Lending to the corporate sector represents a significant part of the activities of the Russian banking sector. At the beginning of 2014 the volume of lending to non-financial organizations amounted to about 56% of the value of the loan portfolio and 39% of the value of Russian banks' assets. Meanwhile, the level of outstanding debt of the corporate loan portfolio tends to increase. A further increase in the share of corporate defaults in the portfolios of banks may cause instability in the banking sector and the financial system as a whole. A large proportion of lending in the Russian market represented lending construction companies. The crises of 2007-2009, 2015-2016 shows that companies in this industry is largely affected by macroeconom...
This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter i...
Due implementation of debtors’ financial solvency assessment models by Ukrainian banks with the aim ...
The paper concerns selection of input variab les for discriminant analysis and logistic regression a...
The paper has developed a set of evaluation models of the probability of corporate borrowers' defaul...
A direct method for calculating default rates by industry and target corporate segments is not possi...
The paper is aimed at comparing the divergence of existing credit risk models and creating a synergi...
The construction industry is highly sensitive on economic cycle. So, construction companies have exp...
The main goal of this paper is modeling credit risk of non-financial businesses entities by assessin...
One of the most important tasks in the risk management is the correct determination of probability o...
Abstract The recent bankruptcies of some construction companies have underlined the importance of de...
In recent years, supervisory bodies around the world have lost some of their confidence in the estim...
This paper analyzes how to appeal to the securitization of leasing companies affect the possible app...
Key words: financial ratios, bank failure prediction, cross-country model, linear probability model,...
Credit risk is the most important risk that financial institutions all around the world have to face...
Martin [4] was first who applied logit-model to forecast bank defaults at the period 1975-1976 in US...
This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter i...
Due implementation of debtors’ financial solvency assessment models by Ukrainian banks with the aim ...
The paper concerns selection of input variab les for discriminant analysis and logistic regression a...
The paper has developed a set of evaluation models of the probability of corporate borrowers' defaul...
A direct method for calculating default rates by industry and target corporate segments is not possi...
The paper is aimed at comparing the divergence of existing credit risk models and creating a synergi...
The construction industry is highly sensitive on economic cycle. So, construction companies have exp...
The main goal of this paper is modeling credit risk of non-financial businesses entities by assessin...
One of the most important tasks in the risk management is the correct determination of probability o...
Abstract The recent bankruptcies of some construction companies have underlined the importance of de...
In recent years, supervisory bodies around the world have lost some of their confidence in the estim...
This paper analyzes how to appeal to the securitization of leasing companies affect the possible app...
Key words: financial ratios, bank failure prediction, cross-country model, linear probability model,...
Credit risk is the most important risk that financial institutions all around the world have to face...
Martin [4] was first who applied logit-model to forecast bank defaults at the period 1975-1976 in US...
This paper is devoted to the estimation of the probability of default (PD) as a crucial parameter i...
Due implementation of debtors’ financial solvency assessment models by Ukrainian banks with the aim ...
The paper concerns selection of input variab les for discriminant analysis and logistic regression a...