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Innovational potential of small innovative enterprises established by institutes of higher education

Knyazev S.A. assistant lecturer of chair «The Management», Volgograd State University ( sergey-cknyazev@mail.ru )

Aparina O.P. assistant lecturer of chair «The Management», Volgograd State University ( ax_ap@mail.ru )

Journal: National interests: priorities and security, #31, 2011

An increase in the innovational potential of small innovative enterprises established by institutes of higher education is the topic considered in this article. Authors consider critical barriers hindering effective commercialization of innovations and suggest ways to solve this problem.


Influence of social stability on level of private investments

Gvozdaryova L.P. PhD in Economics, Associate Professor, the Department of Economics, the Astrakhan State Technical University ( gvlarisa21@rambler.ru )

Knyazev A.G. PhD in Physical and Mathematical Sciences, Head of the Department of Mathematics and Methodology, the Astrakhan State University ( agkniazev@mail.ru )

Makarov К.N. Senior Lecturer of the Department of Management, the Astrakhan State University ( makarovagu@gmail.com )

Journal: National interests: priorities and security, #42, 2013

Barrier to inflow of investments are considerable risks for investors, among which - corruption, crime and other social phenomena which aren't guaranteeing to investors of observance of the property rights. In the article it is offered for an assessment and improvement of investment climate to use the index of social stability measured by work of the average per capita income and size, the return to an inequality index. Statistical check of dependence of inflow of investments from level of social stability is given.


Hierarchical Copulae in Credit Risk Modeling

Kazakova K.A. Astrakhan State University, Astrakhan, Russian Federation ( kristinakazakova0309@gmail.com )

Knyazev A.G. Astrakhan State University, Astrakhan, Russian Federation ( agkniazev@mail.ru )

Lepekhin O.A. Astrakhan State University, Astrakhan, Russian Federation ( okmb07@yandex.ru )

Journal: Digest Finance, #3, 2017

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.


Hierarchical copula in credit risk modeling

Kazakova K.A. Astrakhan State University, Astrakhan, Russian Federation ( kristinakazakova0309@gmail.com )

Knyazev A.G. Astrakhan State University, Astrakhan, Russian Federation ( agkniazev@mail.ru )

Lepekhin O.A. Astrakhan State University, Astrakhan, Russian Federation ( okmb07@yandex.ru )

Journal: National interests: priorities and security, #6, 2017

Importance This research outlines an economic and mathematical model of the overdue loan debt. The model is based on copula functions allowing to simulate the non-Gaussian allocation of financial risks and credit risk, in particular.
Objectives The research models the joint allocation of overdue debt series in order to forecast the credit risk exposure. Relying upon the forecasting results, 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 allocation 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 indebtedness with the hierarchical structure. Based on the modeled multivariate correlation, we forecasted 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 that 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.


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