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.     

Portfolio selection with transaction costs under expected shortfall constraints

An investor subject to proportional transaction costs allocates funds to multiple stocks and a bank account, to maximise the expected growth rate of the portfolio value under Expected Shortfall (ES) constraints. In a numerical example with ten time steps and one stock important innovations are caused by the introduction of the Expected Shortfall constraint: First, expected returns are reduced by less than one-tenth when the ES constraint is introduced. In comparison, economic capital as measured by ES, is reduced to amounts between one-half and three-quarters, when the ES constraint is introduced. Second, the dependence of expected return and ES on the initial portfolio, in particular when transaction costs are high, is largely removed by the introduction of the ES constraint.     

Intuitionistic weak arithmetic

We construct -framed Kripke models of i₁ and i₁ non of whose worlds satisfies xy(x=2yx=2y+1) and x,yzExp(x, y, z) respectively. This will enable us to show that i₁ does not prove ¬¬xy(x=2yx=2y+1) and i₁ does not prove ¬¬x, yzExp(x, y, z). Therefore, i₁¬¬lop and i₁¬¬i₁. We also prove that HAl₁ and present some remarks about i₂. Mathematics Subject Classification (2000):03F30, 03F55, 03H15.     

Takagi-Sugeno模糊模型的可辨识性问题

Takagi—Sugeno模糊模型广泛应用于对复杂系统的辨识,但可辨识性问题很少涉及。本文给出在规则数和规则前件确定的情况下,Takagi—Sugeno模糊模型的可辨识性条件。     

关于期权定价的一点注记

在介绍由随机波动源和异常波动源共同作用的波动源模型的基础上,对该模型进行了期权定价的研究,结果表明该模型只能比传统的对数正态分布模型更精确地描述现实市场中的股价波动规律而不能改善期权价值。     

Portfolio selection with probabilistic utility

Inspired by statistical physics, we present a probabilistic approach to portfolio selection. Instead of seeking the global extremum of some chosen utility function, we reinterpret the latter as a probability distribution of ‘optimal’ portfolios, and select the portfolio that is given by the mean value with respect to that distribution. Compared to the standard maximization of expected utility, this approach has several attractive features. First, it significantly reduces the excessive sensitivity to external parameters that often plague optimization procedures. Second, it mitigates the commonly observed concentration on too few assets; and third, it provides a natural and consistent way to account for the incompleteness of information and the aversion to uncertainty. Supportive empirical evidence is derived by using artificial data to simulate finite-sample behavior and out-of-sample performance.     

Randomly generating portfolio-selection covariance matrices with specified distributional characteristics

In portfolio selection, there is often the need for procedures to generate “realistic” covariance matrices for security returns, for example to test and benchmark optimization algorithms. For application in portfolio optimization, such a procedure should allow the entries in the matrices to have distributional characteristics which we would consider “realistic” for security returns. Deriving motivation from the fact that a covariance matrix can be viewed as stemming from a matrix of factor loadings, a procedure is developed for the random generation of covariance matrices (a) whose off-diagonal (covariance) entries possess a pre-specified expected value and standard deviation and (b) whose main diagonal (variance) entries possess a likely different pre-specified expected value and standard deviation. The paper concludes with a discussion about the futility one would likely encounter if one simply tried to invent a valid covariance matrix in the absence of a procedure such as in this paper.     

风险资产的最优保险

本文采用期望方差方法，引入无风险投资；建立多元风险模型，从投保人角度讨论了最优保险决策，分析了投资风险，无风险投资收益和保费政策等因素对最优决策的影响，为现代企业采取综合措施降低风险提供了理论依据．