Country Banking Crisis Prediction Using Transvariation Analysis

Xuejie Chen (), Asheber Abebe (), Kehao Zhang () and John S. Jahera ()
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Xuejie Chen: Department of Finance, Auburn University AL 36849, USA
Asheber Abebe: Department of Mathematics & Statistics, Auburn University AL 36849, USA
Kehao Zhang: Department of Computer Science, Auburn University AL 36849, USA
John S. Jahera: Department of Finance, Auburn University AL 36849, USA

Review of Pacific Basin Financial Markets and Policies (RPBFMP), 2015, vol. 18, issue 01, 1-21

Abstract: The main purpose of this paper is to investigate macroeconomic variables that are predictive of banking crisis. We focus on selecting variables that have high predictive power to discriminate between two groups of countries: the sound and the distressed. We consider a sample of 50 emerging market and developing countries during 1990–2005 time period, and apply generalized estimating equations as well as univariate, bivariate and trivariate transvariation analysis to choose the variables that have high predictive and discriminative powers to separate the distressed countries from the sound countries. In order to compare the predictive performance of these selected variables, we calculate the leave one out predictive error rate for the top ranking variables with low transvariation probability using discrimination procedures. Not surprisingly the results show that countries with better macroeconomic and monetary environments and healthier banking institutions are less likely to suffer systemic banking crisis.

Keywords: Banking crisis; generalized estimating equations; multivariate transvariation analysis; linear discriminant function (search for similar items in EconPapers)
JEL-codes: G1 G2 G3 (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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DOI: 10.1142/S0219091515500010

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