It is well known that there are asymmetric dependence structures between financial returns. This paper describes a portfolio selection method rooted in the classical mean–variance framework incorporates such dependency using nonparametric measure of local dependence, Gaussian correlation (LGC). shown optimization process for returns with straightforward covariance matrices. The new to outperfor...

A mathematical model of portfolio optimization is usually quantified with mean-risk models offering a lucid form of two criteria with possible trade-off analysis. In the classical Markowitz model the risk is measured by a variance, thus resulting in a quadratic programming model. Following Sharpe's work on linear approximation to the mean-variance model, many attempts have been made to lineariz...

The authors extend the well-known Hansen and Jagannathan (HJ) volatility bound. HJ characterize the lower bound on the volatility of any admissible stochastic discount factor (SDF) that prices correctly a set of primitive asset returns. The authors characterize this lower bound for any admissible SDF that prices correctly both primitive asset returns and quadratic payoffs of the same primitive ...

Two methods are frequently used for modeling the choice among uncertain outcomes: stochastic dominance and mean–risk approaches. The former is based on an axiomatic model of risk-averse preferences but does not provide a convenient computational recipe. The latter quantifies the problem in a lucid form of two criteria with possible tradeoff analysis, but cannot model all risk-averse preferences...

This paper will investigate the optimum portfolio for an investor, taking into account 5 criteria. The mean variance model of portfolio optimization that was introduced by Markowitz includes two objective functions; these two criteria, risk and return do not encompass all of the information about investment; information like annual dividends, S&P star ranking and return in later years which is ...

Abstract The global minimum variance portfolio (GMVP) is the starting point of Markowitz mean‐variance efficient frontier. estimation GMVP weights therefore much importance for financial investors. depend only on inverse covariance matrix returns risky assets, this reason estimated are subject to substantial risk, especially in high‐dimensional settings. In paper we review recent literature tra...

Solutions of portfolio optimization problems are often in ̄uenced by errors or misspeci®cations due to approximation, estimation and incomplete information. Selected methods for analysis of results obtained by solving stochastic programs are presented and their scope illustrated on generic examples ± the Markowitz model, a multiperiod bond portfolio management problem and a general strategic inv...

Fund managers highly prioritize selecting portfolios with a high Sharpe ratio. Traditionally, this task can be achieved by revising the objective function of the Markowitz mean-variance portfolio model and then resolving quadratic programming problems to obtain the maximum Sharpe ratio portfolio. This study presents a closed-form solution for the optimal Sharpe ratio portfolio by applying Cauch...