The Experts below are selected from a list of 321 Experts worldwide ranked by ideXlab platform
Sini Guo - One of the best experts on this subject based on the ideXlab platform.
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portfolio selection problems with markowitz s mean Variance framework a review of literature
Fuzzy Optimization and Decision Making, 2018Co-Authors: Yuanyuan Zhang, Sini GuoAbstract:Since the pioneering work of Harry Markowitz, mean---Variance portfolio selection Model has been widely used in both theoretical and empirical studies, which maximizes the investment return under certain risk level or minimizes the investment risk under certain return level. In this paper, we review several variations or generalizations that substantially improve the performance of Markowitz's mean---Variance Model, including dynamic portfolio optimization, portfolio optimization with practical factors, robust portfolio optimization and fuzzy portfolio optimization. The review provides a useful reference to handle portfolio selection problems for both researchers and practitioners. Some summaries about the current studies and future research directions are presented at the end of this paper.
Shouyang Wang - One of the best experts on this subject based on the ideXlab platform.
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continuous time portfolio selection with liability mean Variance Model and stochastic lq approach
Insurance Mathematics & Economics, 2008Co-Authors: Zhongfei Li, Shouyang WangAbstract:In this paper we formulate a continuous-time mean-Variance portfolio selection Model with multiple risky assets and one liability in an incomplete market. The risky assets' prices are governed by geometric Brownian motions while the liability evolves according to a Brownian motion with drift. The correlations between the risky assets and the liability are considered. The objective is to maximize the expected terminal wealth while minimizing the Variance of the terminal wealth. We derive explicitly the optimal dynamic strategy and the mean-Variance efficient frontier in closed forms by using the general stochastic linear-quadratic (LQ) control technique. Several special cases are discussed and a numerical example is also given.
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risk control over bankruptcy in dynamic portfolio selection a generalized mean Variance formulation
IEEE Transactions on Automatic Control, 2004Co-Authors: Shushang Zhu, Shouyang WangAbstract:For an investor to claim his wealth resulted from his multiperiod portfolio policy, he has to sustain a possibility of bankruptcy before reaching the end of an investment horizon. Risk control over bankruptcy is thus an indispensable ingredient of optimal dynamic portfolio selection. We propose in this note a generalized mean-Variance Model via which an optimal investment policy can be generated to help investors not only achieve an optimal return in the sense of a mean-Variance tradeoff, but also have a good risk control over bankruptcy. One key difficulty in solving the proposed generalized mean-Variance Model is the nonseparability in the associated stochastic control problem in the sense of dynamic programming. A solution scheme using embedding is developed in this note to overcome this difficulty and to obtain an analytical optimal portfolio policy.
Gordon B Hazen - One of the best experts on this subject based on the ideXlab platform.
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nonlinear utility Models arising from unModelled small world intercorrelations
Management Science, 1992Co-Authors: Robert F Bordley, Gordon B HazenAbstract:Savage's axioms show the rationality of maximizing expected utility when all uncertainties are explicitly Modelled. But individuals actually make decisions in bounded contexts called small worlds. Savage's axioms do not imply the optimality of maximizing expected utility in small worlds unless lotteries in different small worlds are probabilistically independent. Relaxing this independence assumption causes Savage's axioms to imply the optimality of maximizing a nonlinear utility Model which includes, as special cases, the Chew weighted linear utility Model, the Bell elation/disappointment Model and the Allais mean/Variance Model in utility-independent small worlds.
Yuanyuan Zhang - One of the best experts on this subject based on the ideXlab platform.
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portfolio selection problems with markowitz s mean Variance framework a review of literature
Fuzzy Optimization and Decision Making, 2018Co-Authors: Yuanyuan Zhang, Sini GuoAbstract:Since the pioneering work of Harry Markowitz, mean---Variance portfolio selection Model has been widely used in both theoretical and empirical studies, which maximizes the investment return under certain risk level or minimizes the investment risk under certain return level. In this paper, we review several variations or generalizations that substantially improve the performance of Markowitz's mean---Variance Model, including dynamic portfolio optimization, portfolio optimization with practical factors, robust portfolio optimization and fuzzy portfolio optimization. The review provides a useful reference to handle portfolio selection problems for both researchers and practitioners. Some summaries about the current studies and future research directions are presented at the end of this paper.
Zhongfeng Qin - One of the best experts on this subject based on the ideXlab platform.
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mean Variance Model for portfolio optimization problem in the simultaneous presence of random and uncertain returns
European Journal of Operational Research, 2015Co-Authors: Zhongfeng QinAbstract:The determination of security returns will be associated with the validity of the corresponding portfolio selection Models. The complexity of real financial market inevitably leads to diversity of types of security returns. For example, they are considered as random variables when available data are enough, or they are considered as uncertain variables when lack of data. This paper is devoted to solving such a hybrid portfolio selection problem in the simultaneous presence of random and uncertain returns. The Variances of portfolio returns are first given and proved based on uncertainty theory. Then the corresponding mean-Variance Models are introduced and the analytical solutions are obtained in the case with no more than two newly listed securities. In the general case, the proposed Models can be effectively solved by Matlab and a numerical experiment is illustrated.
