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Prepared by Martin Čihák and Petya Koeva Brooks. More details, as well as results of contingent claims analysis for the euro area are provided in Čihák and Koeva Brooks (2008).
A rough tool for distinguishing credit supply and demand factors are the bank lending surveys, organized by the Eurosystem since 2003, and summarizing responses of senior loan officers regarding loan demand and changes in their bank’s lending policy in the previous quarter. Practical problems in interpreting the results of the survey include the qualitative, subjective nature of the survey data, and the short time series available. Empirically, the survey results suggest that both the loan demand and the lending standards are procyclical (Čihák and Koeva Brooks, 2008), but the time series of lending surveys are too short to allow for a more elaborate analysis or to test for breaks in the correlations.
Data are from the BankScope database by Bureau van Dijk for 1997–2006. To explain the factors contributing to credit developments, the following variables are used: total bank assets, total loans, shareholders’ equity, short–term liabilities, long–term liabilities, liquid holdings (cash, ECB and other financial institutions’ securities, and government securities), equity price data (“last price,” daily), and equity shares outstanding (daily).
The distance to default (DD) is an increasingly popular measure of bank soundness. It is based on the valuation model of Black and Scholes (1973) and Merton (1974), who drew attention to the concept that corporate securities are contingent claims on the asset value of the issuing firm. The DD is calculated from market prices of bank shares and balance sheet data on individual banks obtained from the BankScope database.
Detailed results are available upon request.
Greenlaw and others (2008) use the Treasury–Eurodollar (TED) spread as another instrument for credit supply in the United States. As the difference between unsecured and government–backed deposit rates, the TED spread provides a useful measure of credit risk, which is likely to be correlated with credit supply. A weakness of the TED spread is that it may be influenced by “flight to quality” flows that move Treasury bill yields, as well as the funding pressures that drive LIBOR rates.
Cyprus, Malta, Luxembourg, and Slovenia are not included due to data limitations.
The basic story of the financial accelerator is that it is a mechanism linking the condition of borrower balance sheets to the terms of credit, and hence to the demand for capital. Corporate–sovereign bond spreads are a key measure of the credit terms.
Simultaneity may be an issue because the paper does not propose a structural VAR.