AI helps you reading Science

AI generates interpretation videos

AI extracts and analyses the key points of the paper to generate videos automatically


pub
Go Generating

AI Traceability

AI parses the academic lineage of this thesis


Master Reading Tree
Generate MRT

AI Insight

AI extracts a summary of this paper


Weibo:
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

Cited by: 0|Views10
Full Text
Bibtex
Weibo

Abstract

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

Code:

Data:

0
Introduction
  • 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
Highlights
  • 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
Results
  • 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
Conclusion
  • 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
Tables
  • 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
Download tables as Excel
Study subjects and analysis
cases: 3
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

observations: 448
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

observations: 448
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

Reference
  • Acharya, V. V., J. A. C. Santos, and T. Yorulmazer. 2010. “Systemic Risk and Deposit Insurance Premiums.” Economic Policy Review (Federal Reserve Bank of New York) 16 (1): 89–98.
    Google ScholarLocate open access versionFindings
  • Acharya, V. V., and T. Yorulmazer. 2007. “Too Many to Fail— An Analysis of Time-Inconsistency in Bank Closure Policies.” Journal of Financial Intermediation 16 (1): 1–31.
    Google ScholarLocate open access versionFindings
  • Adrian, T., and M. Brunnermeier. 2008. “CoVaR.” Federal Reserve Bank of New York Staff Report No. 348.
    Google ScholarFindings
  • Allen, F., E. Carletti, and A. Babus. 2009. “Financial Crises: Theory and Evidence.” Annual Review of Financial Economics 1 (1): 97–116.
    Google ScholarLocate open access versionFindings
  • Allen, F., and D. Gale. 2000. “Financial Contagion.” Journal of Political Economy 108: 1–33.
    Google ScholarLocate open access versionFindings
  • Bernanke, B. 2009. “Financial Reform to Address Systemic Risk.” Speech at the Council on Foreign Relations, Washington, DC, March 10.
    Google ScholarLocate open access versionFindings
  • Chavez-Demoulin, V., A. C. Davison, and A. J. McNeil. 2005. “Estimating Value-at-Risk: A Point Process Approach.” Quantitative Finance 5 (2): 227–34.
    Google ScholarFindings
  • Cifuentes, R., H. S. Shin, and G. Ferrucci. 2005. “Liquidity Risk and Contagion.” Journal of the European Economic Association 3 (2–3): 556–66.
    Google ScholarLocate open access versionFindings
  • Dasgupta, A. 2004. “Financial Contagion through Capital Connections: A Model of the Origin and Spread of Bank Panics.” Journal of the European Economic Association 2 (6): 1049–84.
    Google ScholarLocate open access versionFindings
  • De Bandt, O., and P. Hartmann. 2001. “Systemic Risk in Banking: A Survey.” In Financial Crisis, Contagion, and the Lender of Last Resort: A Reader, ed. C. Goodhart and G. Illing, 249–98. Oxford University Press.
    Google ScholarFindings
  • De Haan, L., and A. Ferreira. 2006. Extreme Value Theory: An Introduction. Springer.
    Google ScholarFindings
  • De Vries, C. G. 2005. “The Simple Economics of Bank Fragility.” Journal of Banking and Finance 29 (4): 803–25.
    Google ScholarLocate open access versionFindings
  • Diebold, F. X., T. Schuermann, and J. D. Stroughair. 2000. “Pitfalls and Opportunities in the Use of Extreme Value Theory in Risk Management.” Journal of Risk Finance 1 (2): 30–35.
    Google ScholarLocate open access versionFindings
  • Diebold, F. X., and K. Yilmaz. 2009. “Measuring Financial Asset Return and Volatility Spillovers, with Application to Global Equity Markets.” Economic Journal 119 (534): 158–71.
    Google ScholarLocate open access versionFindings
  • Embrechts, P., C. Kluppelberg, and T. Mikosch. 1997. Modelling Extremal Events: for Insurance and Finance. Springer.
    Google ScholarFindings
  • Freixas, X., B. M. Parigi, and J.-C. Rochet. 2000. “Systemic Risk, Interbank Relations, and Liquidity Provision by the Central Bank.” Journal of Money, Credit, and Banking 32 (3): 611–38.
    Google ScholarLocate open access versionFindings
  • Hartmann, P., S. Straetmans, and C. G. de Vries. 2004. “Asset Market Linkages in Crisis Periods.” Review of Economics and Statistics 86 (1): 313–26.
    Google ScholarLocate open access versionFindings
  • ———. 2005. “Banking System Stability: A Cross-Atlantic Perspective.” NBER Working Paper No. 11698.
    Google ScholarFindings
  • International Monetary Fund. 2009. Global Financial Stability Report (April).
    Google ScholarFindings
  • Lagunoff, R., and S. L. Schreft. 2001. “A Model of Financial Fragility.” Journal of Economic Theory 99 (1): 220–64.
    Google ScholarLocate open access versionFindings
  • Poon, S., M. Rockinger, and J. Tawn. 2004. “Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications.” Review of Financial Studies 17 (2): 581–610.
    Google ScholarLocate open access versionFindings
  • Rajan, R. G. 2009. “Too Systemic to Fail: Consequences, Causes and Potential Remedies.” Written statement to the Senate Banking Committee Hearings, May 6.
    Google ScholarFindings
  • Segoviano, M., and C. Goodhart. 2009. “Banking Stability Measures.” IMF Working Paper No. 09/04.
    Google ScholarFindings
  • Stern, G. H., and R. J. Feldman. 2004. Too Big to Fail: The Hazards of Bank Bailouts. Brookings Institution Press.
    Google ScholarFindings
  • Zhou, C. 2008. On Extreme Value Statistics. PhD Thesis, Tinbergen Institute.
    Google ScholarFindings
Author
Your rating :
0

 

Tags
Comments
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn
小科