Purpose
– This paper aims to find the effects of regulatory and supervisory policies on bank risk-taking. The same regulation and supervision have different effects on bank risk-taking depending on influence factors. These factors were considered and a sample of the largest European banks from France, Germany, UK, Italy, Spain and Greece was used over the period 2005-2011.
Design/methodology/approach
– In this paper, the author analyses the effects of regulation and supervision on risk-taking. The author uses a sample of the biggest banks from six European countries (France, UK, Germany, Italy, Spain and Greece) over the period 2005-2011. Because the applicable entry of IFRS was in 2005, thus data of European banks are not available before this date. For each country in the sample, the 10 largest banks (defined by total assets) that lend money to firms were identified. The author does not include central banks or postal banks, which generally do not lend money to firms and are described as non-banking institutions (La Porta et al., 2002).
Findings
– It was found that restrictions on bank activities, supervisors’ power and capital adequacy decrease risk-taking. Thus, regulation and supervision enhance bank’s stability. While, deposit insurance increases the risk due to its association to moral hazard. Finally, it was found that strengthening regulatory and supervisory framework raises the risk-taking and weakens the stability of European banks.
Originality/value
– The author contributes to existing empirical analyses in three ways. First, the existing literature has drawn a lot of attention on US banks. However, the purpose of this paper is to examine the biggest banks of three European leaders (France, Germany and UK) and three more European countries influenced by the recent crisis (Spain, Italy and Greece) over the period 2005-2011. Second, most studies focus mainly on the relationship between regulation and profitability, yet seldom on the relationship between regulation, supervision and risk-taking. The author focuses on this relationship. Third, this study applies the two-step dynamic panel data approach suggested by Blundell and Bond (1998) and also uses dynamic panel generalized method of moments (GMM) method to address potential problems. The two-step GMM estimator that the author uses is generally the most efficient.
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