Designing the overflow model of probability of financial helplessness in Iran's banking system with the approach of multivariable DCC-GARCH

اقتصاد پولی مالی

Article 6, Volume 29, Issue 24, September 2022, Pages 158-186   

Document Type: Original Article

DOI: 10.22067/mfe.2023.72762.1118

Authors

behrouz barzegar^{* 1}; nir feiz falahshams²; maryam khalili iraghi³; hashem nikoomaram⁴

¹1. PhD Student in Financial Management, Department of Financial Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

²Associate Professor, Department of Financial Management, Faculty of Management, Central Tehran Branch, Islamic Azad University, Tehran, Iran

³3. Assistant Professor, Department of Business Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

⁴4. Professor, Department of Accounting, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran

Abstract

Abstract
The current research has been designed to design the overflow model of probability of financial helplessness in Iran's banking system with the approach of multivariate GARCH models.
The statistical population of the includes the banks admitted to the Tehran Stock Exchange, which have been analyzed in the period of 2015 to 2019. To calculate them, time series data of banks' stock returns, equity value, book value of liabilities and daily value of assets have been used. The current research has investigated the probability of financial helplessness spillover to other banks by applying the KMV method and the concept of distance to default and by using the VAR model and the multivariate GARCH method (DCC-GARCH).
The results of the research have shown that there is a significant relationship between the financial helplessness risk of banks with each other; Mellat Bank is exposed to the highest risk of helplessness contagion and Parsian Bank shows the least effectiveness. Based on the results of the model, the increase in operational risks of banks, including credit risk and market risk, has a significant effect on increasing the risk of financial helplessness, and this risk can spread to other banks in the banks' communication network and then to the entire economy.

Keywords

financial helplessness; helplessness risk spread; credit risk; banking network; market risk

References

Ahadian, Azam., & Gurji, Mehsa. (2016). Explaining the Bankruptcy Forecasting Model to Identify Healthy and At-Risk Banks, Scientific-Research Quarterly of Asset Management and Financing, Fifth Year, Third Issue, Serial Number (81), Fall 2016, pp. 1-18

Bhattacharya, M.; Nkwoma, I. John., & Rebecca Valenzuela, M. (2020). Credit risk and financial integration: an application of network analysis. International Review of Financial Analysis. 72,101588

Bolo, Q., & Arabi, M. (2019). Identifying the Factors Affecting the Comprehensive Risk of State-Owned Banks, Bi-Quarterly Journal of Public Accounting, Fifth Year No. 2 (10 consecutive), 25-46. (in Persian)

Delnavaz, B. (2020). A Study of the Comprehensive Risk of Financial Helplessness among Car Supply Chain Companies in Tehran Stock Exchange, Fallah Shams, M. F., Faculty of Management and Economics, Islamic Azad University, Central Tehran Branch. (in Persian)

 Fatahi, Shahram.; Sohaili, Kiyomarth, & Dehghan Jabarabadi, Shahram. (2017). Investigating contagion in Iran's financial markets using a combination of Orenstein-Ollenbeck process and continuous wavelet transform, Econometric Modeling Quarterly, 2nd year, 4th issue (7 series), 54 -33.

Hafeez, U.; Zhuquan, W.; Muhammad, G. A.; Fan, Z.; Umeair, S., & Memon Rafait, M. (2021). Association of Financial Distress and Predicted Bankruptcy: The Case of Pakistani Banking Sector, Journal of Asian Finance, Economics and Business. 8(1): 573–585.

Jowita, G. (2020). Determinants of the bankruptcy risk of commercial banks in Central and Eastern Europe Prace Naukowe University to Economic znigo we Wroclawiureserch Papers of wroclaw university of economics and business 2020, 64(1) ISSN1899-3192 e-ISSN 2392-0041: 55-65.

Mahfuzur, R.; Cheong, Li Sa Md., & Masud, A. K. (2021). Predicting Firms’ Financial Distress: an Empirical Analysis Using the F-Score Model, Journal of Risk and Financial Management 14: 199. https://doi .org /10.3390/jrfm14050199: pp. 1-16

Mir Askari, Seyed Reza.; Renji, Fariborz., & Mousavinia, Seyed Morteza. (2019). Analysis of the impact of macroeconomic variables on the risk of helplessness of Iranian banks, Applied Economics Quarterly, Year 6, Number. 2, pp. 29-46.

Qi Fan, Xiu; Di Du, Meng., & Long, Wen. (2017). Risk Spillover Effect of Chinese Commercial Banks: Based on Indicator Method and CoVaR Approach, Procedia Computer Science, 122, 2017, 932–940.

 Shoja Shooshad, M.; Zomordian, G.; Pourzarandi, M. I., & Minooei, M. (2021). Study of the contagiousness of financial distress risk and credit risk in the banking system of the country, Investment Knowledge Research Quarterly, 313-339. (in Persian)

Wang, X. W.; Cao, Y. M., & Park, C. (2019). The relationships among community experience, community commitment, brand attitude, and purchase intention in social media. International Journal of Information Management, 49, 475-488

Zarrin, J.; Jamshidi Navid, B.; Ghanbari, M., & Constellation, A. (2020). Title of developing a model for predicting bankruptcy of state-owned companies using macroeconomic, managerial, financial and political variables, Journal of Public Accounting, Seventh Year, No. (1), 96-79. (in Persian)