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Abstract
Using a case study approach, this chapter illustrates the process of developing a balance sheet stress testing model for a relatively large banking sector of regional importance. The chapter first discusses the importance of ensuring consistent data for stress testing and of choosing the appropriate econometric setup. Given the unique characteristics of the data set—short time period, large number of banks—the model applies the System Generalized Method of Moments estimator that also deals with so-called dynamic panel bias. The setup consists of credit risk models for projecting the impact of macroeconomic shocks on the delinquency ratios of loans to seven main economic sectors, as well as a satellite model for credit growth to determine the absolute increase in nonperforming loans (NPLs). On the basis of projections for additionally required provisions, preprovision net income and change in risk-weighted assets (RWA), the expected change in capital adequacy ratios is then calculated. The stress test results for the country at hand illustrate that severe shocks in the stress scenarios cause a considerable increase in NPL ratios, whereas the average capitalization ratio does not fall by much. This discrepancy is attributable to banks’ high preprovision net income absorbing the cost of additional loan losses and the relative inelasticity of RWA under the Basel I framework applied in this country.
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