Abstract
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.
Highlights
In this article, the authors consider the problem of assessing the probability of company default, which is relevant in the framework of the credit risk analysis
Most of the methods do not take into account the structure of the company, or its dynamics in the context of changing external factors, and these methods imply the presence of a large sample of data about similar companies
The behavior of macroeconomic variables which are external factors for the system dynamics model is described by a multivariate autoregressive moving average models (ARIMA)-Generalized models of autoregressive conditional heteroscedasticity (GARCH) model [4], used in econometrics to predict non-stationary time series
Summary
The authors consider the problem of assessing the probability of company default, which is relevant in the framework of the credit risk analysis.Currently, a significant number of mathematical methods for estimating the probability of a borrower's default have been developed, based on the analysis of the values of various quantitative and qualitative indicators of an enterprise [1]. Most of the methods do not take into account the structure of the company, or its dynamics in the context of changing external factors, and these methods imply the presence of a large sample of data about similar companies. This work demonstrates another approach to assess the probability of a company's bankruptcy, which is based on system dynamics model [2, 3], and eliminates the aforementioned drawbacks. Relations between elements are described by functional dependencies and differential equations that determine the company's dynamics, and the degree of its stability in relation to various macroeconomic scenarios.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
