Incorporating External Economic Scenarios into Your CCAR Stress Testing Routines

Since the financial crisis of 2008, banks and bank holding companies in the United States have faced increased regulation. One of the recent changes to these regulations is known as the Comprehensive Capital Analysis and Review (CCAR). At the core of these new regulations, specifically under the DoddFrank Wall Street Reform and Consumer Protection Act and the stress tests it mandates, are a series of “what-if” or “scenario analyses” requirements that involve a number of scenarios provided by the Federal Reserve. This paper proposes frequentist and Bayesian time series methods that solve this stress testing problem using a highly practical top-down approach. The paper focuses on the value of using univariate time series methods, as well as the methodology behind these models. INTRODUCTION The financial crisis of 2008 sparked a new wave of regulations for banks and bank holding companies (BHCs). One change was to augment the traditional economic capital modeling with macroeconomic scenarios as factors in simulations. These new regulations fall into two categories (Touryalai 2014): Comprehensive Capital Analysis and Review (CCAR) and the Dodd-Frank Act stress tests (DFAST). Adhering to these regulations requires a new method of forecasting the performance of banks and evaluating their ability to continue normal operations in a set of increasingly adverse economic scenarios (Hirtle 2012). This paper outlines two methods of incorporating these scenarios into projections of bank performance. The first method views the problem from a traditional, frequentist perspective. The second method uses a form of Bayesian model averaging to give the analysis tremendous power and intuition. For banks, economic capital and operational risk modeling is a very complicated and resource-intensive activity. Traditional modeling of risk, or more appropriately risk simulation, involves modeling the probability of default (PD) and the loss given default (LGD) for each asset (Wang 2013). This paper introduces a novel way to approximate similar outcomes by using a top-down approach that has substantially lower data requirements and is far less computationally expensive. With this approach, these methods of incorporating the required economic scenarios can be extended to much smaller banks and BHCs that do not currently have such disaggregated or pristine data. The models that are presented in this paper are specifically tailored to the problem of “too many” predictors in the context of estimating a model of losses. Three scenarios are provided by the Federal Reserve. BASELINE, ADVERSE, AND SEVERELY ADVERSE ECONOMIC SCENARIOS The most disruptive change to the existing process of oversight and compliance is the new requirement that external economic scenarios be built into economic stress tests. These scenarios, which are provided by the Federal Reserve (Fed), consist of 16 US macroeconomic indicators in three possible economic states: baseline, adverse, and severely adverse. Table 1 lists these macroeconomic indicators. The Fed recently expanded this list to include 12 additional international economic factors, though these are not part of the analysis in this paper.