多阶段均值-方差资产负债管理的随机控制（英文）

资产负债管理研究如何合理分配资产以到达最小化风险同时确保期望剩余财富(财富减去负债)达到一定水平.本文在均值-方差投资组合理论的框架下研究两类资产负债管理模型,包括带有跨期均值-方差投资目标和带有非破产约束的模型.由于在动态规划意义下,方差不具有可分性质,传统的随机最优控制方法难以直接应用.如采用处理动态均值-方差优化问题的嵌入法来解决以上问题会带来计算上的困难.本文借鉴平均场控制的思想对以上两类问题加以研究.本文假设了非常宽泛的市场模型:所有的资产都是风险资产;债务和风险资产之间存在相关性.在此市场假设模型下,本文给出了最优投资策略(控制率)的解析表达式和均值-方差有效前沿的表达形式.本研究成果为投资者提供了新的投资策略,可应用于更复杂的资产负债管理中.     

多阶段均值–方差资产负债管理的随机控制

资产负债管理研究如何合理分配资产以到达最小化风险同时确保期望剩余财富(财富减去负债)达到一定水平.本文在均值–方差投资组合理论的框架下研究两类资产负债管理模型, 包括带有跨期均值–方差投资目标和带有非破产约束的模型. 由于在动态规划意义下, 方差不具有可分性质, 传统的随机最优控制方法难以直接应用. 如采用处理动态均值–方差优化问题的嵌入法来解决以上问题会带来计算上的困难. 本文借鉴平均场控制的思想对以上两类问题加以研究. 本文假设了非常宽泛的市场模型: 所有的资产都是风险资产; 债务和风险资产之间存在相关性. 在此市场假设模型下, 本文给出了最优投资策略(控制率)的解析表达式和均值–方差有效前沿的表达形式. 本研究成果为投资者提供了新的投资策略, 可应用于更复杂的资产负债管理中.     

考虑通货膨胀因素下的连续时间均值-方差投资组合选择

考虑通货膨胀因素,利用均值-方差模型研究连续时间投资组合选择问题.利用 Lagrange 乘子技术将原均值-方差模型转化为一个标准的随机最优控制问题,应用动态规划的方法得到问题的解析解,进而求解出原均值-方差模型的有效投资策略和有效边界的解析表达式.通过实证分析进一步表明了结论的正确性.     

基于集对分析的不确定性多属性决策模型与算法

针对不确定性多属性决策问题,提出统计学中的样本特征参数均值和方差也是模糊数（区间数、三角模糊数、梯形模糊数）特征参数的理论,并用联系数A+Bi表示“均值+方差”,再根据集对分析中的联系数理论计算“均值+方差”的“模”,用这种“模”代表属性权重和属性值进行不确定性多属性决策,简便实用;利用“均值+方差”联系数中i的不同取值可以考察不确定性对排序的影响.应用实例表明该理论是有效和可行的.     

风险资产收益序列相关的多阶段均值-方差投资组合选择

基于多阶段均值-方差框架, 研究任意多种风险资产存在一般收益序列相关时的投资组合选择问题. 首先, 采用Lagrange 对偶原理与动态规划相结合的方法对模型进行求解, 得到多阶段均值-方差模型的有效投资策略和有效边界的解析表达式; 然后, 证明在含有无风险资产的情形下有效边界仍为均值-标准差平面上的一条射线; 最后, 应用所得结论给出一个具体的实例分析.

     

带内生负债的不确定终止时间多期均值–方差资产–负债管理

本文研究了带内生负债的不确定退出时间多期均值-方差资产-负债管理问题.和外生负债不可控所不同的是,内生负债可通过各种金融工具和投资者(机构)的决策来调控.在本文的模型中,投资者(机构)在考虑资产最优配置的同时,还需要考虑负债的最优配置.本文采用Lagrange对偶理论、矩阵Hadamard乘积技术和动态规划方法对模型进行分析性求解,得到了模型的有效策略及有效边界的显式表达式.     

基于易腐品和不可分割耐用品的最优消费投资与寿险购买策略

随着经济的发展和人民生活水平的提高,投资者的投资组合不再局限于证券市场投资.通过将寿险购买引入投资者的投资组合并划分消费品为易腐品或不可分割耐用品,研究投资者的最优消费投资与寿险购买策略.投资者的投资目标为期望效用最大化.运用动态规划原理得到哈密尔顿-雅可比-贝尔曼方程,最终得到最优策略满足的方程,并讨论方程存在正根的条件.最后通过数值分析方法,验证模型结论与实际现实情况的一致性.     

基于联系数的区间粗糙数多属性决策方法

定义了正态分布的区间粗糙数,补充联系数的运算法则。利用集对分析的不确定性理论,建立了区间粗糙数的均值-方差联系数模型,针对属性值为区间粗糙数且权重已知的区间粗糙数多属性决策问题,提出了一种基于联系数的区间粗糙数多属性决策方法,并给出了决策的步骤。最后,通过实例分析,表明该方法的有效性和实用性,且计算简单易行。     

基于随机基准的动态均值-方差投资组合选择

在不完全市场下, 研究基于随机基准的动态均值-方差投资组合选择问题. 该问题也可以理解为一个跟踪误差动态投资组合问题, 并将之转化为一个等价的考虑风险调整的期望相对收益最大化问题. 利用随机动态规划方法, 给出了最优投资策略和有效前沿的显式表达式. 最后通过实证分析表明了不完全市场和完全市场下最优投资策略和有效前沿的变化, 并对相关结论进行了经济解释.

     

考虑投资者心理特征的模糊投资组合优化模型

投资者在实际金融市场中的决策行为往往会受到主观心理认知的影响.考虑参照依赖、敏感性递减和损失厌恶等影响投资决策的心理特征,研究模糊环境下的投资组合选择问题.首先,假设资产的收益为梯形模糊数,依据前景理论中的价值函数,将组合收益转化为体现投资者心理特征的感知价值;然后,以感知价值的可能性均值最大化和可能性下半方差最小化为目标,建立考虑心理特征的模糊投资组合优化模型;接着,为了有效地求解模型,设计一个多种群遗传算法;最后,通过实例分析表明模型和算法的有效性.结果表明,与传统的遗传算法相比,所设计的多种群遗传算法可更有效地求解模型,考虑心理特征的模糊投资组合优化模型能够提升投资者的满意程度,可为实际的投资活动提供决策支持.     

Risk control over bankruptcy in dynamic portfolio selection: a generalized mean-variance formulation

For an investor to claim his wealth resulted from his multiperiod portfolio policy, he has to sustain a possibility of bankruptcy before reaching the end of an investment horizon. Risk control over bankruptcy is thus an indispensable ingredient of optimal dynamic portfolio selection. We propose in this note a generalized mean-variance model via which an optimal investment policy can be generated to help investors not only achieve an optimal return in the sense of a mean-variance tradeoff, but also have a good risk control over bankruptcy. One key difficulty in solving the proposed generalized mean-variance model is the nonseparability in the associated stochastic control problem in the sense of dynamic programming. A solution scheme using embedding is developed in this note to overcome this difficulty and to obtain an analytical optimal portfolio policy.     

Models and Simulations for Portfolio Rebalancing

In 1950 Markowitz first formalized the portfolio optimization problem in terms of mean return and variance. Since then, the mean-variance model has played a crucial role in single-period portfolio optimization theory and practice. In this paper we study the optimal portfolio selection problem in a multi-period framework, by considering fixed and proportional transaction costs and evaluating how much they affect a re-investment strategy. Specifically, we modify the single-period portfolio optimization model, based on the Conditional Value at Risk (CVaR) as measure of risk, to introduce portfolio rebalancing. The aim is to provide investors and financial institutions with an effective tool to better exploit new information made available by the market. We then suggest a procedure to use the proposed optimization model in a multi-period framework. Extensive computational results based on different historical data sets from German Stock Exchange Market (XETRA) are presented.     

股指期货套利管理系统

针对我国股指期货市场存在的问题——理论与实际应用脱节,造成股指期货市场的发展需求和实际发展条件不平衡,提出构建股指期货套利管理系统的策略. 依据实地调研资料进行系统设计,使用C#实现股指期货套利管理系统（SIFAM-System）. 系统实现了基于基差的跨期套利和基于无套利区间的跨期套利,以及与套利相关的信息管理功能. SIFAM-System利用计算机实现模型计算、行情监控、套利机会的判断及开平仓,达到了辅助投资者决策的目的,也为股指期货市场的发展问题提供了从理论到实现的解决策略.     

Portfolio selection using neural networks

In this paper we apply a heuristic method based on artificial neural networks (NN) in order to trace out the efficient frontier associated to the portfolio selection problem. We consider a generalization of the standard Markowitz mean-variance model which includes cardinality and bounding constraints. These constraints ensure the investment in a given number of different assets and limit the amount of capital to be invested in each asset. We present some experimental results obtained with the NN heuristic and we compare them to those obtained with three previous heuristic methods. The portfolio selection problem is an instance from the family of quadratic programming problems when the standard Markowitz mean-variance model is considered. But if this model is generalized to include cardinality and bounding constraints, then the portfolio selection problem becomes a mixed quadratic and integer programming problem. When considering the latter model, there is not any exact algorithm able to solve the portfolio selection problem in an efficient way. The use of heuristic algorithms in this case is imperative. In the past some heuristic methods based mainly on evolutionary algorithms, tabu search and simulated annealing have been developed. The purpose of this paper is to consider a particular neural network (NN) model, the Hopfield network, which has been used to solve some other optimisation problems and apply it here to the portfolio selection problem, comparing the new results to those obtained with previous heuristic algorithms.     

基于高频数据的股指期货期现统计套利程序交易

我国最近推出股指期货后,大量投资者采用传统的基于持有成本理论的日间股指期货期现套利策略进场套利,使得期现套利的价差很快收窄,可套利机会越来越少。从两个角度对传统的股指期货期现套利策略进行拓展:一方面,将获取绝对收益的统计套利策略引入到股指期货期现套利中,并解决统计套利策略在进行股指期货期现套利时遇到的问题;另一方面,将交易标点的交易周期深入到分钟级别的高频行情。通过这两个方面的拓展,不仅开阔了股指期货期现套利的理论研究思路,而且获得了较好的收益风险系数,对投资实践也具有一定的指导意义。     

一种基于改进免疫遗传算法的证券组合投资策略

针对Markowitz均值-方差模型的不足，结合文献[1]算法提出了一种基于改进免疫遗传算法的证券组合投资策略，它克服了均值一方差模型的收益率必须服从正态分布的局限，并通过将投资收益率、风险损失和风险报酬三者分开度量，明晰了三者的关系。通过引入偏好系数加权法将上述三者进行权衡。仿真结果揭示了风险报酬和风险损失呈线性关系，得到了较好的效果。     

A Bayesian information criterion for portfolio selection

The mean-variance theory of Markowitz (1952) indicates that large investment portfolios naturally provide better risk diversification than small ones. However, due to parameter estimation errors, one may find ambiguous results in practice. Hence, it is essential to identify relevant stocks to alleviate the impact of estimation error in portfolio selection. To this end, we propose a linkage condition to link the relevant and irrelevant stock returns via their conditional regression relationship. Subsequently, we obtain a BIC selection criterion that enables us to identify relevant stocks consistently. Numerical studies indicate that BIC outperforms commonly used portfolio strategies in the literature.     

Evolutionary multi-objective portfolio optimization in practical context

This paper addresses evolutionary multi-objective portfolio optimization in the practical context by incorporating realistic constraints into the problem model and preference criterion into the optimization search process. The former is essential to enhance the realism of the classical mean-variance model proposed by Harry Markowitz, since portfolio managers often face a number of realistic constraints arising from business and industry regulations, while the latter reflects the fact that portfolio managers are ultimately interested in specific regions or points along the efficient frontier during the actual execution of their investment orders. For the former, this paper proposes an order-based representation that can be easily extended to handle various realistic constraints like floor and ceiling constraints and cardinality constraint. An experimental study, based on benchmark problems obtained from the OR-library, demonstrates its capability to attain a better approximation of the efficient frontier in terms of proximity and diversity with respect to other conventional representations. The experimental results also illustrated its viability and practicality in handling the various realistic constraints. A simple strategy to incorporate preferences into the multi-objective optimization process is highlighted and the experimental study demonstrates its capability in driving the evolutionary search towards specific regions of the efficient frontier.     

A hybrid algorithm for constrained portfolio selection problems

Since Markowitz’s seminal work on the mean-variance model in modern portfolio theory, many studies have been conducted on computational techniques and recently meta-heuristics for portfolio selection problems. In this work, we propose and investigate a new hybrid algorithm integrating the population based incremental learning and differential evolution algorithms for the portfolio selection problem. We consider the extended mean-variance model with practical trading constraints including the cardinality, floor and ceiling constraints. The proposed hybrid algorithm adopts a partially guided mutation and an elitist strategy to promote the quality of solution. The performance of the proposed hybrid algorithm has been evaluated on the extended benchmark datasets in the OR Library. The computational results demonstrate that the proposed hybrid algorithm is not only effective but also efficient in solving the mean-variance model with real world constraints.