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A comparison of methodologies in the stress testing of credit risk – alternative scenario and dependency constructs

1 Ph.D., CFA, Principal Director, Accenture Consulting, Finance and Risk Services Advisory/Models, Methodologies & Analytics, 1345 Avenue of the Americas, New York, USA
2 Non-Visiting Fellow, US Commodity Futures Trading Commission, 525 West Monroe Street Chicago, IL, 60611, USA

In the aftermath of the financial crisis of the last decade, banking supervisors have sought the solution to the problem of determining the optimal capital levels that an institution should hold, in order to support their risk taking activities. The experience of this financial downturn has given rise to the conclusion that traditional approaches, such as regulatory or economic capital are inadequate to this end, leading to the prevalence of supervisory stress testing as a primary tool of prudential supervision. A critical input into this process is the set of macroeconomic scenarios, either provided by the prudential supervisors, or developed by financial institutions. Prevalent among approaches in the industry is the combination of expert opinion and an econometric methodology, for example the Vector Autoregression (“VAR”) model that captures the dependency structure among and between macroeconomic explanatory variables and banking loss / income target variables. Despite the prevalence of this approach, we know from the previous finance literature that Gaussian VAR models are unable to cope with the empirical fact of deviation from normality. In this paper we investigate the alternative Markov Switching VAR (“MS-VAR”) model, featured more commonly in the academic realm as opposed to being applied in practice. We conduct an empirical experiment using data from regulatory filings and Federal Reserve macroeconomic data released by the regulators for mandated stress testing exercises. Our finding is that the MS-VAR model performs better than the VAR model, both in terms of producing severe scenarios conservative than the VAR model, as well as showing superior predictive accuracy. Furthermore, we find that the multiple equation VAR model outperforms the single equation autoregressive (“AR”) models according to various metrics across all modeling segments.
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