Classical formulations of the portfolio optimization, such as mean-variance or Value-at-Risk（VaR） approaches, can result in a portfolio extremely sensitive to errors in the date, such as mean and covariance matrix. ...Classical formulations of the portfolio optimization, such as mean-variance or Value-at-Risk（VaR） approaches, can result in a portfolio extremely sensitive to errors in the date, such as mean and covariance matrix. In this paper we propose a way to alleviate this problem in a tractable manner. We proposed a robust portfolio optimization model, which can be solved by using linear matrix inequalities （LMI） .To substantiate the conclusion we cite an empirical analysis which adopts correlated data from Shanghai Stock Exchange. The result indicates that the robust model of portfolio is efficient and feasible.展开更多
文摘Classical formulations of the portfolio optimization, such as mean-variance or Value-at-Risk（VaR） approaches, can result in a portfolio extremely sensitive to errors in the date, such as mean and covariance matrix. In this paper we propose a way to alleviate this problem in a tractable manner. We proposed a robust portfolio optimization model, which can be solved by using linear matrix inequalities （LMI） .To substantiate the conclusion we cite an empirical analysis which adopts correlated data from Shanghai Stock Exchange. The result indicates that the robust model of portfolio is efficient and feasible.
