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Franziska Hünnekes

5 February 2024
WORKING PAPER SERIES - No. 2902
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Abstract
In this paper we build a unique dataset to study how banks decide which firms to lend to and how this decision depends on their own situation and the characteristics of their borrowers. We find that weaker capitalised banks adjust their credit standards more than healthier banks, especially for firms with a higher default risk. We also show how credit standards change in reaction to two specific macroeconomic developments, namely an increase in bank funding costs and a sudden deterioration in banks’ corporate loan portfolios. Here we find that weaker banks respond more forcefully by tightening their credit standards more than better capitalised banks. This development is particularly pronounced when banks are linked to riskier firms. Insofar, we provide evidence of heterogeneity in the bank lending channel, depending on the situation of the lenders and the borrowers.
JEL Code
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
12 January 2023
ECONOMIC BULLETIN - BOX
Economic Bulletin Issue 8, 2022
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Abstract
This box examines the information that the euro area bank lending survey (BLS) provides on future growth in loans to firms and households for house purchase in the euro area. The survey has proved to be invaluable for assessing the passthrough of monetary policy to borrowers via banks, for obtaining early information on turning points in lending conditions, and for understanding changes in loan demand and lending conditions during exceptional periods, such as the COVID-19 (coronavirus) pandemic and the Russian war in Ukraine. First, simple cross-correlations reveal a strong relation between BLS indicators and actual loan growth several quarters ahead. Second, BLS indicators help improve loan forecasts. In terms of loans to firms, the credit standards and loan demand reported in the BLS provide additional information that can be used when forecasting lending, while for housing loans, forecasts are improved by taking into account reported demand in particular. Finally, bank-level data confirm that BLS responses also reveal information on loan developments at the individual bank level. Overall, recent developments regarding BLS credit standards and loan demand point to a deceleration of growth in loans to firms and households in the coming quarters.
JEL Code
E4 : Macroeconomics and Monetary Economics→Money and Interest Rates
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E5 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit
E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
3 December 2021
WORKING PAPER SERIES - No. 2621
Details
Abstract
We assess how firm expectations about future production impact current production and pricing decisions. Our analysis is based on a large survey of firms in the German manufacturing sector. To identify the causal effect of expectations, we rely on the timing of survey responses and match firms with the same fundamentals but different views about the future. Firms that expect their production to increase (decrease) in the future are 15 percentage points more (less) likely to raise current production and prices, compared to firms that expect no change in production. In a second step, we show that expectations also matter even if they turn out to be incorrect. Lastly, we aggregate expectation errors across firms and find that they account for about 15 percent of aggregate fluctuations.
JEL Code
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
D84 : Microeconomics→Information, Knowledge, and Uncertainty→Expectations, Speculations
E71 : Macroeconomics and Monetary Economics