基于极端损失约束的均值-方差投资组合选择研究

将极端损失约束引入投资组合选择问题研究,构建了极端损失约束的均值一方差投资组合模型,通过拉格朗日乘数法,得到了最优投资组合策略及有效边界的解析解,并就投资策略运用方面进行了实证分析结果表明:在方差一均值坐标中有效边界与经典的均值一方差模型的有效边界形状是一致的,但极端损失约束越紧,有效前沿越向方差一均值坐标的右侧移动;在传统均值-方差模型中加入极端损失约束可以改善投资组合的业绩表现,相对于等权组合策略和最小方差策略而言,适当的极端损失约束可以给投资者带来最好的投资业绩。     

现代资产组合理论的产生与发展综述

本文主要讨论现代资产组合理论的产生、发展与局限性。1952年Markowitz提出的均值一方差理论标志着现代资产组合理论的正式形成;进入20世纪60年代,以Sharpe、Lintner和Mossin为代表的一批学者,以Markowitz的均值一方差理论为基础,提出著名的资本资产定价模型(CAPM);作为CAPM的进一步推广,套利定价理论(ATP)显得更直观。现代资产组合理论尚存在理论假设过多、风险分散方式有限、风险判断机械、实际应用操作困难等方方面面的缺陷。     

全国社保基金投资组合绩效评价理论综述

对全国社保基金以及全国社保基金投资组合绩效评价的有关理论进行了总结,分析了现代投资组合理论与组合绩效的衡量方法,Markowitz的均值一方差模型为有效衡量和测度投资组合的绩效表现奠定了重要的理论基础,在此基础上分析了CAPM模型、古典单因素评估模型、组合时变性评估模型、条件绩效模型和其他相关理论研究,为全国社保基金投资组合绩效评价的深入展开作出必要的准备和铺垫。     

基于均值——方差模型的云南特色股票研究

Markowitz均值——方差模型是用于研究投资者如何通过合理的资金分配来达到既定收益下,风险最小化问题.本文采用该模型,基于投资优化组合的理论对云南省内地的特色股票做投资组合分析,通过对五支股票数据的统计分析,得到在预期收益既定的条件下,投资风险最小时的最优投资组合。同时,根据这五支股票的投资权重进一步精选出三支股票,并计算出了最优的投资权重。     

CVaR模型的有效边界与组合选择

CVaR模型是经典马柯维茨均值一方差模型的直接推广,即由CVaR来直接代替方差作为风险约束条件,使得投资组合模型在新的度量标准下更加合理。本文证明了基于CVaR约束下投资组合模型有效边界的上凸性,并在收益为正态分布的假定下,结合负指数效用函数,解决了投资组合的选择问题,求得具体的显示解,并得出与均值一方差模型相一致的结论。     

两个证券组合模型的实证比较

本文随机选取沪深300中的十只股票为例,对证券投资组合的均值-方差模型(MV)和最小二阶矩模型进行了实证比较.计算分析发现,最小二阶矩模型比MV模型具有更好的业绩表现.     

Black-Litterman模型在中国养老金入市投资中的应用

本文利用Black-Litterman模型对养老金投资的金融资产进行优化配置。Black-Litterman资产配置模型是对经典的资产配置模型——Markowitz均值-方差模型的改进。它一方面解决了Markowitz模型对输入参数的高度敏感性的弱点,另一方面它导入了投资者对某项资产的主观预期。本文在第二章简单介绍并推导出Black-Litterman模型。随后又给出如何在Black-Litterman模型中加入主观观点并得出最优配置公式。在第三章中,本文利用Black-Litterman模型对养老金投资到各项金融资产的分配比例进行实证。本文假设有三种投资渠道:国债,企业债券和股票;以国债指数,企债指数和沪深300指数为数据,计算出三种资产分别的均值和方差,并作为主观观点输入Black-Litterman模型,最终得出最优投资比例。     

关于最优资本配置模型研究的文献综述

资本配置的优化问题一直是学界研究的热点,传统的马科维茨均值方差模型在提出之后,被广泛的应用于投资组合的选择和资本配置。国内外学者受其启发,开始在该理论的基础上对最优资本配置模型进行优化与改良,得到了相当丰富的研究成果。本文整理了最优资本配置模型的研究文献,涉及的较为典型的模型除了传统的均值方差模型外,还有损失最小化模型、MV模型和尾均值—方差模型,在对这些模型进行比较分析的基础上本文提出了未来研究中仍需改进的三个方面。     

基于双基准与多风险制度下的中国外汇储备币种结构配置研究北大核心CSSCISSCI

内容借鉴Markowitz基本均值方差模型的思想,本文运用基于双投资基准和多风险制度的投资组合模型对我国外汇储备币种结构配置进行了实证研究。本文首先从中国实际出发确定了两个投资基准和三种风险制度,进而在此基础上实证研究了在不同投资基准、不同风险制度和不同风险厌恶度情况下的我国外汇储备币种结构。最后,本文提出在不同风险制度转换过程中所对应的我国外汇储备各币种所占比重的调整方向。     

基于双基准与多风险制度下的中国外汇储备币种结构配置研究

内容借鉴Markowitz基本均值方差模型的思想,本文运用基于双投资基准和多风险制度的投资组合模型对我国外汇储备币种结构配置进行了实证研究。本文首先从中国实际出发确定了两个投资基准和三种风险制度,进而在此基础上实证研究了在不同投资基准、不同风险制度和不同风险厌恶度情况下的我国外汇储备币种结构。最后,本文提出在不同风险制度转换过程中所对应的我国外汇储备各币种所占比重的调整方向。     

A CROSS-SECTIONAL REGRESSION TEST OF THE MEAN-VARIANCE EFFICIENCY OF AN AUSTRALIAN VALUE WEIGHTED MARKET PORTFOLIO

This paper tests the Mean-Variance efficiency of a value weighted Australian market portfolio using a multivariate cross-sectional regression approach developed by Shanken (1985). This test methodology is sufficiently powerful to reject the null hypothesis that the market portfolio is ex ante Mean-Variance efficient when test assets are constructed on the basis of size (market capitalisation). However, when test assets are constructed on the basis of industry classification the model is unable to reject the Mean-Variance efficiency of the market portfolio. This test statistic provides some useful diagnostics which are examined in the paper.     

Portfolio selection with a drawdown constraint

When identifying optimal portfolios, practitioners often impose a drawdown constraint. This constraint is even explicit in some money management contracts such as the one recently involving Merrill Lynch’ management of Unilever’s pension fund. In this setting, we provide a characterization of optimal portfolios using mean–variance analysis. In the absence of a benchmark, we find that while the constraint typically decreases the optimal portfolio’s standard deviation, the constrained optimal portfolio can be notably mean–variance inefficient. In the presence of a benchmark such as in the Merrill Lynch–Unilever contract, we find that the constraint increases the optimal portfolio’s standard deviation and tracking error volatility. Thus, the constraint negatively affects a portfolio manager’s ability to track a benchmark.     

An algorithmic trading system based on a stacked generalization model and hidden Markov model in the foreign exchange market

The Forex market has been one of the most attractive markets to researchers, funds, and traders. Literature shows that a single model algorithm usually cannot perform satisfactorily on the foreign exchange (Forex) time series because of the market's complexity. This study develops an algorithm based on two stacked generalization models consisting of four machine-learning models. First, the optimal lags of features are found using the Fisher discriminant ratio and partial autocorrelation function. Second, one stacked model fits the bullish trends, and the other holds the bearish trends resulting from a hidden Markov model. We reinforce the predictive signals of these models by extracting relationships between currency pairs with correlation and mutual information. Lastly, the proposed algorithm constructs a portfolio based on the strength of signals dependent on correlation and mutual information. As a result, this paper compares the performance of the proposed approach with different states of the model and several established benchmarks regarding return and risk metrics. The outcomes show that the proposed model's added features—such as time series clustering, considering a range of inputs, and internal relationships among different assets—can increase its performance in the Forex market.     

Optimal life insurance with no-borrowing constraints: duality approach and example

We solve an optimal portfolio choice problem under a no-borrowing assumption. A duality approach is applied to study a family’s optimal consumption, optimal portfolio choice, and optimal life insurance purchase when the family receives labor income that may be terminated due to the wage earner’s premature death or retirement. We establish the existence of an optimal solution to the optimization problem theoretically by the duality approach and we provide an explicitly solved example with numerical illustration. Our results illustrate that the no-borrowing constraints do not always impact the family’s optimal decisions on consumption, portfolio choice, and life insurance. When the constraints are binding, there must exist a wealth depletion time (WDT) prior to the retirement date, and the constraints indeed reduce the optimal consumption and the life insurance purchase at the beginning of time. However, the optimal consumption under the constraints will become larger than that without the constraints at some time later than the WDT.     

Portfolio Selection and Optimization with Higher Moments: Evidence from the Indian Stock Market

The Markowitz portfolio optimization model, popularly known as the Mean-Variance model, assumes that stockreturns follow normal distribution. But when stock returns do not follow normal distribution, this model wouldbe inadequate as it would prescribe sub-optimal portfolios. Stock market literature often deliberates that stock returns are non-normal. In such context the Markowitz model would not be sufficient to estimate the portfolio risks. The purpose of this paper is to expand the original Markowitz portfolio theory (mean-variance) via adding the higher order moments like skewness (third moment about the mean) and kurtosis (fourth moment about the mean) in the return characteristics. The research paper investigates the impact of including higher moments using multi-objective programming model for portfolio stock selection and optimization. The empirical results indicate that the inclusion of higher moments had a considerable impact in estimating the returns behavior of portfolios. The portfolios optimized using all the four moments, generated higher returns for the given level of risk in comparison to the returns of the Markowitz model during the study period 2000–2011. The results of this study would be immensely useful to fund managers, portfolio managers and investors as it would help them in understanding the Indian stock market behavior better and also in selecting alternative portfolio selection models.     

Strategic technology adoption and hedging under incomplete markets

We investigate the implications of technological innovation and non-diversifiable risk on entrepreneurial entry and optimal portfolio choice. In a real options model where two risk-averse individuals strategically decide on technology adoption, we show that the impact of non-diversifiable risk on the option timing decision is ambiguous and depends on the frequency of technological change. Compared to the complete market case, non-diversifiable risk may accelerate or delay the optimal investment decision. Moreover, strategic considerations regarding technology adoption play a central role for the entrepreneur’s optimal portfolio choice in the presence of non-diversifiable risk.     

Selecting a portfolio with skewness: Recent evidence from US,European, and Latin American equity markets

Polynomial goal programming, in which investor preferences for skewness can be incorporated, is utilized to determine the optimal portfolio from Latin American, US and European capital markets. The empirical findings suggest that the incorporation of skewness into an investor’s portfolio decision causes a major change in the resultant optimal portfolio. The empirical evidence indicates that investors do trade expected return of the portfolio for skewness.     

Islamic and conventional portfolios optimization under investor sentiment states: Bayesian vs Markowitz portfolio analysis

This paper investigates the portfolio optimization under investor’s sentiment states of Hidden Markov model and over a different time horizon during the period 2004–2016. To compare the efficient portfolios of the Islamic and the conventional stock indexes, we have employed two approaches: the Bayesian and Markowitz mean-variance. Our findings reveal that the Bayesian efficient frontier of Islamic and conventional stock portfolios is affected by the investor’s sentiment state and the time horizon. Our findings also indicate that the investor’s sentiment regimes change the Islamic and the conventional optimal diversified portfolios.Moreover, the results show that the potential diversification benefits seem to be more important when using the Bayesian approach than when applying the Markowitz approach. This finding is valid for the bearish, depressed, bullish and calm states in Islamic stock markets. However, the diversification of potential portfolios is significant only for the bullish and the bubble states in the conventional financial markets.The findings of the study provided additional evidence for investors to exploit googling investor sentiment states to evaluate the portfolio performance and make an optimal portfolio allocation.     

HARA utility maximization in a Markov-switching bond–stock market

We present a flexible multidimensional bond–stock model incorporating regime switching, a stochastic short rate and further stochastic factors, such as stochastic asset covariance. In this framework we consider an investor whose risk preferences are characterized by the hyperbolic absolute risk-aversion utility function and solve the problem of optimizing the expected utility from her terminal wealth. For the optimal portfolio we obtain a constant-proportion portfolio insurance-type strategy with a Markov-switching stochastic multiplier and prove that it assures a lower bound on the terminal wealth. Explicit and easy-to-use verification theorems are proven. Furthermore, we apply the results to a specific model. We estimate the model parameters and test the performance of the derived optimal strategy using real data. The influence of the investor’s risk preferences and the model parameters on the portfolio is studied in detail. A comparison to the results with the power utility function is also provided.