Applying Scenario Optimization to Portfolio Credit Risk

Standard market risk optimization tools, based on assumptions of normality, are ineffective for credit risk. In this paper, we develop three scenario optimization models for portfolio credit risk. We first create the trade risk profile and find the best hedge position for a single asset or obligor. The second model adjusts all positions simultaneously to minimize the regret of the portfolio subject to general linear restrictions. Finally, a credit risk-return efficient frontier is constructed using parametric programming. While scenario optimization of quantile-based credit risk measures leads to problems that are not generally tractable, regret is a relevant and tractable measure that can be optimized using linear programming. We demonstrate the models on a portfolio of emerging market bonds.