Application of Portfolio Model in the Real Investment Transactions

This paper studies discrete investment portfolio model that the objective function is utility function. According to a hybrid branch-and-bound method based on Lagrangian relaxation and continuous relaxation, the paper analyzes the question using the real statistical data. The results indicate that discrete investment portfolio model really has its guidance in the actual investment.

Portfolio Choice under the Mean-Variance Model with Parameter Uncertainty

Assuming the investor is uncertainty-aversion,the multiprior approach is applied to studying the problem of portfolio choice under the uncertainty about the expected return of risky asset based on the mean-variance model. By introducing a set of constraint constants to measure uncertainty degree of the estimated expected return,it built the max-min model of multi-prior portfolio,and utilized the Lagrange method to obtain the closed-form solution of the model,which was compared with the mean-variance model and the minimum-variance model; then,an empirical study was done based on the monthly returns over the period June 2011 to May 2014 of eight kinds of stocks in Shanghai Exchange 50 Index. Results showed,the weight of multi-prior portfolio was a weighted average of the weight of mean-variance portfolio and that of minimumvariance portfolio; the steady of multi-prior portfolio was strengthened compared with the mean-variance portfolio; the performance of multi-prior portfolio was greater than that of minimum-variance portfolio. The study demonstrates that the investor can improve the steady of multi-prior portfolio as well as its performance for some appropriate constraint constants.

Dynamic Portfolio Choice under Uncertainty about Asset Return Model

The effect of uncertainty about stochastic diffusion model on dynamic portfolio choice of an investor who maximizes utility of terminal portfolio wealth was studied.It applied stochastic control method to obtain the closed-form solution of optimal dynamic portfolio,and used the Bayesian rule to estimate the model parameters to do an empirical study on two different samples of Shanghai Exchange Composite Index.Results show,model uncertainty results in positive or negative hedging demand of portfolio,which depends on investor's attitude toward risk;the effect of model uncertainty is more significant with the increasing of investment horizon,the decreasing of investor's risk-aversion degree,and the decreasing of information;predictability of risky asset return increases its allocation in portfolio,at the same time,the effect of model uncertainty also strengthens.

A branch-and-bound algorithm for discrete multi-factor portfolio optimization model

In this paper, a new branch-and-bound algorithm based on the Lagrangian dual relaxation and continuous relaxation is proposed for discrete multi-factor portfolio selection model with roundlot restriction in financial optimization. This discrete portfolio model is of integer quadratic programming problems. The separable structure of the model is investigated by using Lagrangian relaxation and dual search. Computational results show that the algorithm is capable of solving real-world portfolio problems with data from US stock market and randomly generated test problems with up to 120 securities.

Stochastic Modelling on Dynamics of Portfolio Diversifications among the Fixed and Operational Investments through Internal Bivariate Linear Birth, Death and Migration Processes

In this paper, a bivariate stochastic process with Poisson postulates has been considered to model the incomings, outgoings and mutual transfers of investments between and within the portfolios during an epoch of time “t”. Stochastic differential equations were obtained from the simple differential difference equations during the epoch of time “Δt”. The notion of bivariate linear birth, death and migration process has been utilized for measuring various statistical characteristics among the investments of Long and Short terms. All possible fluctuations in the investment flow have been considered to explore more meaningful assumptions with contemporary marketing environments. Mathematical relations for proposed statistical measures such as average sizes and variances of short term and long-term investments along with the correlation coefficient between them are derived after obtaining the related differential equations. Numerical illustrations were provided for better understanding of the developed models with practitioner’s point of view.

Online risk‑based portfolio allocation on subsets of crypto assets applying a prototype‑based clustering algorithm

Mean-variance portfolio optimization models are sensitive to uncertainty in risk-return estimates,which may result in poor out-of-sample performance.In particular,the estimates may suffer when the number of assets considered is high and the length of the return time series is not sufficiently long.This is precisely the case in the cryptocur-rency market,where there are hundreds of crypto assets that have been traded for a few years.We propose enhancing the mean-variance(MV)model with a pre-selection stage that uses a prototype-based clustering algorithm to reduce the number of crypto assets considered at each investment period.In the pre-selection stage,we run a prototype-based clustering algorithm where the assets are described by variables representing the profit-risk duality.The prototypes of the clustering partition are auto-matically examined and the one that best suits our risk-aversion preference is selected.We then run the MV portfolio optimization with the crypto assets of the selected cluster.The proposed approach is tested for a period of 17 months in the whole cryp-tocurrency market and two selections of the cryptocurrencies with the higher market capitalization(175 and 250 cryptos).We compare the results against three methods applied to the whole market:classic MV,risk parity,and hierarchical risk parity methods.We also compare our results with those from investing in the market index CCI30.The simulation results generally favor our proposal in terms of profit and risk-profit financial indicators.This result reaffirms the convenience of using machine learning methods to guide financial investments in complex and highly-volatile environments such as the cryptocurrency market.

Schedule Model for Project Portfolio Based on Design Structure Matrix

To improve the enterprise resource utilization and shorten the cycle of the whole project portfolio, a scheduling model based on Design Structure Matrix （DSM） is built. By setting the project activity weight index system and calculating the activity weight for the project portfolio, the constraint relationship between project portfolio information and resource utilization, as the two dimensions of the DSM, are fully reflected in the sched- ule model to determine the order of these activities of project portfolio. A project portfolio example is given to il- lustrate the applicability and effectiveness of the schedule model.

Multi-Knapsack Model of Collaborative Portfolio Configurations in Multi-Strategy Oriented

Aiming at constructing the multi-knapsack model of collaborative portfolio configurations in multi-strategy oriented, the hybrid evolutionary algorithm was designed based on greedy method, combining with the organization of the multiple strategical guidance and multi-knapsack model. Furthermore, the organizing resource utility and risk management of portfolio were considered. The experiments were conducted on three main technological markets which contain communication, transportation and industry. The results demonstrated that the proposed model and algorithm were feasible and reliable.

Copula模型的改进及其应用

Copula模型能精确计算投资组合尾部风险,弥补Person相关系数的不足。文章基于信用风险Cop⁃ula模型,探讨了不同抽样算法在信贷投资组合中的应用问题,优化重要性抽样和交叉熵算法,测试了高斯及t-Copula模型的风险计算算法,并通过数值模拟予以检验,结果表明:朴素蒙特卡罗模拟的精度和效率较低;重要性抽样算法通过解析逼近显著降低计算方差,提高精度,但求解复杂且耗时;交叉熵算法同样有效,但需自适应算法求解优化问题。算例分析结果表明,基于不同场景选择Copula模型,可提高信贷投资组合风险计算精度和效率。

工程项目组合风险扩散模型的级联失效分析

为构建符合工程项目组合特点的风险扩散模型,解决风险扩散问题。本文考虑风险扩散的双关系载体—项目交互关系和风险因素逻辑关系,构建工程项目组合风险扩散的双层相依网络模型,运用级联失效理论提出风险扩散规则,通过数值仿真分析风险扩散特征。仿真结果发现:工程项目组合中,双层网络模型能够反映风险在项目和风险因素双关系载体中的扩散特征;与单层网络风险扩散模型相比,构建的模型展现出差异性的风险扩散效应,风险扩散在工程项目组合中表现出更剧烈的级联失效过程。风险扩散模型作为工程项目组合风险扩散问题的前置性研究,为进一步风险扩散网络稳定性的研究提供新思路和启发。

基于改进Black-Litterman模型的投资组合优化

考虑到金融市场“非完全有效性”且投资者“非完全理性”,通过贝叶斯框架建立了投资者观点与多渠道信息相结合的改进Black-Litterman模型,由此确定了最优的个性化投资策略.在中国股票市场的实证研究中,利用SVM-ARIMA-GARCH模型解决了投资者观点量化的问题.对比几类参考策略,改进Black-Litterman模型所确定的最优投资策略的样本外绩效表现更加稳健,在不同市场行情下均能获得较高的夏普比率和较低的换手率.

得分驱动时变DCC模型及其应用

收益率协方差矩阵在投资组合和风险计量中有重要应用。文章先用得分驱动方法推导出DCC模型的等价形式,以此表明DCC模型的观测驱动特性;然后再次采用得分驱动方法将DCC模型的参数时变化,得出加速DCC(aDCC)模型。随机模拟及实证研究显示,加速DCC模型不仅具有更好的波动拟合效果,而且基于其构建的最小方差组合还能实现更小的组合方差及夏普比率。

基于压力-状态-响应和云模型的危废处置中心安全韧性评价

为提高危险废物处置中心的系统风险抵抗能力,保证其处置过程安全进行,以武汉某危险废物处置中心为研究对象,基于压力-状态-响应(PSR)模型从压力、状态以及响应3个方面构建危险废物处置中心的安全韧性评价指标体系,运用层次分析法和熵权法分别得出主观权重值和客观权重值,并运用组合赋权法得出各指标组合权重值,根据云模型得出危险废物处置中心的安全韧性等级为“良好”。结果表明,基于PSR模型的安全韧性评价模型对于系统安全评价具有一定的科学性和借鉴意义。

基于扭曲风险度量的鲁棒投资策略

投资组合策略在很大程度上取决于损失的基本分布.因此当损失的分布信息只能通过有限的数据样本来观察时,投资组合策略模型的稳健性是至关重要的.假设损失的基本分布具有已知的均值和方差且位于一个以经验分布为中心,以Wasserstein距离为半径的球内,本文建立了一个基于扭曲风险度量的稳健投资组合策略模型,并将其转化为更简便的等价形式.此外,本文运用模拟和实证研究证明了该模型的有效性.

基于投资组合高阶矩分析的电力系统灵活性评估

针对现有灵活性指标形式主要集中于低阶统计量,仅能反映系统灵活性的平均水平与集中程度,而忽视了灵活性的高阶特征这一问题,引入投资组合高阶矩分析理论对电力系统灵活性进行刻画,以揭示系统灵活性的调节潜力与风险。首先,通过分析投资组合的均值-方差-偏度-峰度模型(mean-variance-skewness-kurtosismodel,MVSK Model),给出了灵活性单元组合的定义,基于多元Copula函数构建考虑空间相关性的灵活性单元概率模型。其次,基于灵活性单元组合的各阶矩建立灵活性评估指标,并给出基于核密度估计与蒙特卡洛模拟的指标计算方法。最后,通过FTS-213测试系统与德国某电网的历史数据对所提指标进行了测算和验证。算例表明所提指标能够反映系统灵活性调节能力的平均水平、稳定程度、潜力与风险,且能量化评估灵活性资源种类与投建地区对系统灵活性的影响,为后续的灵活性资源规划提供理论支持。

System portfolio selection based on GRA method under hesitant fuzzy environment

The hesitant fuzzy set(HFS) is an important tool to deal with uncertain and vague information.In equipment system portfolio selection, the index attribute of the equipment system may not be expressed by precise data;it is usually described by qualitative information and expressed as multiple possible values.We propose a method of equipment system portfolio selection under hesitant fuzzy environment.The hesitant fuzzy element(HFE) is used to describe the index and attribute values of the equipment system.The hesitation degree of HFEs measures the uncertainty of the criterion data of the equipment system.The hesitant fuzzy grey relational analysis(GRA) method is used to evaluate the score of the equipment system, and the improved HFE distance measure is used to fully consider the influence of hesitation degree on the grey correlation degree.Based on the score and hesitation degree of the equipment system,two portfolio selection models of the equipment system and an equipment system portfolio selection case is given to illustrate the application process and effectiveness of the method.

Optimal Portfolio Rules with Habit Formation and Preference for Wealth

This paper describes a model in which a representative investor's preference depends on both the consumption history consumption and his wealth. Thus, the investor accumulates wealth not only for the sake of consumption history but also for wealth. We examine the implication for consumption, portfolio choice. We solve the consumption portfolio choice problem and provide the optimal policy. The optimal solution to the problem shows that the preference for wealth and consumption formation will affect the investor's optimal portfolio policy. For the purpose of further research, we also calculate the steady-state distribution of habit-consumption ratio.