熵风险下的国内证券投资组合模型

通过结合我国的实际情况,考虑交易费用、限制约束、最小交易单位以及限制卖空等几个条件,建立一套针对我国证券市场的投资组合熵模型。该模型主要运用了求解整数规划问题的方法,根据主要赋予风险水平不同的取值。对模型进行求解。求得出对于不同风险水平的最优分配方案,以及对应收益的极大值,确定模型的有效边界,从而得出的结论：该模型是一套针对我国股票市场有效的投资组合模型,其与我国真实股票市场情况更接近,实用性更强。     

含有模糊约束的最优投资组合模型

文章考虑了收益率为梯形模糊数的投资组合问题。在一定置信水平上,本文用投资组合的模糊预期收益率偏离按历史数据计算估计出的收益率的程度来度量风险,建立了一种含有模糊约束的投资组合模型,并利用梯形模糊数的比较,将模型转化为相应的确定性二次规划问题进行求解。     

多目标模型在求解投资组合最优解中的应用

建立一个投资组合首先需要求解其中各项资产的最优比例,马科维茨的均值-方差理论已经成为了此类规划问题的经典解法,但是其锁定风险或收益其一求解另一者的方法需要人为设定约束条件的数值,因而可能不是理论上的最优解。本文试图将多目标模型运用到投资组合中求解风险资产的占比,再在考虑居民风险收益偏好的基础上探索在组合中加入无风险资产,从而为家庭资产的投资组合与运用提出建议。     

多目标模型在求解投资组合最优解中的应用

建立一个投资组合首先需要求解其中各项资产的最优比例,马科维茨的均值-方差理论已经成为了此类规划问题的经典解法,但是其锁定风险或收益其一求解另一者的方法需要人为设定约束条件的数值,因而可能不是理论上的最优解。本文试图将多目标模型运用到投资组合中求解风险资产的占比,再在考虑居民风险收益偏好的基础上探索在组合中加入无风险资产,从而为家庭资产的投资组合与运用提出建议。     

大学生证券投资与教育投资模型分析

通过定义教育、股票与基金等三种大学生课余投资的收益率与风险,利用模型计算出最优组合。针对Markowitz投资组合模型中协方差未知的情况,先后利用两基金分离定律与区间数模糊投资组合模型进行求解,最后探讨其可行性与适用性。     

基于VaR模型的股票组合投资风险管理研究

本文探讨了将GARCH模型与方差-协方差方法相结合的VaR风险计量方法,并用VaR风险替代Markiwitz组合投资模型中的方差风险,通过求解非线性规划问题,得到最小化股票投资组合VaR风险的最优投资策略。     

基子VaR模型的股票组合投资风险管理研究

本文探讨了将GARCH模型与方差-协方差方法相结合的VaR风险计量方法,并用VaR风险替代Markiwitz组合投资模型中的方差风险,通过求解非线性规划问题,得到最小化股票投资组合VaR风险的最优投资策略.     

基于带红利的控制公司破产风险因素投资组合模型

本文考虑在现金红利投资的收益情形下,投资者通过对所投资的股票公司的财务数据进行研究,通过评分来预测企业破产的概率,从而建立优质的投资组合模型。文章中应用最非线性规划优化的理论和方法建立投资组合模型,并利用优化算法对模型进行求解。     

不允许卖空情况下均值-半方差投资组合优化研究

运用不等式组的旋转算法求解不允许卖空情况下均值-半方差投资组合模型,并选取沪市六只业绩比较好的股票,进行模拟投资,计算出模型的总收益率,并与等比例投资相比较。结果表明均值-半方差投资组合的投资效果总体上优于等比例投资。     

用蚁群算法求解带交易成本的投资组合模型

证券投资组合优化问题的实质就是有限的资产在具有不同风险收益特征的证券之间的优化配置问题。本文在经典马科维茨投资组合的均值-方差模型框架下,将蚁群算法引入模型求解,提出考虑交易成本的股票投资组合模型。实证结果表明,蚁群算法是一种解决股票投资组合优化问题的有效算法,不同的参数设置对算法运行结果有显著影响。     

ENHANCEMENT OF THE APPLICABILITY OF MARKOWITZ'S PORTFOLIO OPTIMIZATION BY UTILIZING RANDOM MATRIX THEORY

The traditional estimated return for the Markowitz mean-variance optimization has been demonstrated to seriously depart from its theoretic optimal return. We prove that this phenomenon is natural and the estimated optimal return is always       times larger than its theoretic counterpart, where       with  y  as the ratio of the dimension to sample size. Thereafter, we develop new bootstrap-corrected estimations for the optimal return and its asset allocation and prove that these bootstrap-corrected estimates are proportionally consistent with their theoretic counterparts. Our theoretical results are further confirmed by our simulations, which show that the essence of the portfolio analysis problem could be adequately captured by our proposed approach. This greatly enhances the practical uses of the Markowitz mean-variance optimization procedure.     

Optimal Portfolios with Bounded Capital at Risk

We consider some continuous-time Markowitz type portfolio problems that consist of maximizing expected terminal wealth under the constraint of an upper bound for the capital at risk. In a Black–Scholes setting we obtain closed-form explicit solutions and compare their form and implications to those of the classical continuous-time mean-variance problem. We also consider more general price processes that allow for larger fluctuations in the returns.     

基于风险预算的新疆居民家庭金融资产的风险资产配置

通过对新疆2013-2017年的居民家庭金融总资产以及投资和储蓄的分析,从宏观的家庭金融资产视角使用Markowitz的投资组合的均值-方差模型在风算预算的基础上,进一步计算出实际新疆居民每年应当用于储蓄或者用于投资与固定风险和较大风险的比例,即得出风险资产的配置,从而提高新疆居民的储蓄向投资的转化,加强居民金融资产的管理和风险资产的分配,提高居民的风险收益,为新疆居民家庭获取更多金融资产投资于风险性收入提供更好的政策建议。     

Choosing an optimal investment strategy: The role of robust pair-copulas based portfolios

This paper is concerned with the efficient allocation of a set of financial assets and its successful management. Efficient diversification of investments is achieved by inputing robust pair-copulas based estimates of the expected return and covariances in the mean-variance analysis of Markowitz. Although the whole point of diversifying a portfolio is to avoid rebalancing, very often one needs to rebalance to restore the portfolio to its original balance or target. But when and why to rebalance is a critical issue, and this paper investigates several managers' strategies to keep the allocations optimal. Findings for an emerging market target return and minimum risk investments are highly significant and convincing. Although the best strategy depends on the investor risk profile, it is empirically shown that the proposed robust portfolios always outperform the classical versions based on the sample estimates, yielding higher gains in the long run and requiring a smaller number of updates. We found that the pair-copulas based robust minimum risk portfolio monitored by a manager which checks its composition twice a year provides the best long run investment.     

Learning equilibrium mean-variance strategy

We study a dynamic mean-variance portfolio optimization problem under the reinforcement learning framework, where an entropy regularizer is introduced to induce exploration. Due to the time–inconsistency involved in a mean-variance criterion, we aim to learn an equilibrium policy. Under an incomplete market setting, we obtain a semi-analytical, exploratory, equilibrium mean-variance policy that turns out to follow a Gaussian distribution. We then focus on a Gaussian mean return model and propose a reinforcement learning algorithm to find the equilibrium policy. Thanks to a thoroughly designed policy iteration procedure in our algorithm, we prove the convergence of our algorithm under mild conditions, despite that dynamic programming principle and the usual policy improvement theorem failing to hold for an equilibrium policy. Numerical experiments are given to demonstrate our algorithm. The design and implementation of our reinforcement learning algorithm apply to a general market setup.     

基于确定性偏好的投资组合选择

文章在对马科维茨证券投资组合模型简要评述的基础上，针对投资者可选标的证券信息集非对称的现实，依据确定性偏好原理，将投资者对可选标的证券信息的确定性程度转换成偏好次序关系，同时结合行为金融学中的前景理论，依确定性偏好次序规则来确定权重函数，并在价值函数-风险的框架下探讨了证券投资组合模型的构建及其最优解，从而在行为金融理论下扩展了马氏证券投资组合模型。实证分析表明，我国证券市场投资者基本是采用线性赋权方式来处理非对称信息集下的投资组合选择的。     

资产组合风险度量与选择之文献述评

根据资产组合理论的本质,理解和掌握资产组合理论具有重要的意义。本文主要从风险度量方法比较、现实金融资产收益的实际分布与相关性、以及金融资产收益的动态变化特征等角度,对资产组合风险度量与选择的相关文献进行回顾与评述。     

Optimal Dynamic Portfolio Selection: Multiperiod Mean‐Variance Formulation

The mean‐variance formulation by Markowitz in the 1950s paved a foundation for modern portfolio selection analysis in a single period. This paper considers an analytical optimal solution to the mean‐variance formulation in multiperiod portfolio selection. Specifically, analytical optimal portfolio policy and analytical expression of the mean‐variance efficient frontier are derived in this paper for the multiperiod mean‐variance formulation. An efficient algorithm is also proposed for finding an optimal portfolio policy to maximize a utility function of the expected value and the variance of the terminal wealth.     

Emerging markets,downside risk and the asset allocation decision

This study examines the use of downside risk measures in the construction of an optimal international portfolio, with particular reference to the estimated allocations in emerging markets and the out-of-sample performance of the optimal portfolios. The use of downside risk measures is assessed due to the problems of using a conventional mean-variance analysis approach in the presence of the non-normality often found to be present in emerging market data. The data set used consists of the MSCI indices for developed equity markets and the IFC data set on emerging markets. The primary component of the paper consists of the construction of optimal portfolios under both mean-variance and downside risk frameworks. In addition, the use of Bayes–Stein estimators is also assessed, in an attempt to reduce estimation error. The resulting estimated allocations are then used to assess the out-of-sample performance of the optimal portfolios. The results indicate that for risk-averse investors the use of downside risk measures can result in significant improvements in performance.