Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-745bb68f8f-f46jp Total loading time: 0 Render date: 2025-02-11T09:13:18.338Z Has data issue: false hasContentIssue false

28 - Systemic Risk EarlyWarning System: A Micro-Macro Prudential Synthesis

from PART IX - REGULATION

Published online by Cambridge University Press:  05 June 2013

By
Mikhail V. Oet ,
Ryan Eiben ,
Timothy Bianco ,
Dieter Gramlich ,
Stephen J. Ong  and
Jing Wang
Edited by
Jean-Pierre Fouque  and
Joseph A. Langsam
Mikhail V. Oet
Affiliation:
Usa
Ryan Eiben
Affiliation:
Indiana University
Timothy Bianco
Affiliation:
Usa
Dieter Gramlich
Affiliation:
Baden-Wuerttemberg Cooperative State University
Stephen J. Ong
Affiliation:
Usa
Jing Wang
Affiliation:
Cleveland State University
Jean-Pierre Fouque
Affiliation:
University of California, Santa Barbara
Joseph A. Langsam
Affiliation:
University of Maryland, College Park
Get access

Summary

Abstract From the financial supervisor's point of view, an early warning system involves an ex ante approach to regulation, targeted to predict and prevent crises. An efficient EWS allows timely ex ante policy action and can reduce the need for ex post regulation. This chapter builds on existing microprudential and macroprudential early warning systems (EWSs) to propose a hybrid class of models for systemic risk, incorporating the structural characteristics of the financial system and a feedback amplification mechanism. The models explain financial stress using data from the five largest bank holding companies, regressing institutional imbalances using an optimal lag method. The z-scores of institutional data are justified as explanatory imbalances. The models utilize both public and proprietary supervisory data. The Systemic Assessment of Financial Environment (SAFE) EWS monitors microprudential information from systemically important institutions to anticipate the buildup of macroeconomic stresses in the financial markets at large. To the supervisor, SAFE offers a toolkit of possible institutional actions that can be used to diffuse the buildup of systemic stress in the financial markets. A hazard inherent in all ex ante models is that the model's uncertainty may lead to wrong policy choices. To mitigate this risk, SAFE develops two modeling perspectives: a set of medium-term (six-quarter) forecasting specifications that gives policymakers enough time to take ex ante policy action, and a set of short-term (two-quarter) forecasting specifications for verification and adjustment of supervisory actions. Individual financial institutions may utilize the public version of SAFE EWS to enhance systemic risk stress testing and scenario analysis.

Type
Chapter
Information
Handbook on Systemic Risk , pp. 791 - 846
Publisher: Cambridge University Press
Print publication year: 2013

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Adrian, T., Brunnermeier, M.K. (2009). CoVaR. Federal Reserve Bank of New York Staff Report, No. 348, New York, September 2008 (updated: September 2011).
Bahmani-Oskooee, M., Brooks, T.J. (2003). A new criteria for selecting the optimum lags in Johansen's cointegration technique. Applied Economics 35 (8), 875–880.CrossRefGoogle Scholar
Borbély, D., Meier, C.-P. (2003). Macroeconomic interval forecasting: the case of assessing the risk of deflation in Germany. Kiel Working Papers 1153, Kiel Institute for the World Economy.Google Scholar
Bordo, M.D., Dueker, M., Wheelock, D. (2000). Aggregate price shocks and financial instability: an historical analysis. Federal Reserve Bank of St. Louis Working Paper 2000-005B, St. Louis.Google Scholar
Borio, C. (2003). Towards a macroprudential framework for financial supervision and regulation?Bank for International Settlements Working Paper, No. 128, Basel.Google Scholar
Borio, C., Drehmann, M. (2009). Assessing the risk of banking crises – revisited. Bank for International Settlements Quarterly Review, March, 29–46.Google Scholar
Borio, C., Lowe, P. (2002a). Asset prices, financial and monetary stability: exploring the nexus. BIS – Bank for International Settlements Working Paper, No. 114, Basel.Google Scholar
Borio, C., Lowe, P. (2002b). Assessing the risk of banking crises. Bank for International Settlements Quarterly Review, December, 43–54.Google Scholar
Borio, C., Kennedy, N., Prowse, S. (1994). Exploring aggregate asset price fluctuations across countries: measurement, determinants and monetary policy implications. BIS Economic Papers, No. 40, Basel.Google Scholar
Breusch, T.S., Pagan, A.R. (1979). Simple test for heteroscedasticity and random coefficient variation. Econometrica 47 (5) 1287–1294.CrossRefGoogle Scholar
Brooks, C. (2008). Introductory Econometrics for Finance. Cambridge University Press.CrossRefGoogle Scholar
Brown, R.L., Durbin, J., Evans, J.M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical SocietyB 37 149–163.Google Scholar
Callen, T. (1991). Estimates of private sector wealth. Report, Reserve Bank of Australia.
Chao, J.C., Phillips, P.C.B. (1999). Model selection in partially nonstationary vector autoregressive processes with reduced rank structure. Journal of Econometrics 91 227–271.CrossRefGoogle Scholar
Cogley, T. (1999). Should the Fed take deliberate steps to deflate asset price bubbles?Economic Review, Federal Reserve Bank of San Francisco, pp. 42–52.Google Scholar
Davies, G.W. (1977). A model of the urban residential land and housing markets. Canadian Journal of Economics 10 (3) 393–410.CrossRefGoogle Scholar
Demirgüç-Kunt, A., Detragiache, E. (1998). The determinants of banking crises in developing and developed countries. IMF Staff Papers, Vol. 45 (1998), No. 1, pp. 81–109.
Dueck, G., Scheuer, T. (1990). Threshold accepting: a general purpose optimization algorithm appearing superior to simulated annealing. Journal of Computational Physics 90 (1) 161–175.CrossRefGoogle Scholar
Edison, H. (2003). Do indicators of financial crises work? An evaluation of an early warning system. International Journal of Finance and Economics 8 (1) 11–53.CrossRefGoogle Scholar
Federal Reserve Board (2005). Supervision and regulation – statistical assessment of bank risk. An early warning model for banks. Washington.
Frye, J., Pelz, E. (2008). BankCaR (Bank Capital-at-Risk): a credit risk model for US commercial bank charge-offs. Working Paper WP 2008–03, Federal Reserve Bank of Chicago.
Furfine, C.H. (2003). Interbank exposures: quantifying the risk of contagion. Journal of Money, Credit and Banking 35 (1) 111–128.CrossRefGoogle Scholar
Gaytán, A., Johnson, C.A. (2002). A review of the literature on early warning systems for banking crises. Central Bank of Chile Working Paper, No. 183, Santiago.Google Scholar
Godfrey, L.G. (1978). Testing for multiplicative heteroskedasticity. Journal of Econometrics 8 (2) 227–236.CrossRefGoogle Scholar
Gramlich, D., Miller, G., Oet, M., Ong, S. (2010). Early warning systems for systemic banking risk: critical review and modeling implications. Banks and Bank Systems 5(2) 199–211.Google Scholar
Gramlich, D., Oet, M. (2011), The structural fragility of financial systems: Analysis and modeling implications for early warning systems. Journal of Risk Finance 12 (4) 270–290.CrossRefGoogle Scholar
Granger, C.W.J. (1969). Investigating causal relations by econometric models and crossspectral methods. Econometrica 37 (3) 424–438.CrossRefGoogle Scholar
Greenspan, A. (2008). The financial crisis and the role of federal regulators: Hearings before the committee of government oversight and reform. 111st Congress, October 23, 2008, testimony of Dr. Alan Greenspan.
Group of Ten (2001). Consolidation in the financial sector. Bank for International Settlements publication, Basel.
Gujarati, D.N. (2003). Basic Econometrics. 4th International ed. McGraw-Hill.Google Scholar
Hanschel, E., Monnin, P. (2005). Measuring and forecasting stress in the banking sector: evidence from Switzerland. BIS – Bank for International Settlements. Working Paper No. 22, Basel.Google Scholar
Hanssens, D.M., Liu, L.-M. (1983). Lag specification in rational distributed lag structural models. Journal of Business & Economic Statistics 1 (4) 316–325.Google Scholar
Hendricks, D., Kambhu, J., Mosser, P. (2007). Systemic risk and the financial system. Federal Reserve Bank of New York Economic Policy Review 13(2) 65–80.Google Scholar
Holmes, J.M., Hutton, P.A. (1992). A new test of money–income causality. Journal of Money, Credit and Banking 24 (3) 338–55.CrossRefGoogle Scholar
Honohan, P., Klingebiel, D. (2003). The fiscal cost implications of an accommodating approach to banking crises. Journal of Banking and Finance 27 (8) 1539–1560.CrossRefGoogle Scholar
Lee, T.-H., Yang, W.-P. (2006). Money–income Granger-causality in quantiles. Working paper, University of California, Riverside.Google Scholar
Illing, M., Liu, Y. (2003). An index of financial stress for Canada. Bank of Canada Working Paper, No. 2003–14, Ottawa.Google Scholar
Illing, M., Liu, Y. (2006). Measuring financial stress in a developed country: An application to Canada. Journal of Financial Stability 2 (4) 243–265.CrossRefGoogle Scholar
International Monetary Fund (2009). Global financial stability report 04/2009. Responding to the financial crisis and measuring systemic risks. Washington.
Jacobson, T. (1995). On the determination of lag order in vector autoregressions of cointegrated systems. Computational Statistics 10 177–192.Google Scholar
Jagtiani, J., Kolari, J., Lemieux, C., Shin, H. (2003). Early warning models for bank supervision: simpler could be better. Federal Reserve Bank of Chicago; Economic Perspectives 27 49–60.Google Scholar
Kaminsky, G.L., Lizondo, S., Reinhart, C.M. (1998). Leading indicators of currency crises. IMF Staff Paper 45 (1) 1–48.CrossRefGoogle Scholar
Kaminsky, G.L., Reinhart, C.M. (1999). The twin crises: the causes of banking and balance-of-payments problems. American Economic Review 89 (3) 473–500.CrossRefGoogle Scholar
Kindleberger, C.P. (1992). Bubbles. In The New Palgrave Dictionary of Money and Finance, Newman, P. et al., (eds). Macmillan.Google Scholar
King, T.B., Nuxoll, D.A., Yeager, T.J. (2006). Are the causes of bank distress changing? Can researchers keep up?Federal Reserve Bank of St. Louis Review 88 (1) 57–80.Google Scholar
Krishnamurthy, A. (2009). Amplification mechanisms in liquidity crises. Working paper, 45th Annual Conference on Bank Structure and Competition, April 2009, Federal Reserve Bank of Chicago.CrossRefGoogle Scholar
Kutner, M., Nachtsheim, C., Neter, J. (2004). Applied Linear Regression Models, 4th ed. McGraw-Hill–Irwin.Google Scholar
Maringer, D.Winker, P. (2004). Optimal lag structure selection in VEC-models. Computing in Economics and Finance 155 213–234.Google Scholar
Matz, L. (2007). Liquidity risk measurement. In Liquidity Risk Measurement and Management, Matz, L., Neu, P., (eds). Wiley.Google Scholar
Mishkin, F.S. (1992). The Economics of Money, Banking, and Financial Markets. 3rd ed. HarperCollins.Google Scholar
Moody's Investors Service (2001). Ratings Methodology: How Moody's Evaluates US Bank & Bank Holding Company Liquidity.
Moody's Investors Service (2002). Bank liquidity: Canadian Bank Case Study.
Moshirian, F., Wu, Q.-B. (2009). Banking industry volatility and banking crises. Journal of International Financial Markets, Institutions and Money 19 (2) 351–370.CrossRefGoogle Scholar
Murray, C.J., Papell, D.H. (2001). Testing for unit roots in panels in the presence of structural change with an application to OECD unemployment. In Nonstationary Panels, Panel Cointegration, and Dynamic Panels, B.H., Baltagi, T.B., Fomby, R. Carter, Hill (eds). Advances in Econometrics 15 223–238.Google Scholar
Müller, J. (2006). Interbank credit lines as a channel of contagion. Journal of Financial Services Research 29 (1) 37–60.CrossRefGoogle Scholar
O'Brien, R.M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality and Quantity 41 (5) 673–690.Google Scholar
Oet, M., Eiben, R. (2009). Financial Stress Index. Federal Reserve Bank of Cleveland Policy Discussion Paper, Cleveland.
Oet, M., Eiben, R., Bianco, T., Gramlich, D., Ong, S.J., (2011). The financial stress index: identification of systemic risk conditions. Federal Reserve Bank of Cleveland Working Paper No. 11–30 (October 28, 2011).Google Scholar
Phillips, P.C.B., Hansen, B.E. (1990). Statistical inference in instrumental variables regression with I(1) processes. The Review of Economic Studies 57 (1) 99–125.CrossRefGoogle Scholar
Pierce, J.L. (1996). Commercial bank liquidity. Board of Governors of the Federal Reserve System (ed.), Federal Reserve Bulletin.
Raffis, L.D. (2007). The net cash capital tool in bank liquidity management. In Liquidity Risk Measurement and Management, Matz, L., Neu, P. (eds). Wiley.Google Scholar
Ramsey, J.B. (1969). Tests for specification errors in classical linear least squares regression analysis. Journal of The Royal Statistical Society, B 31 (2) 350–371.Google Scholar
Reinhart, C., Rogoff, K. (2011). This Time Is Different: A Panoramic View of Eight Centuries of Financial Folly. Princeton University Press. Also, NBER Working Paper, No. 13882, March 2008.Google Scholar
Report of the Technical Committee of the International Organization of Securities Commissions (2002). Sound Practices for the Management of Liquidity Risk at Securities Firms.
Savin, N.E., White, K.J. (1977). The Durbin–Watson test for serial correlation with extreme sample sizes or many regressors. Econometrica 45 (8) 1989–1996.CrossRefGoogle Scholar
Sharp, D.C., Jeffress, P., Finnigan, S.M. (2003). A tool for selecting optimal variable time lags in regional forecasting model. Applied Research in Economic Development 1 (2) 3–11.Google Scholar
Shiller, R.J. (2008). How a bubble stayed under the radar. The New York Times, March 2, 2008.Google Scholar
Standard & Poor's (2008). S&P/Case–Shiller Home Price Indices Methodology.
Studenmund, A.H. (2006). Using Econometrics: a Practical Guide. 5th ed. Pearson Addison Wesley.Google Scholar
Thomson, J.B. (2009). On systemically important financial institutions and progressive systemic mitigation. Federal Reserve Bank of Cleveland Policy Discussion Paper, No. 27.
Vogelvang, B. (2005). Econometrics. Pearson Addison Wesley.Google Scholar
White, H. (1980). A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity. Econometrica 48 (4) 817–838.CrossRefGoogle Scholar
Winker, P. (1995). Identification of multivariate AR-models by threshold accepting. Computational Statistics and Data Analysis 20 295–307.CrossRefGoogle Scholar
Winker, P. (2000). Optimized multivariate lag structure selection. Computational Economics 16 87–103.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure no-reply@cambridge.org is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×