We present a robust optimization approach when the uncertainty in objective coefficients is described using multiple ranges for each coefficient. This setting arises when the value of the uncertain coefficients, such as cash flows, depends on an underlying random variable, such as the effectiveness of a new drug. Traditional robust optimization with a single range per coefficient would require ...

The growth-optimal portfolio is designed to have maximum expected log-return over the next rebalancing period. Thus, it can be computed with relative ease by solving a static optimization problem. The growthoptimal portfolio has sparked fascination among finance professionals and researchers because it can be shown to outperform any other portfolio with probability 1 in the long run. In the sho...

in this paper, a maximum likelihood estimation and a minimum entropy estimation for the expected value and variance of normal fuzzy variable are discussed within the framework of credibility theory. as an application, a credibilistic portfolio selection model is proposed, which is an improvement over the traditional models as it only needs the predicted values on the security returns instead of...

This paper considers the worst-case CVaR in situation where only partial information on the underlying probability distribution is given. It is shown that, like CVaR, worst-case CVaR remains a coherent risk measure. The minimization of worst-case CVaR under mixture distribution uncertainty, box uncertainty and ellipsoidal uncertainty are investigated. The application of worst-case CVaR to robus...

Market conditions change continuously. However, in portfolio investment strategies, it is hard to account for this intrinsic non-stationarity. In paper, we propose address issue by using the Inverse Covariance Clustering (ICC) method identify inherent market states and then integrate such into a dynamic optimization process. Extensive experiments across three different markets, NASDAQ, FTSE HS3...