Descriptive Modeling of Systemic Banking Crises
The topic of the work is detection of connections between occurrences of systemic banking crises and values of socio-economic indicators in time frames of three years before outburst of crises. For this task we have used the list of banking crises in the period 1976-2007 prepared by the International Monetary Fund that we have connected with publically available Word Bank data. For the analysis a machine learning methodology based on subgroup discovery has been used. The main result is that demographic indicators have been detected as most relevant. At first place this is the indicator of percentage of total population that is in the age group 15-64 years. This indicator is present in both developed models and presents a condition related to a high number of crises outbursts. In the first model this indicator is connected with the indicator of annual percentage of money and quasi money growth while in the second model it is connected with the indicator of life expectancy for male population. For the analysis especially interesting is the second model because it includes decreasing or very low positive trend in active population in a period before the onset of the crises. The importance of the result is in the fact that such situations may be expected in the near future in many developed and developing economies.
KeywordsSystemic Banking Systemic Risk Negative Case Active Population Money Growth
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- 1.Demirguc-Kunt, A., Detragiache, E.: Cross-country empirical studies of systemic bank distress: A survey. World Bank Policy Research Working Paper No 3719 (2005), http://papers.ssrn.com/sol3/papers
- 5.Battiston, S., Glattfelder, J., Garlaschelli, D., Lillo, F., Caldarelli, G.: The Structure of Financial Networks. In: Network Science, pp. 131–163 (2010)Google Scholar
- 8.Battiston, S., Gatti, D., Gallegati, M., Greenwald, B., Stiglitz, J.: Default Cascades: When Does Risk Diversification Increase Stability?, ETH-RC-11-006, ETH Risk Center - Working Paper Series (2012)Google Scholar
- 9.Pokutta, S., Schmaltz, C., Stiller, S.: Measuring Systemic Risk and Contagion in Financial Networks (2011), Available at SSRN: http://dx.doi.org/10.2139/ssrn.1773089
- 10.De Nicolo, G., Lucchetta, M.: Systemic Risks and the Macroeconomy, IMF Working Paper WP/10/29, IMF, pp. 1–39 (2010)Google Scholar
- 11.Cerutti, E., Claessens, S., McGuire, P.: Systemic Risks in Global Banking: What Can Available Data Tell us and What More Data are Needed, BIS Working Papers, No376, pp. 1–22 (2012)Google Scholar
- 12.Kloesgen, W.: Explora: a multipattern and multistrategy assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271. AAAI/MIT Press (1996)Google Scholar
- 13.Wrobel, S.: An Algorithm for Multi-Relational Discovery of Subgroups. In: Antsaklis, P.J., Kohn, W., Nerode, A., Sastry, S.S. (eds.) HS 1996. LNCS (LNAI), vol. 1273, pp. 78–87. Springer, Heidelberg (1997)Google Scholar
- 16.Laeven, L., Valencia, F.: Systemic banking crises: A new database. International Monetary Fund Working Paper (2008), http://www.imf.org/external/pubs/ft/wp/2008/wp08224.pdf
- 18.Fuernkranz, J., Gamberger, D., Lavrač, N.: Foundations of Rule Learning. Springer (to be published, 2012)Google Scholar
- 19.Gamberger, D., Šmuc, T., Lavrač, N.: Subgroup discovery: on-line data mining server and its application. In: Proc. of Simulations in Biomedicine V, pp. 433–442 (2003)Google Scholar