Credit rating and preparing risk transition matrix for legal clients of banks with Markov chain approach

Document Type : Original Article

Authors

1 Department of Financial Management, Qom Branch, Islamic Azad University, Qom, Iran.

2 Department of Finance, Central Tehran Branch, Islamic Azad University, Tehran, Iran. (Modern Financial Risk Research Group)

3 Department of Accounting, Qom Branch, Islamic Azad University, Qom, Iran.

4 Department of Finance, Eslamshahr Branch, Islamic Azad University, Tehran, Iran. (Modern Financial Risk Research Group)

5 Department of Accounting, Qom branch, Islamic Azad University, Qom, Iran.

Abstract

Since most of the country's financial resources are in the banking industry and one of the main activities of the banking industry is to provide credit (facilities) to its customers, so many factors can lead to increased customer default in repaying credit received from the bank, which Lead to an increase in the credit risk of banks and ultimately the bankruptcy of the industry. The purpose of this article is to reduce the risk of default and prevent the bankruptcy of banks. Accurate credit rating leads to the facilities granted by the bank to its legal clients. The statistical population of the present study is active stock exchange companies that have used banking facilities. In this regard, using the Markov regime change model (MS), the factors affecting the probability of default of customers were estimated and the probability matrix of default was calculated. The results showed that the effect of debt-to-assets and debt-to-equity ratios on the probability of default was positive and significant, leading to an increase in the probability of default of companies. As can be seen, the shock of financial ratios in the two regimes did not have the same effects, indicating asymmetry.

Keywords


  • Ebrahimi, Babak and Mahmoudi, Rezvan (2016), Evaluation and Selection of the best Asset-Liability Combination of Iranian Banks, Tehran, Khatam University, Technical and Engineering Research Institute.
  • Daee Karimzadeh, Saeed (2016), The Optimal Combination of Participatory Facilities of Iranian Commercial Banks in Economic Sectors Using the Postmodern Theory of Investment Portfolio, Asset Management and Financing, 4 (4), 17-28.
  • Salehi, Fahimeh; Jafari Eskandari, Meysam and Salehi, Mojtaba (2014), Optimization of the Portfolio of Facilities Granted by Financial Institutions Using Mathematical Programming and Genetic Algorithm (Case study: Tejarat Bank), Monetary and Banking Management Development Quarterly, 2, 3, 1-22. 4. Mashhadiyan Maleki, Mohammad Reza, Souri, Ali, Ebrahimi, Mohsen, Mehrara, Mohsen, Majed, Vahid (2018), The Optimal Combination of Banks' assets in Response to Economic Conditions (Case study: Tejarat Bank). Iranian Journal of Applied Economic Studies, 9 (35), 155-173.
  • Mansouri, Ali and Azar, Adel (2002), Designing and explaining an Efficient Model to Allocate banking facilities, Neural Network Approach, Logistic and Linear regression, Computer Research Center of Islamic Sciences.
  • Mehrara, Mohsen and Sadeghian, Soghari (2008), Determining Optimal Loan Combination in Economic Sectors: (Case Study: Saman Bank), Financial Economics, 2, 5, 116-134.
  • Basel Committee on Banking Supervision, Principal for Management of Credit Risk September 2000.
  • Campbell, R. Harvey, John C. Liechty, Merril W. Liechty, and Peter Mueller. (2010). Portfolio Selection with Higher Moments, University of Texas M.D. Anderson Cancer Center, Houston, TX 77030, USA.
  • Edwin, J. Elton, Martin, J.Gruber, Stephen, J. Brown, William, N. Goetzmann. (2010). Modern Portfolio Theory and Investment Analysis. Business & Economics, 752 pages.
  • Jim, V. (2016). Business Cycle Based Portfolio Optimisation, Tilburg School of Economics and Management, Tilburg University, MSc Finance thesis November.
  • Korn, R. (1997). Optimal Portfolios: Stochastic Models for optimal investment and Risk management in continuous time,World Scientific,Singapore.
  • Marcucci, J., Quagliariello, M. (2006). “Is bank portfolio riskiness procyclical?, Evidence from Italy using a vector autoregression”, Journal of International Financial Markets, Institutions and Money, Volume 18, Issue 1, 46-63.
  • Pim Van Vliet, (2011). “Dynamic Strategic Asset Allocation: Risk and Return Across Economic Regimes”, SSRN Electronic Journal, Vol. 12, 360-375.
  • Saunders, A. and Allen, L (2002), Credit Risk Measurement. Second Edition, New York: John Wiley and Sons.
  • Siegel (1991). “Investment Portfolio Management Using the Business Cycle Approach”, Vilnius Gediminas Technical University, SaulÄ—tekio al. 11, LT-10223 Vilnius, Lithuania E-mails: 1 Audrius.Dzikevicius@vgtu.lt (corresponding author); 2 jarvet@gmail.com Received 27 July 2012; accepted 25 October 2012.
  • Treacy, William F; Carey Mark S. (1998), Credit risk rating at large U.S. banks, Federal Reserve Bulletin, Board of Governors of the Federal Reserve System (U.S).
  • Wilson T., (1997), Credit Risk Modeling: A New Approach, New York: Mc Kinsey Inc.
  • Zhang, Y., Li, X. & Guo, S. (2018). “Portfolio selection problems with Markowitz’s mean–variance framework: a review of literature”. Fuzzy Optim Decis Making, 17, 125-158.