基于泰尔指数的盈利能力差异性测度研究

盈利能力是商业银行稳健性的重要保证。资产收益率(ROA)是衡量商业银行盈利能力的一个重要指标,但其无法直接衡量不同类型银行间盈利能力的差异性,也无法直接体现盈利能力差异不同影响因素的贡献度。基于此,本文的主要工作是在资产收益率的基础上,利用泰尔指数构造了单位资产收益率泰尔指数(ROUAT)与人均收益率泰尔指数(ROPPT)两个指标,用来测度商业银行盈利能力的差异性,并对中国13家上市商业银行2006~2015年的财务数据进行实证。实证结果表明:国有控股银行盈利能力的组内差异远大于股份制银行的组内差异,也大于国有控股银行和股份制银行的组间差异。通过对单位资产收益率泰尔指数(ROUAT)与人均收益率泰尔指数(ROPPT)对比,进一步发现人均收益率差异比单位资产收益率差异表现更为突出,说明现阶段银行的员工人数比银行的总资产对银行盈利能力的影响大。本文的主要贡献是利用泰尔指数和资产收益率构造了单位资产收益率泰尔指数和人均收益率泰尔指数两个指标,解决了不同分组下的商业银行的盈利能力差异性的测度问题,解决了差异性影响因素的贡献度问题,盈利能力差异性的测度为相关部门的政策制定提供参考。     

基于模糊收益率的投资组合模型构建及应用

投资组合理论是现代化投资管理活动的理论支持,广泛应用于证券投资领域.将模糊集理论与投资组合理论相结合,建立基于可能性理论和机会测度的投资组合模型,并用混合智能算法对模型求解.选取上证50指数成分股近两年的交易数据对模型进行实证分析.结果显示,模型构建的投资组合收益率优于经典模型收益率和上证50指数同期收益率,模型显著有效.     

基于可信性分布的投资组合问题的一种混合智能方法

研究了模糊环境下基于效用函数的有效资产投资组合的收益率模型,模型建立在可信性分布的基础上,而不是概率分布或可能性分布基础上.给出模糊环境下基于可信性分布的n种资产的最优投资组合问题的混合智能算法以寻找某种效用函数意义下的最优组合.并以实例仿真说明该方法的有效性.     

基于多分形波动率测度的股票市场条件收益率分布

多分形波动率MFV(multifractal volatility)是一种最近提出的金融市场波动率测度方法。以中国股票市场最具代表性股价指数(上证综指和深证成指)的代表性波动周期为例,通过运用描述性统计、QQ图和双变量分布散点图等方法,分别考察了非条件收益率、基于MFV的条件收益率和基于GARCH类波动率测度的条件收益率分布特征。研究结果显示,经过MFV标准化后的条件收益率分布较非条件收益率和基于GARCH类波动率测度的条件收益率更接近于正态分布,多分形波动率MFV对中国股票市场波动特征的刻画优于传统的GARCH类模型。     

资产收益率分形相关性下的Mean-PDCCA组合策略研究

准确地测量资产之间的相关性,是构建有效投资组合模型的前提.文章针对资产收益率存在分形相关性的现实情况,首先通过消除趋势交叉相关分析(DCCA)等方法,构建了分形相关性统计测度,用于测量资产之间的相关性;随后,通过将分形相关性统计测度纳入到收益-风险准则之中,构建了多时间标度前置下的投资组合模型Mean-PDCCA,即分形投资组合模型,并给出了模型的解析解;最后,实证分析发现,在资产收益率存在分形相关性的典型事实约束下,分形投资组合整体上优于经典投资组合,不仅能够提升投资业绩,还具有更好的稳健性,为投资者提供了有效的决策参考.     

随机利率与双指数跳跃扩散模型下几何平均水平重置期权定价

在资产收益率满足双指数跳跃扩散模型条件下,用Vasicek随机利率模型刻画市场利率的变动,并充分考虑市场利率对资产收益率的影响,研究了具有几何平均特征的水平重置期权定价问题.通过应用测度变换和多维Fourier逆变换方法,给出了此类重置期权定价的解析公式.最后,通过数值实例分析了模型参数对期权价格的影响.结果表明,上跳概率、跳跃频率和利率的长期平均水平对期权价格有正向影响,上跳和下跳幅度对期权价格有反向影响,而利率的均值回复速率对期权价格的影响会因为利率与资产收益率间的相关系数的影响而呈现出复杂性.     

基于Black-Litterman框架的资产配置策略研究

本文基于Black-Litterman框架提出了中国股票市场投资中行业间资产配置的策略。因为宏观经济指标对于股票收益率有一定的解释能力,本文通过宏观经济变量对收益率序列建模并且用GJR-GARCH模型捕捉资产收益率变化的特征,得出的预测资产收益率及其方差作为Black-Litterman框架下的输入。最后通过实证结果表明,基于这种策略构建的投资组合收益率在一定条件下会优于基于市场均衡权重或者传统Markowitz框架下的投资策略。     

Vasicěk随机利率模型下指数O-U过程的幂型期权鞅定价

研究了Vasicěk随机利率模型中一维标准Brown运动与资产价格服从指数Ornstein-Uhlenbeck过程中一维标准Brown运动的相关系数为ρ(-1≤ρ≤1)的情形下的幂型期权鞅定价问题.推广了基于Vasicěk随机利率模型下基于Black-Scholes公式的两种幂型期权定价问题.并利用Girsanov定理和等价鞅测度,给出了基于Vasicěk随机利率模型下服从指数Ornstein-Uhlenbeck过程的两种欧式幂型期权鞅定价公式.     

一种基于新的区间二型梯形模糊相似测度的多属性群决策方法

本文首先基于区间二型梯形模糊数的周长、面积、负指数距离提出了一种新的区间二型梯形模糊相似测度,讨论了其性质。其次,基于该相似测度公式分别构建了区间二型梯形模糊专家权重和属性权重确定模型,然后通过集结区间二型梯形模糊决策信息与权重信息,给出了一种基于该相似测度的群决策方法。最后,通过投资方案选择实例说明了该方法的合理性和有效性。     

广义Black-Scholes模型期权定价新方法--保险精算方法

利用公平保费原则和价格过程的实际概率测度推广了Mogens Bladt和Tina Hviid Rydberg的结果．在无中间红利和有中间红利两种情况下，把Black-Scholes模型推广到无风险资产(债券或银行存款)具有时间相依的利率和风险资产(股票)也具有时间相依的连续复利预期收益率和波动率的情况，在此情况下获得了欧式期权的精确定价公式以及买权与卖权之间的平价关系．给出了风险资产(股票)具有随机连续复利预期收益率和随机波动率的广义Black-Scholes模型的期权定价的一般方法．利用保险精算方法给出了股票价格遵循广义Ornstein-Uhlenback过程模型的欧式期权的精确定价公式和买权和卖权之间的平价关系．     

Portfolio selection based on upper and lower exponential possibility distributions

In this paper, two kinds of possibility distributions, namely, upper and lower possibility distributions are identified to reflect experts' knowledge in portfolio selection problems. Portfolio selection models based on these two kinds of distributions are formulated by quadratic programming problems. It can be said that a portfolio return based on the lower possibility distribution has smaller possibility spread than the one on the upper possibility distribution. In addition, a possibility risk can be defined as an interval given by the spreads of the portfolio returns from the upper and the lower possibility distributions to reflect the uncertainty in real investment problems. A numerical example of a portfolio selection problem is given to illustrate our proposed approaches.     

考虑背景风险的高阶矩三角模糊随机投资组合模型

在已有的大部分投资组合模型中,证券的收益服从随机分布或者模糊分布。然而,在实际的市场中存在大量的不确定性,市场不仅具有内在的风险,也存在由投资者个体差异产生的背景风险。本文推导随机模糊数的高阶矩性质,构建一个考虑背景风险的高矩三角模糊随机投资组合风险模型,采用沪深股市的数据分析背景风险对投资组合的影响。     

Use of DEA cross-efficiency evaluation in portfolio selection: An application to Korean stock market

We propose a way of using DEA cross-efficiency evaluation in portfolio selection. While cross efficiency is an approach developed for peer evaluation, we improve its use in portfolio selection. In addition to (average) cross-efficiency scores, we suggest to examine the variations of cross-efficiencies, and to incorporate two statistics of cross-efficiencies into the mean-variance formulation of portfolio selection. Two benefits are attained by our proposed approach. One is selection of portfolios well-diversified in terms of their performance on multiple evaluation criteria, and the other is alleviation of the so-called “ganging together” phenomenon of DEA cross-efficiency evaluation in portfolio selection. We apply the proposed approach to stock portfolio selection in the Korean stock market, and demonstrate that the proposed approach can be a promising tool for stock portfolio selection by showing that the selected portfolio yields higher risk-adjusted returns than other benchmark portfolios for a 9-year sample period from 2002 to 2011.     

A tail-revisited Markowitz mean-variance approach and a portfolio network centrality

A measure for portfolio risk management is proposed by extending the Markowitz mean-variance approach to include the left-hand tail effects of asset returns. Two risk dimensions are captured: asset covariance risk along risk in left-hand tail similarity and volatility. The key ingredient is an informative set on the left-hand tail distributions of asset returns obtained by an adaptive clustering procedure. This set allows a left tail similarity and left tail volatility to be defined, thereby providing a definition for the left-tail-covariance-like matrix. The convex combination of the two covariance matrices generates a “two-dimensional” risk that, when applied to portfolio selection, provides a measure of its systemic vulnerability due to the asset centrality. This is done by simply associating a suitable node-weighted network with the portfolio. Higher values of this risk indicate an asset allocation suffering from too much exposure to volatile assets whose return dynamics behave too similarly in left-hand tail distributions and/or co-movements, as well as being too connected to each other. Minimizing these combined risks reduces losses and increases profits, with a low variability in the profit and loss distribution. The portfolio selection compares favorably with some competing approaches. An empirical analysis is made using exchange traded fund prices over the period January 2006–February 2018.

     

Optimizing fuzzy portfolio selection problems by parametric quadratic programming

This paper develops a robust method to describe fuzzy returns by employing parametric possibility distributions. The parametric possibility distributions are obtained by equivalent value (EV) reduction methods. For common type-2 triangular and trapezoidal fuzzy variables, their reduced fuzzy variables are studied in the current development. The parametric possibility distributions of reduced fuzzy variables are first derived, then the second moment formulas for the reduced fuzzy variables are established. Taking the second moment as a new risk measure, the reward-risk and risk-reward models are developed to optimize fuzzy portfolio selection problems. The mathematical properties of the proposed optimization models are analyzed, including the analytical representations for the second moments of linear combinations of reduced fuzzy variables as well as the convexity of second moments with respect to decision vectors. On the basis of the analytical representations for the second moments, the reward-risk and risk-reward models can be turned into their equivalent parametric quadratic convex programming problems, which can be solved by conventional solution methods or general-purpose software. Finally, some numerical experiments are performed to demonstrate the new modeling ideas and the efficiency of solution method.     

Asset pricing and portfolio selection based on the multivariate extended skew-Student-<Emphasis Type="Italic">t</Emphasis> distribution

The returns on most financial assets exhibit kurtosis and many also have probability distributions that possess skewness as well. In this paper a general multivariate model for the probability distribution of assets returns, which incorporates both kurtosis and skewness, is described. It is based on the multivariate extended skew-Student-t distribution. Salient features of the distribution are described and these are applied to the task of asset pricing. The paper shows that the market model is non-linear in general and that the sensitivity of asset returns to return on the market portfolio is not the same as the conventional beta, although this measure does arise in special cases. It is shown that the variance of asset returns is time varying and depends on the squared deviation of market portfolio return from its location parameter. The first order conditions for portfolio selection are described. Expected utility maximisers will select portfolios from an efficient surface, which is an analogue of the familiar mean-variance frontier, and which may be implemented using quadratic programming.     

Mean-variance models for portfolio selection with fuzzy random returns

This paper develops two novel types of mean-variance models for portfolio selection problems, in which the security returns are assumed to be characterized by fuzzy random variables with known possibility and probability distributions. In the proposed models, we take the expected return of a portfolio as the investment return and the variance of the expected return of a portfolio as the investment risk. We assume that the security returns are triangular fuzzy random variables. To solve the proposed portfolio problems, this paper first presents the variance formulas for triangular fuzzy random variables. Then this paper applies the variance formulas to the proposed models so that the original portfolio problems can be reduced to nonlinear programming ones. Due to the reduced programming problems include standard normal distribution in the objective functions, we cannot employ the conventional solution methods to solve them. To overcome this difficulty, this paper employs genetic algorithm (GA) to solve them, and verify the obtained optimal solutions via Kuhn-Tucker (K-T) conditions. Finally, two numerical examples are presented to demonstrate the effectiveness of the proposed models and methods.     

Robust portfolio selection involving options under a “ marginal+joint ” ellipsoidal uncertainty set

In typical robust portfolio selection problems, one mainly finds portfolios with the worst-case return under a given uncertainty set, in which asset returns can be realized. A too large uncertainty set will lead to a too conservative robust portfolio. However, if the given uncertainty set is not large enough, the realized returns of resulting portfolios will be outside of the uncertainty set when an extreme event such as market crash or a large shock of asset returns occurs. The goal of this paper is to propose robust portfolio selection models under so-called “ marginal+joint” ellipsoidal uncertainty set and to test the performance of the proposed models. A robust portfolio selection model under a “marginal + joint” ellipsoidal uncertainty set is proposed at first. The model has the advantages of models under the separable uncertainty set and the joint ellipsoidal uncertainty set, and relaxes the requirements on the uncertainty set. Then, one more robust portfolio selection model with option protection is presented by combining options into the proposed robust portfolio selection model. Convex programming approximations with second-order cone and linear matrix inequalities constraints to both models are derived. The proposed robust portfolio selection model with options can hedge risks and generates robust portfolios with well wealth growth rate when an extreme event occurs. Tests on real data of the Chinese stock market and simulated options confirm the property of both the models. Test results show that (1) under the “ marginal+joint” uncertainty set, the wealth growth rate and diversification of robust portfolios generated from the first proposed robust portfolio model (without options) are better and greater than those generated from Goldfarb and Iyengar’s model, and (2) the robust portfolio selection model with options outperforms the robust portfolio selection model without options when some extreme event occurs.     

Mean-risk-skewness models for portfolio optimization based on uncertain measure

Numerous empirical studies show that portfolio returns are generally asymmetric. In this paper, skewness is considered to measure the asymmetry of portfolio returns and a mean-risk-skewness model for portfolio selection will be proposed in uncertain environment. Here, the returns of the securities are regarded as uncertain variables which are estimated by experienced experts instead of historical data. Furthermore, the corresponding variations and crisp forms of the model are considered. To solve the proposed optimization models, a hybrid intelligent algorithm is designed. Finally, the feasibility and necessity of the hybrid intelligent algorithm and the application of the proposed models are illustrated by two numerical examples.     

The lambda selections of parametric interval-valued fuzzy variables and their numerical characteristics

To model the uncertainty in the secondary possibility distributions, this paper develops a new method for handling interval-valued fuzzy variables with variable lower and upper possibility distributions. For a parametric interval-valued fuzzy variable, we define its lower selection variable, upper selection variable and lambda selection variable. The three selection variables are characterized by variable possibility distributions, and their numerical characteristics like expected values and n-th moments are important indices in practical optimization and decision-making problems. Under this consideration, we establish some useful analytical expressions of the expected values and n-th moments for the lambda selections of parametric interval-valued trapezoidal, normal and Erlang fuzzy variables. Furthermore, we focus on the arithmetic about the sums of common parametric interval-valued fuzzy variables. Finally, we apply the proposed optimization indices to a quantitative finance problem, where the second moment is used to measure the risk of a portfolio.