Abstract
This paper analyzes the role of banks’ regulatory capitalization in the transmission of monetary policy. We use a confidential dataset for Austrian banks spanning from the first quarter of 1997 to the fourth quarter of 2003. We find evidence that Austrian banks react in an asymmetric way to monetary policy depending on their regulatory excess capitalization, i.e. low capitalized banks react more restrictively to a monetary tightening than their highly capitalized peers.
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Notes
The same as for traditional macroeconomics is true for the modern microfounded approaches. Banks do not play any role in the modern workhorse new neoclassical synthesis (New Keynesian) models (e.g., Galí 2003). Only recently some authors have started to include the banking sector (e.g., Goodfriend and McCallum 2006).
For a description of the Austrian banking sector see the working paper version (Engler et al. 2005).
Vienna Interbank Offered Rate (VIBOR) until the adoption of the Euro, Euro Interbank Offered Rate (EURIBOR) afterwards.
Note, however, that the study does not allow for a comparison between countries in the euro area because of the confidentiality of national bank supervision data sets.
Engler et al. (2005).
In the dataset we cannot discriminate between loans with flexible and fixed interest rates.
For further details on the outlier correction see Engler et al. (2005).
As of the end of the year 2003.
VIBOR before 1999.
Over the observation period, the Austrian Shilling was pegged to the German Mark and consequently, the German monetary policy, as mirrored by the German interest rate, played a relevant role in Austria.
The period average was deducted to remove the time trend.
For a short review of the buffer theory and literature references see e.g. Heid et al. (2003).
In the calculation of the total effect of monetary policy (generally called long-term coefficient in the literature), the dynamic structure of the model has to be taken into account. The coefficient for the monetary policy indicator is calculated as follows: \( \sum\limits_{j = 0}^3 {\beta _j /(1 - \sum\limits_{j = 1}^8 {\alpha _j } } ) \). Other total effects are calculated in the same way.
When using the assets lagged by one period or alternatively, when omitting this variable entirely, the estimated coefficients are similar. In some specifications the estimation does however suffer from some higher order autocorrelation as a consequence, which may be due to jumps in the data caused by merger activity. Thus we choose the above equation that is assumed not to run into a simultaneity bias as the variable is instrumented and the alternative specifications deliver similar results.
The overall liquidity is defined as the sum of cash, short-term interbank deposits and government securities.
The index j for the shift dummy represents the number of mergers which is controlled for.
They are not shown in the table for brevity, but are available on request.
These numbers have been calculated as follows: estimated coefficient (4.35) * average capitalization of the 10% best capitalized banks (0.064) * 1% interest rate increase (0.01) = 0.003.
During the sample periods of the aforementioned studies, numbers for regulatory capital were not available yet.
Engler et al. (2005).
The banks’ size does not seem to play a role for credit cooperatives, which may be due to the network structure of this sector (see, e.g., Ehrmann and Worms 2004).
The VECM was identified by means of the Cholesky decomposition.
Ehrmann (2000) also uses a cointegration rank of order 2 to estimate the monetary rule for Austria, however, he uses only two lags for the endogenous variable.
See Ehrmann and Worms (2004) for Germany.
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Acknowledgements
We would like to thank the Oesterreichische Nationalbank (OeNB) for financial support and provision of the database. Special thanks to Eduard Hochreiter for making the project possible and to Sylvia Kaufmann for very valuable help and suggestions. We also thank Vanessa-Maria Redak and Ralf Dobringer for compiling the database. Furthermore we acknowledge valuable suggestions of Kai Carstensen (Kiel Institute for the World Economy), Michael Ehrmann (ECB), Helmut Herwartz (Christian-Albrechts University, Kiel), Skander van den Heuvel (The Wharton School), Stéphanie Stolz, Andreas Worms and Fred Ramb (Deutsche Bundesbank), participants of the Advanced Studies seminar, especially Renatas Kyzis, the Brown Bag Seminar (Kiel), the FindEcon conference (Lodz), the International Conference on Macroeconomics and International Finance (Rethymno), a seminar at FU Berlin and the Vosta workshop (OeNB) and two anonymous referees.
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Appendix
Appendix
1.1 Descriptive statistics
a) Changes in the monetary policy indicators (in %)
b) Distribution of loan growth
Note: Most of the loan growth density in around zero. The upward outliers are controlled for by the merger shift dummies.
c) Distribution of excess capital
Note: The excess capital is normalized around zero. The distribution is limited by the capital requirement regulation on the left hand side and by the overall balance sheet size on the right hand side.
d) Low and highly capitalized banks
Number of observations | Relative capitalization compared to average | Standard deviation | |
|---|---|---|---|
Below average capital | 10565 | −0.016 | 0.009 |
Above average capital | 7422 | +0.022 | 0.045 |
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Engler, P., Jokipii, T., Merkl, C. et al. The effect of capital requirement regulation on the transmission of monetary policy: evidence from Austria. Empirica 34, 411–425 (2007). https://doi.org/10.1007/s10663-007-9041-4
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DOI: https://doi.org/10.1007/s10663-007-9041-4