The Austrian Central Bank’s SRM models historical default rates separately for ten industries. The equations include relevant macroeconomic variables and industry-specific explanatory variables.<sup>16</sup> The Czech Central Bank uses similar models for all loan portfolios considered. Namely, the dependent variable in these models is the twelvemonth default rate computed as the ratio of new bad loans over the initial loan portfolio, while the explanatory variables are: real GDP growth, real interest rates (one-month and one-year PRIBOR interbank rates), the real effective exchange rate, the nominal CZK/EUR and CZK/USD rates, and the level of indebtedness of the economy (the ratio of client loans to GDP). Hungarian, Romanian, and Slovak stress tests make use of different models for different loan portfolios, owing to the specificities of considered sectors and data availability. To estimate corporate PDs, the Hungarian Central Bank links the corporate bankruptcy rate to GDP growth, inflation, the nominal effective exchange rate, BUBOR, and EURIBOR in a VAR model. The Hungarian commercial real estate loans have been riskier than the average corporate loans, therefore their PDs are assumed to be twice as high as other corporate PDs. The Central Bank of Slovakia estimates the corporate default rates separately for three sectors distinguished according to their sensitivity to the credit cycle (sensitive, less-sensitive and non-sensitive sectors) using logit models. The explanatory variables in these models are the real GDP growth, inflation rate, 3M EURIBOR as endogenous variables, and the base ECB rate and Germany’s real GDP growth as exogenous variables. The Romanian Central Bank employs a more granular approach for the corporate loans using both micro-data for individual borrowers available from the Ministry of Finance and credit-by-credit data. First, financial performance of borrowers is predicted using scoring functions and a logit model linking corporate PDs with the macroeconomic variables. Further, it is assumed that the predicted negative change in a company’s financial performance signals a negative change in the expected performance of its debt; accordingly, the debt is then reclassified into a higher risk category. Based on this, LLPs are computed.

Regarding the household PDs, the Hungarian Central Bank estimates a hazard model using a panel of banks, in which the macro explanatory variables are the unemployment rate, the exchange rate, 3M BUBOR, and EURIBOR. For the unsecured household loans, PDs are obtained from the bottom-up stress-testing exercise. The estimation of the Slovak household default rates are based on aggregated data. The explanatory variables in this model comprise GDP growth, inflation rate, interbank interest rates, and the unemployment rate. The Romanian Central Bank used to base the assessment of household credit risk on the assumption that the household sector’s LLPs had the same growth rate as the corporate LLPs, given the lack of data on disposable income and the level of indebtedness. However, the recent crisis proved this assumption to be obsolete; there is work in progress to link the household LLPs to macroeconomic variables (the

> 16 ANB (2009) presents an update of the ANB’s credit model. This introduces a principal component analysis in order to exploit the information in a larger macroeconomic data set (24 Austrian macroeconomic variables).  In additional, the issue of potential nonlinearity in the relation between credit and business cycles is tackled through a threshold approach.

13