The use of the system dynamics model to determine the probability of company defaultстатья
Информация о цитировании статьи получена из
Scopus
Статья опубликована в журнале из списка Web of Science и/или Scopus
Дата последнего поиска статьи во внешних источниках: 23 сентября 2021 г.
Авторы:
Kurennoy D.S. ,
Golembiovsky D.Yu
Журнал:
IOP Conference Series: Materials Science and Engineering
Том:
1047
Номер:
1
Год издания:
2021
Издательство:
-
Местоположение издательства:
Krakow
Первая страница:
012033
Последняя страница:
012033
Номер статьи:
012033
DOI:
10.1088/1757-899x/1047/1/012033
Аннотация:
This research demonstrates the approach of using the system dynamics model to assess the probability of company default that is a relevant problem in credit risk analysis. System dynamics offers models in which the reality is simulated structurally. According to the principles of system dynamics, the company is represented in the form of continuously interacting elements and external factors. Enterprise dynamics and the enterprise's resistance to various macroeconomic environments are determined by functional dependencies and differential equations that describe the links between the elements of the model. The behavior of random macroeconomic variables is described with a multivariate ARIMA-GARCH model, which is used in econometrics to predict non-stationary time series. The probability of company default is determined as a result of experiments with the obtained system dynamics model using the Monte Carlo simulation. The estimation of a default probability is the overall share of macroeconomic scenarios leading to the ruin of the enterprise. A comparative analysis of the obtained results and data from Moody's and Fitch demonstrates the closeness of the probability of company defaults obtained by simulation and corresponding estimates of rating agencies, which makes it possible to conclude that the considered approach is acceptable for estimating the probability of default of a borrower. © Published under licence by IOP Publishing Ltd.
Добавил в систему:
Голембиовский Дмитрий Юрьевич