Credit Risk Modeling Using Markov Switching Model

One of the most important causes of banking system problems is not paying enough attention to credit risk. Ignoring the creditworthiness of applicants and mandatory facilities has left banks with a significant amount of problem assets and a large number of non-current facilities and has severely reduced their ability to finance. Intelligent methods such as multivariate switching model, due to the behavior based on multiple analysis, make it possible to find appropriate answers to predict the amount of risk. The purpose of this study is to model the credit risk of some Iranian banks using the Markov-Switching method. In order to prevent potential losses of banks, it is important to study the variables that have a significant impact on creating high-risk and critical conditions. This can be modeled through the Markov dual-regime model, which is a useful way to describe the process by which variables escape in a series of states at a continuous time being measured. Therefore, in this paper, by analyzing independent variables such as micro and macroeconomic variables, financial factors, external shocks and financial distress index using multivariate switching method to identify, score and determine the impact of each of the independent variables in credit risk control and consequently credit risk forecast is considered in banks. In this regard, three hypotheses were determined and the annual data of the member banks of the Tehran Stock Exchange in the period 2011 to 2020 were used to test the hypothesis. The results of experiments in MATLAB software show the proper performance of risk prediction accuracy of the method based on multivariate Markov switching.