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Could Early Warning Systems Have Helped To Predict the Sub-Prime Crisis?

Published online by Cambridge University Press:  26 March 2020

E. Philip Davis  and
Dilruba Karim
E. Philip Davis*
Affiliation:
Brunel UniversityUxbridgeMiddlesexUB8 3PH NIES
Dilruba Karim*
Affiliation:
Brunel UniversityUxbridgeMiddlesexUB8 3PH
*
e-mail e_philip_davis@msn.com
e-mail dilruba.karim@brunel.ac.uk
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Abstract

One of the features of the sub-prime crisis, that began in August 2007, was its unexpected nature. It came as a surprise not only to most financial market participants but also in some degree to the policy community. In this context, we seek to assess whether early warning systems based on the logit and binomial tree approaches on the UK and US economies could have helped to warn about the crisis. We also consider a ‘check list approach’ of indicators based on history. Although not all of the complementary approaches are successful, we contend that our work suggests that a broadening of approaches to macroprudential analysis is appropriate.

Keywords

Sub-prime crisisearly warningfinancial instabilitymacroprudential analysisE44E58
Type
Articles
Information
National Institute Economic Review , Volume 206 , October 2008 , pp. 35 - 47
Copyright
Copyright © 2008 National Institute of Economic and Social Research

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References

Bank of England (2007), ‘Financial Stability Report, April 2007’, London, Bank of England.Google Scholar
Bank for International Settlements (2007), ‘77th Annual Report’, Basel, BIS.Google Scholar
Breiman, L., Friedman, J.H., Olshen, R.A. and Stone, J.C., (1984), ‘Classification and regression trees’, Wadsworth Inc, Monterey, California.Google Scholar
Congdon, P. (2003), Applied Bayesian Modelling, Wiley Series in Probability and Statistics, John Wiley and Sons Ltd.CrossRefGoogle Scholar
Davis, E.P. (1995), Debt, Financial Fragility and Systemic Risk, revised and expanded version, Oxford University Press.CrossRefGoogle Scholar
-(1999), “Financial data needs for macroprudential surveillance: what are the key indicators of risk to domestic financial stability?”, Lecture Series No 2, Centre for Central Banking Studies, Bank of England.Google Scholar
-(2002), ‘A typology of financial crises’, Financial Stability Review, No 2, Austrian National Bank.Google Scholar
Davis, E.P. and Karim, D. (2008), ‘Comparing early warning systems for banking crises’, Journal of Financial Stability, 4, 2, pp 89- 120.CrossRefGoogle Scholar
Demirgüc-Kunt, A. and Detragiache, E. (1998), ‘The determinants of banking crises in developed and developing countries’, IMF Staff Paper, 45, 1, Washington, International Monetary Fund.Google Scholar
-(2000), ‘Monitoring banking sector fragility: a multivariate logit approach’, World Bank Economic Review, Oxford University Press, 14(2), pp. 287–307, May.CrossRefGoogle Scholar
-(2005), ‘Cross-country empirical studies of systemic bank distress: a survey’, IMF Working Papers 05/96, International Monetary Fund.Google Scholar
Duttagupta, R. and Cashin, P. (2008), ‘The anatomy of banking crises’, IMF Working paper series, No. 08/93, Washington, IMF.CrossRefGoogle Scholar
ECB (2007), ‘Financial Stability Review, June 2007’, Frankfurt, European Central Bank.Google Scholar
Ghosh, S. and Ghosh, A.R. (2002), ‘Structural vulnerabilities and currency crises’, IMF Working Paper No. 02/9, International Monetary Fund.CrossRefGoogle Scholar
IMF (2007), ‘Global Financial Stability Report, April 2007’, Washington DC, International Monetary Fund.Google Scholar
Kaminsky, L.G. and Reinhart, C.M. (1999), ‘The twin crises; the causes of banking and balance of payments problems’, American Economic Review, 89, 3, pp. 473–500.CrossRefGoogle Scholar
Karim, D. (2008a), ‘The optimal threshold for banking crisis prevention and the consequences of regulatory forbearance’, Brunel University Working Paper, 08-19, Department of Economics and Finance, Brunel University.Google Scholar
-(2008b), ‘The use of binary recursive trees for banking crisis prediction’, Brunel University Department of Economics and Finance Working Paper, Brunel University.Google Scholar
Katz, M. (2006), Multivariable Analysis: A Practical Guide for Clinicians, New York, Churchill-Livingstone.CrossRefGoogle Scholar
Manasse, P., Roubini, N. and Schimmelpfennig, A. (2003), ‘Predicting sovereign debt crises’, IMF Working Paper No. 03/221, International Monetary Fund.Google Scholar
Steinberg, D. and Colla, P. (1995), CART: Tree Structured Non-Parametric Data Analysis, Salford Systems, San Diego, California.Google Scholar
Steinberg, D. and Golovnya, M. (2007), CART 6.0 User's Guide, Salford Systems, San Diego, California.Google Scholar