The effect of financial and economic variables on the systemic risk warning system in Iran's financial market

Document Type : Original Article

Authors
Morteza Abbasi nehjoei 1
Yazdan Gudarzi Farahani 2
Seyedalireza Mirarab bayegi 3
1 Ph.D student in financial management, Rodhan Branch, Islamic Azad University, Roudehen, Iran.
2 Assistant Professor, Department of Islamic Economics, Faculty of Economic and Administrative Sciences, University of Qom, Qom, Iran.
3 Assistant Professor, Department of Financial Management, Roudhan Branch, Islamic Azad University, Roudehen, Iran.
10.30495/jik.2023.76020.4438
Abstract
Financial crises in Iran's economy and its financial markets, like many developing economies, although they differ in terms of intensity, size and period of stability, but the role of financial and economic variables on financial crises through instability in economic indicators and currency market disturbances. It was almost the same. The purpose of this paper is to investigate the impact of macroeconomic and financial variables such as exchange rate, inflation rate and interest rate in the early warning system of systemic risk in the financial market of Iran. In this study, a time-varying quantitative model approach (TVP-QVAR) was used in the period of 2011-2023. In order to model systemic risk, conditional value at risk method was used. In the designed model, the relationship between financial systemic risk index, changes in exchange rate, interest rate and inflation rate was investigated as the most important variables affecting systemic risk. The results obtained from the estimated model indicated that the variables of exchange rate, inflation rate and interest rate respectively had the highest effect on the systemic risk in the country's financial market. Financial crises in Iran's economy and its financial markets, like many developing economies, although they differ in terms of intensity, size and period of stability, but the role of financial and economic variables on financial crises through instability in economic indicators and currency market disturbances. It was almost the same.
Keywords
Warning model
systemic risk
financial markets
short-term long-term memory model
ابریشمی, حمید، مهرآرا، محسن و رحمانی، محمد (1398). اندازه‌گیری و تحلیل ریسک سیستمی در بخش بانکداری ایران و بررسی عوامل مؤثر بر آن. مدلسازی اقتصادسنجی، 4(3)، 11-36.
احمدی، محمدعلی، صالحی، اله کرم، نصیری، سعید و جرجرزاده، علیرضا (1401). بررسی تأثیر نا اطمینانی متغیرهای کلان اقتصادی بر سرعت تعدیل اهرم هدف. دانش سرمایه‌گذاری، 11(44)، 501-530.
اکبرموسوی، سید صالح، سلمانی، بهزاد، حقیقت، جعفر و اصغرپور، حسین (1401). پیش بینی بحران های بانکی: سیستم هشدار زودهنگام پویا. مدلسازی اقتصادسنجی، 7(1)، 9-38.
براتی، لیلا، فلاح شمس، میرفیض، غفاری، فرهاد و حیدرزاده هنزائی، علیرضا (1402). سنجش ریسک سیستمیک و تاثیر متغیرهای بنیادی برآن در سیستم بانکی کشور. دانش سرمایه‌گذاری، 12(48)، 721-744.
فدائی واحد، میثم، دهقان دهنوی، محمد علی، دیواندری, علی و امیری، میثم (1402). بررسی تأثیر شاخص‌های ریسک و رقابتی صنعت بانکداری بر شاخص رشد اقتصادی و تورم مصرف-کننده در اقتصاد ایران. دانش سرمایه‌گذاری، 12(48)، 1-20.
قلیزاده، علیرضا، فلاح شمس، میرفیض و افشار کاظمی، محمد علی. (1400). طراحی سیستم هشدار سریع وقوع بحران مالی در بورس اوراق بهادار تهران با رویکرد درخت تصمیم. دانش سرمایه‌گذاری، 10(40)، 35-55.
کلامی، محمد، سلمانی، بهزاد و اصغرپور، حسین (1398). بررسی عوامل مؤثر بر شاخص بحران ارزی تعدیل شده در ایران: رویکرد رگرسیون لاجیت. فصلنامه علمی پژوهشی اقتصاد مقداری، 16(4)، 43-67.
نمکی، علی، عباسیان، عزت اله و شفیعی، الهه (1401). تجزیه و تحلیل میزان ریسک سیستمی شرکت‌های بورس اوراق بهادار تهران با استفاده از رویکرد سیستم‌های پیچیده. راهبرد مدیریت مالی، 10(1)، 91-112.
Acharya, V. V., Pedersen, L. H., Philipson, T., & Richardson, M. (2017). Measuring systemic risk. The Review of Financial Studies, 30(1), 2–47.
Adrian, T. and Brunnermeier, M. K. (2016). “CoVaR”. American Economic Review 106(7): 1705-1741.
Ando, T., Greenwood-Nimmo, M., and Shin, Y. (2018). “Quantile Connectedness: modelling tail behaviour in the topology of financial networks”. Available at SSRN 3164772.
Di Persio, L., and Honchar, O. (2016). “Artificial neural networks architectures for stock price prediction: Comparisons and applications”. International Journal of Circuits, Systems and Signal Processing 10: 403–413.
Diebold, F. X., & Yılmaz, K. (2014). “On the network topology of variance decompositions: Measuring the connectedness of financial firms”. Journal of econometrics 182(1): 119-134.
Hochreiter, S., and Schmidhuber, J. (1997). “Long Short-term Memory”. Neural computation 9: 1735-1780.
Iturriaga, F. J. L., and Sanz, I. P. (2015). “Bankruptcy visualization and prediction using neural networks: A study of US commercial banks”. Expert Systems with Applications 42(6): 2857–2869.
Koop, G., Pesaran, M. H., & Potter, S. M. (1996). “Impulse response analysis in nonlinear multivariate models”. Journal of econometrics 74(1): 119-147.
Markose, S., Giansante, S., and Eterovic, N.A. (2021). “Early warning of systemic risk in global banking: eigen-pair R number for financial contagion and market price-based methods”. Annals of Operations Research. https://doi.org/10.1007/s10479-021-04120-1
Ouyang, Z., Xi-te, Y., and Yongzeng, L. (2021). “Systemic financial risk early warning of financial market in China using Attention-LSTM model”. North American Journal of Economics and Finance 56(2): 1-16.
Patro, D. K., Qi, M., and Sun, X. (2013). “A simple indicator of systemic risk”. Journal of Financial Stability 9(1): 105–116.
Pesaran, H. H., & Shin, Y. (1998). “Generalized impulse response analysis in linear multivariate models”. Economics letters 58(1): 17-29.
Shi, X., Zhourong, C., Hao, W., Dit-Yan, Y., Wai-kin, W., and Wang-chun, W. (2015). “Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting”. Proceedings of the 28th International Conference on Neural Information Processing Systems 802–810
Yang, Q., and Wang, C. Y. (2019). “A study on forecast of global stock indices based on deep LSTM neural network”. Statistical Research 36(03): 65–77.
Yu, L., Wang, S., and Lai, K. (2010). “A multiscale neural network learning paradigm for financial crisis forecasting”. Neurocomputing 73(4–6): 716–725.
Zhuang, Y., and Wei, H. (2023). “Early warning model and prevention of regional financial risk integrated into legal system”. PLoS ONE 18(6): e0286685. https://doi.org/10.1371/journal.pone.0286685.