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This paper considers three measures of the systemic importance of a financial institution within an interconnected financial system
Are Banks Too Big To Fail? Measuring Systemic Importance Of Financial Institutions
INTERNATIONAL JOURNAL OF CENTRAL BANKING, no. 4 (2010): 205-250
This paper considers three measures of the systemic importance of a financial institution within an interconnected financial system. The measures are applied to study the relation between the size of a financial institution and its systemic importance. Both the theoretical model and empirical analysis reveal that, when analyzing the syste...More
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- Authorities have an incentive to prevent the failure of a financial institution because such a failure would pose a significant risk to the financial system, and to the broader economy.
- A bailout is usually supported by the argument that a financial firm is “too big to fail”: that is, larger banks exhibit higher systemic importance.
- An equivalent question might be posed: does the size of a bank really matter for its systemic impact if it fails?
- The major difficulty in answering such a question is to design measures on the systemic importance of financial institutions.
- The authors use the estimated systemic importance measures and the size measures to empirically test the “too big to fail” statement
- Authorities have an incentive to prevent the failure of a financial institution because such a failure would pose a significant risk to the financial system, and to the broader economy
- We consider a moving window approach, which demonstrates the variation of the systemic importance measures across time
- The data set for constructing the systemic importance measures consists of daily equity returns of twenty-eight U.S banks listed on the New York Stock Exchange (NYSE) from 1987 to 2009.3
- This paper considers three measures of systemic importance of financial institutions in a financial system
- In the current empirical analysis our proposed systemic impact index (SII) measure is shown to be more informative than the PAO measure proposed by Segoviano and Goodhart (2009), we address one potential drawback of the SII measure: it is a simple counting measure that takes no account of the differences between potential losses when different financial institutions fail
- The authors apply the three proposed measures of systemic importance to an artificially constructed financial system consisting of twenty-eight U.S banks.
- From the test on correlation coefficients, the authors can empirically test whether larger banks exhibit larger systemic importance, thereby testing the “too big to fail” argument.
- The data set for constructing the systemic importance measures consists of daily equity returns of twenty-eight U.S banks listed on the New York Stock Exchange (NYSE) from 1987 to 2009.3.
- The data set for constructing the systemic importance measures consists of daily equity returns of twenty-eight U.S banks listed on the New York Stock Exchange (NYSE) from 1987 to 2009.3 The chosen banks are listed in table 1 with the descriptive statistics on their stock returns
- This paper considers three measures of systemic importance of financial institutions in a financial system.
- Since the authors regard the system as the combination of individual institutions, it is a multivariate, rather than bilateral, relation.
- The authors consider the PAO measure proposed by Segoviano and Goodhart (2009), as well as two new measures: the SII measure, which measures the size of the systemic impact if one End of 2009
- Table1: Descriptive Statistics on Daily Stock Returns of Twenty-Eight U.S Banks
- Table2: Descriptive Statistics on Yearly Size Measures of Twenty-Eight U.S Banks
- Table3: Estimated Systemic Importance Measures: Full Sample Analysis
- Table4: Correlation Coefficients: Full Sample Analysis
- Table5: Correlation Coefficients: Moving Window Analysis
- Table6: Systemic Importance Measures: Monthly Data
- Table7: Correlation Coefficients: Monthly Data
Study subjects and analysis
Then, similar to the two-bank case, (X1, X2, X3) follows a three-dimensional EVT setup. Instead of discussing all possible values on the parameters (γ, μ), we focus on three cases: γ is close to 1, γ = 1/2, and γ is close to 0. The results from comparing the SII measures are in the following theorem
Thirdly, with the moving window results on the systemic importance measures, we can get the end-of-year estimates on the systemic importance measures from 1994 to 2009 (sixteen years). We pool all of the bank-year estimates together, which results in 28 · 16 = 448 estimates for each systemic importance measure, and also 448 observations for each size measure. We then calculate the Pearson correlation coefficient for each pair and repeat the test on the significancy
The numbers in parentheses are the p-values for testing whether the correlation coefficient is significantly different from zero. The upper panel reports the results based on pooling all bank-year observations (448 observations). The middle and lower panels report the results based on pooling bank-year observations in two periods: 1994–2000 and 2001–09. ∗∗∗, ∗∗, and ∗ denote significance at the 1 percent, 5 percent, and 10 percent level, respectively
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