Depending on the availability of data, credit risk factors and their correlations with macrovariables can be estimated using data on loan performance (historical NPLs, default rates, recovery rates, loan-loss provisions (LLPs) or cost of credit) or using microdata on corporate sector from credit registries and eventually household sector data (Cihak, 2007). The approaches relying on aggregate loan performance data are the most often employed, given the scarcity and delays associated with the micro-data from borrowers’ financial statements.

Typically, the credit risk models include a measure of credit risk as dependent variable and macroeconomic variables (i.e., output measures, interest rates, inflation, and the exchange rate) as explanatory variables. When the estimations are carried out by conditioning on different sectors/industry specifics, sector- or industry-specific variables are considered. As for the methods of estimations, Foglia (2009) distinguishes among VARs for aggregate data, static and dynamic panel data for bank-level data, and logit and probit regressions to account for the non-linearity between macro variables and the default rates. We elaborate on estimation techniques applied by the CSEECBs in cases of NPLs, PDs and LDGs next.

# **_4.1.1. Mapping of Macroeconomic Variables to NPLs_**

One general approach well represented among CSEECB relies on estimation of the NPL regressions using either bank-specific data (Greece) or aggregated data (Croatia, Bosnia & Herzegovina, Montenegro, and Serbia). Greek Central Bank’s credit risk assessment is focused on the estimation of the NPLs in a panel data framework. The NPLs are related to macroeconomic variables (GDP growth, the unemployment rate and the real lending rate) and bank characteristics, such as performance ratios, total assets, the loans to deposits ratio, the solvency ratio, and the market power. This exercise is performed separately for corporate sector, mortgages and consumer loans.

When micro data is scarce, estimations using aggregated data are employed. The Croatian Central Bank links the evolution of the NPL ratio to GDP growth and changes in the exchange rate. The Central Banks of Albania, Serbia and Bosnia & Herzegovina adopt elasticities attached to macro variables (the GDP, the exchange rate, the risk premium, and the interest rate) from the IMF guidelines to forecast the bank-level ratio of NPLs to total loans. The Central Bank of Montenegro supplements the bottom-up stress test survey by an estimation of the NPL ratio on aggregated data, where the explanatory variables are the real GDP, real net earnings, CPI, real PPES, albeit the relatively short time series are rather prohibitive for getting reliable estimates.

The Central Banks of Poland and Slovenia use a substitute approach for estimating the NPL ratio, which involves estimating the loan loss provisions (LLPs).<sup>14</sup> The Central Bank

> 14 Foglia (2009) enumerates several works that use the LLP ratio to measure credit risk (among them Deutsche Bundesbank, 2006 and Swiss National Bank, 2006). At the same time, she point out the questionability of this measure given the fact that loan loss provisioning rules can vary across jurisdictions sometimes more than NPL (overdue loans) definition. In addition, the LLPs could be relatively more affected by the effort of banks to smooth income and dividend payouts to shareholders.

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