-半绝对偏差可能性投资组合模型,并用自创算法——离散近似迭代法求解.其基本思路为:将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;运用极大代数求出起点至终点的最长路程,即获得模型的一个可行解;以该可行解为基础,继续迭代直到前后两个可行解非常接近.文章还证明了该方法的线性收敛.最后,文章以一个具体的算例验证了该算法的有效性.     

基于离散近似迭代法的多阶段M-V投资组合优化

提出了离散近似迭代方法,并用该方法求解具有交易成本和交易量限制的多阶段均值-方差(M-V)投资组合模型.离散近似迭代方法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;其次,运用嘉量原理求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.还证明了该方法的收敛性和复杂性.     

多阶段M-SV投资组合优化的离散近似迭代法研究

文章提出了离散近似迭代法,用该方法求解具有交易成本和交易量限制的多阶段均值一半方差(M-sV)投资组合模型.离散近似迭代方法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将上述模型转化多阶段赋权有向图;其次,运用嘉量原理求出起点至终点的最长路程,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.文章还证明了该方法的收敛性和复杂性.     

具有实际约束的多阶段M-V投资组合时间一致性策略研究

从动态规划的角度分析,方差算子的不可分离性导致标准的多阶段均值-方差模型的最优投资策略不满足时间一致性。文章采用条件期望映射的方法,构建了一个具有交易成本、借贷约束和阈值约束的多阶段M-V投资组合模型。由于考虑了交易成本,该模型是一个具有路径依赖性的动态优化问题。为了获得其时间一致性投资策略,文章将该问题近似地转化为连续性动态规划模型,证明最优解的近似度,并运用离散迭代算法求解。最后,使用上海证券交易所的部分历史数据验证了模型和算法的有效性。     

连续型凸动态规划的离散近似迭代法研究

为解决连续型凸动态规划的“维数灾”问题,提出了一种新的算法—离散近似迭代法.该算法的基本思路为:首先,将连续型状态变量离散化,根据网络图的构造方法将动态规划问题转化为多阶段有向赋权图;其次,运用极大代数求出起点至终点的最短路,即获得模型的一个可行解;最后,以该可行解为基础,继续迭代直到前后两个可行解非常接近.文章还证明了该算法的收敛性和线性收敛,并以一个具体例子验证了算法的有效性.     

基于直觉模糊规划的多目标投资组合选择模型

将直觉模糊集合的概念引入投资组合模型中,并将多目标投资组合模型中的收益、方差和偏度三个目标模糊化,用隶属函数与非隶属函数作为新的目标函数.针对该模糊多目标投资组合模型,提出了一个动态遗传算法,算例给出了该模型的一个实例的最优解.     

多阶段可能性均值-熵投资组合优化

考虑借款限制、交易量限制、交易成本和风险控制,本文提出了多阶段均值-熵投资组合模型。在该模型中,收益水平和风险分别用可能性均值和熵度量。熵值越小,投资组合包含的不确定性越低,投资组合的安全性越高。此外,熵不依赖于证券收益的对称分布。运用可能性理论,将该模型转化为显示的非线性动态优化问题。由于投资过程存在交易成本,上述模型为具有路径依赖性的动态优化问题。文章提出了前向动态规化方法求解。最后,通过实证研究比较了不同熵的取值投资组合最优投资比例和最终财富的变化,并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.     

具有交易成本的多阶段M-AAD模糊投资组合优化

考虑交易成本、交易量的阀值约束和熵约束,提出均值-平均绝对偏差(M-AAD)多阶段的模糊投资组合模型。模型中的收益水平由模糊收益的均值确定,其风险水平由模糊收益的绝对偏差确定,熵度量投资组合的多样化程度。由于存在交易成本,该模型是一个具有路径依赖性的动态优化问题。提出离散近似迭代法求解。最后,以具体的算例比较不同熵约束下最优投资组合策略,并验证模型的算法和有效性。     

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．     

Multiperiod Mean-Variance Portfolio Optimization via Market Cloning

The problem of finding the mean variance optimal portfolio in a multiperiod model can not be solved directly by means of dynamic programming. In order to find a solution we therefore first introduce independent market clones having the same distributional properties as the original market, and we replace the portfolio mean and variance by their empirical counterparts. We then use dynamic programming to derive portfolios maximizing a weighted sum of the empirical mean and variance. By letting the number of market clones converge to infinity we are able to solve the original mean variance problem.     

Calculating risk neutral probabilities and optimal portfolio policies in a dynamic investment model with downside risk control

This paper presents a method for solving multiperiod investment models with downside risk control characterized by the portfolio’s worst outcome. The stochastic programming problem is decomposed into two subproblems: a nonlinear optimization model identifying the optimal terminal wealth distribution and a stochastic linear programming model replicating the identified optimal portfolio wealth. The replicating portfolio coincides with the optimal solution to the investor’s problem if the market is frictionless. The multiperiod stochastic linear programming model tests for the absence of arbitrage opportunities and its dual feasible solutions generate all risk neutral probability measures. When there are constraints such as liquidity or position requirements, the method yields approximate portfolio policies by minimizing the initial cost of the replication portfolio. A numerical example illustrates the difference between the replicating result and the optimal unconstrained portfolio.     

Optimal portfolio selection for general provisioning and terminal wealth problems

In Dhaene et al. (2005), multiperiod portfolio selection problems are discussed, using an analytical approach to find optimal constant mix investment strategies in a provisioning or a savings context. In this paper we extend some of these results, investigating some specific, real-life situations. The problems that we consider in the first section of this paper are general in the sense that they allow for liabilities that can be both positive or negative, as opposed to Dhaene et al. (2005), where all liabilities have to be of the same sign. Secondly, we generalize portfolio selection problems to the case where a minimal return requirement is imposed. We derive an intuitive formula that can be used in provisioning and terminal wealth problems as a constraint on the admissible investment portfolios, in order to guarantee a minimal annualized return. We apply our results to optimal portfolio selection.     

不确定条件下具有容差项的Markowitz证券组合投资模型的最优化解法

在分析证券市场中证券组合投资不确定性质的基础上,通过对Markowitz模型中证券期望收益与方差引入容差项来度量证券市场的不确定性,建立了不确定条件下具有容差项的Markowitz证券组合投资模型;分类讨论了容差的上界与下界所对应的两类有效组合前沿,得到了不确定条件下的证券组合投资模型的最优化解法及相关定理;最后给出了一个具体的数值实例．     

Optimal asset allocation for a general portfolio of life insurance policies

Asset liability matching remains an important topic in life insurance research. The objective of this paper is to find an optimal asset allocation for a general portfolio of life insurance policies. Using a multi-asset model to investigate the optimal asset allocation of life insurance reserves, this study obtains formulae for the first two moments of the accumulated asset value. These formulae enable the analysis of portfolio problems and a first approximation of optimal investment strategies. This research provides a new perspective for solving both single-period and multiperiod asset allocation problems in application to life insurance policies. The authors obtain an efficient frontier in the case of single-period method; for the multiperiod method, the optimal asset allocation strategies can differ considerably for different portfolio structures.     

Portfolio optimization via stochastic programming: Methods of output analysis

Solutions of portfolio optimization problems are often influenced by errors or misspecifications due to approximation, estimation and incomplete information. Selected methods for analysis of results obtained by solving stochastic programs are presented and their scope illustrated on generic examples – the Markowitz model, a multiperiod bond portfolio management problem and a general strategic investment problem. The approaches are based on asymptotic and robust statistics, on the moment problem and on results of parametric optimization.     

Optimal selection of a portfolio of options under Value-at-Risk constraints: a scenario approach

This paper introduces a multiperiod model for the optimal selection of a financial portfolio of options linked to a single index. The objective of the model is to maximize the expected return of the portfolio under constraints limiting its Value-at-Risk. We rely on scenarios to represent future security prices. The model contains several interesting features, like the consideration of transaction costs, bid-ask spreads, arbitrage-free option pricing, and the possibility to rebalance the portfolio with options introduced at the start of each period. The resulting mixed integer programming model is applied to realistic test instances involving options on the S&P500 index. In spite of the large size and of the numerical difficulty of this model, near-optimal solutions can be computed by a standard branch-and-cut solver or by a specialized heuristic. The structure and the financial features of the selected portfolios are also investigated.