Abstract
Financial stress testing (FST) is a key technique for quantifying financial vulnerabilities; it is an important risk management tool. FST should ask which scenarios lead to big loss with a given level of plausibility. However, traditional FSTs are criticized firstly for the plausibility that rose against stress testing and secondly, for being conducted outside the context of an econometric risk model. Hence, the probability of a sever scenario outcome is unknown and many scenarios yet plausible possibilities are ignored. The aim of this paper is to propose a new FST framework for analyzing stress scenarios for financial economic stability. Based on worst case scenario optimization, our approach is able first to identify the stressful periods with transparent plausibility and second to develop a methodology for conducting FST in the context of any financial-economic risk model. Applied to Tunisian economic system data, our proposed framework identifies more harmful scenarios that are equally plausible leading to stress periods not detected by classical methods.