A critical question that banking supervisors are trying to answer is what is the amount of capital or liquidity resources required by an institution in order to support the risks taken in the course of business. The financial crises of the last several years have revealed that traditional approaches such as regulatory capital ratios to be inadequate, giving rise to supervisory stress testing as a primary tool. A critical input into this process are macroeconomic scenarios that are provided by the prudential supervisors to institutions for exercises such as the Federal Reserve’s Comprehensive Capital Analysis and Review (“CCAR”) program. Additionally, supervisors are requiring that banks develop their own macroeconomic scenarios. A common approach is to combine management judgment with a statistical model, such as a Vector Autoregression (“VAR”), to exploit the dependency structure between both macroeconomic drivers, as well between modeling segments. However, it is well-known that linear models such as VAR are unable to explain the phenomenon of fat-tailed distributions that deviate from normality, an empirical fact that has been well documented in the empirical finance literature. We propose a challenger approach, widely used in the academic literature, but not commonly employed in practice, the Markov Switching VAR (“MS-VAR”) model. We empirically test these models using Federal Reserve Y-9 filing and macroeconomic data, gathered and released by the regulators for CCAR purposes, respectively. We find the MS-VAR model to be more conservative than the VAR model, and also to exhibit greater accuracy in model testing, as the latter model can better capture extreme events observed in history.
ISSN: 1792-6599 (Online)