Stochastic optimization system for bank reverse stress testing

The recent evolution of prudential regulation establishes a new requirement for banks and supervisors to perform reverse stress test exercises in their risk assessment processes, aimed at detecting default or near-default scenarios. We propose a reverse stress test methodology based on a stochastic simulation optimization system. This methodology enables users to derive the critical combination of risk factors that, by triggering a preset key capital indicator threshold, causes the bank's default, thus detecting the set of assumptions that defines the reverse stress test scenario. This article presents a theoretical presentation of the approach, providing a general description of the stochastic framework and, for illustrative purposes, an example of the application of the proposed methodology to the Italian banking sector, in order to illustrate the possible advantages of the approach in a simplified framework, which highlights the basic functioning of the model. In the paper, we also show how to take into account some relevant risk factor interactions and second round effects such as liquidity-solvency interlinkage and modeling of Pillar 2 risks including interest rate risk, sovereign risk, and reputational risk. The reverse stress test technique presented is a practical and manageable risk assessment approach, suitable for both micro- and macro-prudential analysis.