贝塔系数为1条件下的最优证券组合投资决策方法

由马尔科维茨创立并由夏普和林特纳等学者发展的现代组合证券（portfolio）投资理论是分散投资风险的有效途径。这里分散投资的作用主要是削减非系统风险（unsystematicrisk）或称非市场风险（nonmarketrisk），而对市场风险（marketrisk）只起到平均化的效果。因此证券组合通过多样化分散投资可降低总风险，但不能避免市场风险。在夏普的市场指数模型（marketmodel）和资本资产定价模型（CAPM）中，市场风险是以一种证券或证券组合与市场证券组合（marketportfolio）或市场指数的收益率协方差比市场证券组合收益率方差来度量的，该比率称…     

在有序期望收益率条件下的资产组合选择模型

本文主要探讨对马柯维茨的均值一方差资产组合选择模型的改进。运用一定方法对证券的预期收益率进行排序，以证券的预期收益率作为变量而不是以证券历史收益率的均值作为变量，在马柯维茨的均值一方差资产组合模型的基础上建立一个新模型，然后再在考虑固定交易成本的情况下对新模型进行改进。     

证券投资基金的最优投资组合模型研究

基金管理公司可以运用证券组合投资通过分散投资达到降低投资风险的目标。采用Markowitz理论中的约定,风险证券的评价采用预期收益率和收益率方差2项指标,从风险控制的角度出发建立证券组合投资模型,以确定最优化的投资组合。     

组合证券的多选少模型研究

一、问题的提出组合证券投资是分散投资风险的有效途径。H.M.Markowitz的组合证券投资决策模型奠定了现代组合证券投资理论的基础。在Markowitz理论中,风险证券的评价采用预期收益率和收益率方差（代表风险）两项指标。理性的投资行为应具有“非满足性”和“风险回避”两个特征,即在给定风险条件下寻求最大期望收益率或在一定期望收益条件下使投资风险最低,由此给出了两个等价的组合证券投资决策模型。应用Markowitz模型进行组合证券投资决策的过程是：投资者首先根据一定的原则和偏好选择一组证券作为投资对象,然后对这组已选好的投…     

证券投资组合的调整研究

马柯维茨的均值一方差分析是以单一投资回收期和投资事先知道证券收益率的概率分布为假设条件的。但是实际中的证券投资是一个动态过程，即投资回收是多期，而且证券收益率的概率分布亦是随着经济形势的发展而变化，因而一般证券组合亦随之而变。与之相关的一个问题是证券收益率在什么范围内变化，仍使得证券组合保持不变。本运用线性规划中的灵敏度分析对证券组合的调整进行了研究。给出院使原证券组合保持不变的资产收益率均值、协方差的变化范围。     

带有模糊收益的均值-方差-偏度投资组合选择模型

许多经验性的研究表明投资组合收益通常是非对称的,当均值和方差相同时,投资者喜欢非对称度较大的投资组合收益.为了衡量模糊投资组合收益的非对称性,偏度这一概念被定义为此文的三阶中心矩.作为对模糊均值-方差模型的扩展,我们提出均值-方差-偏度模型并考虑其相应的变化.为了求解这个模型,我们设计了带有模糊模拟的遗传算法.最后,给出几个数值例子来说明这个模型的思想以及这一算法的有效性.     

风险资产投资组合与无风险借贷的选择

本文主要依据马柯维茨的均值—方差模型 ,通过对投资组合的有效边界的分析 ,给出了投资者根据个人预期收益率确定无风险借贷的选择和风险资产投资组合的策略。     

基于“损失规避”对马科维茨理论“均值-方差”替代原理的质疑

目前风险度量研究中马科维茨理论的"均值-方差"替代原理认为,方差较大的投机风险或投资组合,其缺陷可用均值增大来弥补;均值较小的投机风险或投资组合,其缺陷也可用方差减小来弥补。文章以实验经济学得出的"损失规避"结论为基础,通过逻辑和数学关系推演,说明了"均值-方差"替代原理在无差异分析方面是不能严格成立的。     

关于证券投资风险的表述及相关模型分析

一、对马柯维茨相关理论假设前提的思考毋庸置疑,马柯维茨最初提出的分析收益风险的均值方差模型具有划时代的意义。他以方差度量风险代替非定量化的主观判断具有重要的理论突破价值。但结合实际情况加以分析,马柯维茨理论的假设前提值得进一步推敲。首先,马柯维茨理论中暗含假设。因为证券的收益率受到证券市场、所处行业以及企业自身等众多因素的影响,所以是随机变量,服从正态分布。因为对于正态分布而言,只要期望值、方差确定了,收益率分布情况也就确定了,由此马柯维茨以收益率的期望值和方差度量证券的收益和风险。从国外的研究…     

效用函数意义下证券组合投资问题的改进决策树方法

基于Markowitz证券组合投资决策模型,运用效用理论中的均值-方差效用函数与决策理论中的决策树方法,提出了一种证券组合投资问题的改进决策树方法;探讨与分析了改进决策树方法的构造与求解过程。改进决策树方法不但可以对证券组合投资问题的决策方案集进行最优投资策略的选择,而且可以对该决策方案集按照一定的标准进行排序。     

Multi-period portfolio optimization under possibility measures

A single-period portfolio selection theory provides optimal tradeoff between the mean and the variance of the portfolio return for a future period. However, in a real investment process, the investment horizon is usually multi-period and the investor needs to rebalance his position from time to time. Hence it is natural to extend the single-period fuzzy portfolio selection to the multi-period case based on the possibility theory. In this paper, we propose the possibilistic expected value and variance for the terminal wealth with fuzzy forms after T periods by using the central value operator. Classes of multi-period possibilistic mean-variance models are formulated originally under the assumption that the proceeds of risky assets are fuzzy variables. Besides, we apply a particle swarm optimization algorithm to solve the proposed multi-period fuzzy portfolio selection models. A numerical example is given to illustrate the performance of the proposed models and algorithm.     

Fuzzy possibilistic portfolio selection model with VaR constraint and risk-free investment

We propose a possibilistic portfolio model with VaR constraint and risk-free investment based on the possibilistic mean and variance, while assuming that the expected rate of returns is a fuzzy number. The model shows more clearly that, in the financial market affected by several non-probabilistic factors, risk-averse investors wish not only to reach the expected rate of returns in their actual investment, but also to assure that the maximum of their possible future risk is lower than an expected loss. Under the condition that the expected rate of returns is a normal distribution fuzzy variable, we proposed a theorem as the solution, and derive a crisp equivalent form of the possibilistic portfolio under constraints of VaR and risk-free investment. This model is an expansion of the fuzzy possibilistic mean–variance model by Zhang (2007). Finally, an empirical study is carried out using the data concerning some stocks of various industries listed at the Shanghai Stock Exchange. A conclusion is reached that the investors are able to choose a portfolio more suitable to them under the VaR constraint.     

The double exponential jump diffusion model for pricing European options under fuzzy environments

The interest rate and volatility may have different values in the different commercial banks and financial institutions. Moreover, the fluctuations of the underlying assets are rare events, and there are not enough historical data to estimate the jump intensity in a precise sense. This paper considers European option pricing problems with the fuzzy interest rate, fuzzy drift, fuzzy volatility and fuzzy jump intensity. We present the fuzzy pricing formula of European options based on the Kou's double exponential jump diffusion model. We also obtain the crisp possibilistic mean option pricing formula in fuzzy double exponential jump diffusion model by using the crisp possibilistic mean values of the fuzzy numbers. Comparing with B-S formula, numerical analysis and empirical results show that the fuzzy double exponential jump diffusion formula and the crisp possibilistic mean option pricing formula are reasonable and can be taken as reference pricing tools for the financial investors.     

Average drawdown risk reduction and risk tolerances

We investigate the effects of average drawdown risk reduction on US mutual funds. Due to numerous evidence of the asymmetric distribution of portfolio returns, the asymmetric risk measures have extensively been used in risk management during the recent decades with extensive usages on the n-degree lower partial moment (LPM) methodology. Unlike the previous literature, we use the n-degree average drawdown risk measure, which is a special case of n-degree LPM, to empirically investigate the impacts of n-degree average drawdown risk reduction on the risk tolerances generated by the US mutual funds.The evidence shows that skewness does not impose any significant problem on the n-degree A-DRM model. Moreover, the effect of changing the tolerances of average drawdown risk in the n-degree A-DRM models is a reduction in the fund returns. The n-degree CA-DRM optimization model reduces investors׳ risk more than other models. Thus, the A-DRM can be accommodated with risk-averse investors׳ approach. The efficient set of mean–variance choices from the investment opportunity set, as described by Markowitz, shows that the n-degree CA-DRM algorithms create this set with lower risk than other algorithms. It implies that the mean–variance opportunity set generated by the n-degree CA-DRM creates lower risk for a given return than covariance and CLPM.     

面向产品创新要求的协同创新客户选择

现有协同创新客户选择方法主要从客户自身特点出发,未充分考虑产品创新要求的影响。鉴于此,提出了综合考虑产品创新要求和客户自身特点的协同创新客户选择方法,建立了产品创新要求和选择协同创新客户的指标之间的质量屋模型;基于模糊加权平均法和-截集确定评价指标权重,实现了指标权重随不同产品创新要求的动态调整;基于粗数方法提出了客户数量较多情况下确定客户在各指标上评价值的方法,计算客户评价值。案例分析结果表明,该方法具有合理性和可行性。     

Optimal portfolio selection with maximal risk adjusted return

We investigate the portfolio diversification problem by maximizing the risk adjusted return (RAR) of the underlying portfolio. The model in this article has two primary advantages over the original portfolio selection model with maximal RAR: (1) it considers the set of available assets containing any number of assets instead of only two assets, which is more reasonable in practical applications and (2) it incorporates the general linear constraint other than the simple budget constraint, which can deal with additional constraints for rational investors. An application including in-sample and out-of-sample tests is provided where the results illustrate that the portfolios selected by our method lead to considerable increases of RAR in comparison with those by the minimization of variance approach, and the outperformance persists using different sample frequencies.     

A multi-period portfolio selection optimization model by using interval analysis

Due to few historical data that can be obtained in an emerging securities market, the future returns, risk and liquidity of securities cannot be forecasted precisely. The investment environment is usually fuzzy and uncertain. To handle these imprecise data, this paper discusses a fuzzy multi-period portfolio optimization problem where the returns, risk, and liquidity of securities are represented by interval variables. By taking the return, risk, liquidity and diversification degree of portfolio into consideration, an interval multi-period portfolio selection optimization model is proposed with the objective of maximizing the terminal wealth under the constraints of the return, risk and diversification degree of portfolio at each period. In the proposed model, a proportion entropy is employed to measure the diversification degree of portfolio. Using the fuzzy decision-making theory and multi-objective programming approach, the proposed model is transformed into a crisp nonlinear programming. Then, we design an improved particle swarm optimization algorithm for solution. Finally, a numerical example is given to illustrate the application of our model and demonstrate the effectiveness of the designed algorithm.     

Economic benefits of using realized covariance forecasts in risk-based portfolios

This article examines the economic benefit of using the realized covariance matrix forecasts, for constructing the risk-based portfolios. We use the two-scale realized covariance estimator (TSC), the jump robust two-scale realized covariance estimator (RTSC) and the realized bipower covariance estimator (BPC), to forecast the daily realized covariance matrix. Using these covariance matrix forecasts, we implement three risk-based portfolios: the global minimum variance portfolio, the equal risk contribution portfolio and the most diversified portfolio. There is evidence that the portfolio performance improves by using TSC or RTSC estimators as compared to the daily-returns-based estimator. The performance gains are robust to the choice of risk-based portfolio strategy, the degree of investor’s relative risk-aversion, the market conditions and the choice of time intervals.     

Revealed preferences for portfolio selection – does skewness matter?

In this article, we consider the portfolio selection problem as a Bayesian decision problem. We compare the traditional mean–variance and mean–variance–skewness efficient portfolios. We develop bi-level programming problem to investigate the market’s preference for risk by using observed (market) weights. Numerical experiments are conducted on a portfolio formed by the 30 stocks in the Dow Jones Industrial Average. Numerical results show that the market’s preferences are better explained when skewness is included.     

Exploring GDP growth volatility spillovers across countries

This study takes a portfolio approach to investigate GDP growth volatility spillovers among 120 countries of the world during the 1960–2017 period. Based on the ratios of growth rate to standard deviation, we rank these countries in pools of high-, midsize-, and low-income growth. Using a spillover index that is based on variance decompositions under a vector autoregressive framework, we analyze the sources of growth volatility dynamics in the world in terms of volatility proportions from and to others. We find that high-income growth countries are net transmitters, while low-income growth countries are net recipients of growth volatility. We also find that it takes several years for low-income growth countries to absorb growth shocks of global nature. Overall, our analyses illustrate the importance of partnership in risk sharing as it is related to portfolio strategies and risk management.