Modelling Probability of Default of Russian Banks and Companies Using Copula Models

Ilya Khankov and Henry Penikas
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Ilya Khankov: National Research University Higher School of Economics, Moscow

No 113, DEM Working Papers Series from University of Pavia, Department of Economics and Management

Abstract: Research is devoted to examination of the classifier, based on copula discriminant analysis (CODA). Performance of the classification of this algorithm was assessed. On samples, modelled with some typical features of corporate default data, sensitivity of the classifier was tested, to sample size, to default rate and to different patterns of variables’ interdependence. Alternative copula families’ selection method is proposed based on certain performance metric optimization. Difference in classification performance of different algorithms are investigated. On real data of Russian corporate defaults, CODA classifier was built. It was supported by single factor analysis, based on discriminant analysis too. Final model demonstrates better classification performance than Linear Discriminant Analysis and Random Forest algorithm, and is comparable to Quadratic Discriminant Analysis. Another experiment was set on data of Russian banks. Single factor analysis was assessed via standard procedure. CODA performance appeared to be lower than of Random Forest here, it was similar to QDA

Pages: 45 pages
Date: 2015-12
New Economics Papers: this item is included in nep-ban, nep-cis, nep-rmg and nep-tra
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Citations: View citations in EconPapers (2)

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