We develop in this paper a novel portfolio selection framework with a feature of dual robustness in both return distribution modeling and portfolio optimization. While predicting the return distributions of the future market always represents the most compelling challenge in investment, any...

We present a theoretical framework for studying how the cross holdings of credit asset securitization (CAS) products may affect systemic risk in banking. We demonstrate that cross holdings can be understood from the perspective of pursuing profit and credit creation; these motives drive up...

Diversification plays an important role in financial theory and lays the foundation for financial risk management. However, its role is greatly weakened when systemic risk events occur. In this paper, we study portfolio selection against systemic risk from the perspective of individual...

Certain characteristics are known for their predictability of cross-sectional expected stock returns. We propose a novel portfolio optimization procedure to incorporate multiple characteristic information, which requires minimum parameters and no stringent assumptions. Instead of investing in...

In this paper we generalize the primal--dual cone affine scaling algorithm of Sturm and Zhang to semidefinite programming.We show in this paper that the underlying ideas of the cone affine scaling algorithm can be naturely applied to semidefiniteprogramming, resulting in a new algorithm....

In this paper we discuss a locational model with a profit-maximizing objective. The model can be illustrated by the followingsituation. There is a set of potential customers in a given region. A firm enters the market and wants to sell a certainproduct to this set of customers. The location and...

In this paper we consider optimization problems defined by a quadraticobjective function and a finite number of quadratic inequality constraints.Given that the objective function is bounded over the feasible set, we presenta comprehensive study of the conditions under which the optimal solution...