Digest Finance

Hierarchical Copulae in Credit Risk Modeling

Vol. 22, Iss. 3, SEPTEMBER 2017

PDF  Article PDF Version

Received: 28 April 2017

Received in revised form: 15 May 2017

Accepted: 9 June 2017

Available online: 21 September 2017


JEL Classification: С58, G17

Pages: 310-320


Kazakova K.A. Astrakhan State University, Astrakhan, Russian Federation

Knyazev A.G. Astrakhan State University, Astrakhan, Russian Federation

Lepekhin O.A. Astrakhan State University, Astrakhan, Russian Federation

Importance This research outlines an economic and mathematical model of the overdue loan debt. The model is based on copula functions allowing to simulate a non-Gaussian distribution of financial risks and credit risk, in particular.
Objectives The research models a joint distribution of overdue debt series in order to forecast the credit risk exposure. Relying upon the forecast, we intend to evaluate the efficiency of methods used to make provisions for possible losses and subsequently determine a reasonable approach to accruing the provision.
Methods We examine whether hierarchical copula models can be applied to build the joint distribution of overdue loan debt series in relation to banking institutions. It is considered as the basis for making further estimates of the overdue loan debt.
Results We build and evaluate a multivariate copula model of overdue loan debt with the hierarchical structure. Based on the modeled multivariate correlation, we forecast indicators of the overdue loan debt, which could be used as estimated provisions for credit losses. The estimated provisions turn to be sufficient for covering the real amount of overdue debt, being, in most cases, much less than that indicated in Regulation of the Central Bank of the Russian Federation № 254-П, On Rates of Provisions for Loan Losses.
Conclusions and Relevance The multivariate copula model of the overdue loan debt can underlie effective risk management systems in credit institutions.

Keywords: bank reserve, credit risk, overdue loan debt, copula-based model, forecast


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