Dynamic Behavior Analysis and Measurement of Financial Market Crash Rate in Iran
Keywords:
Dynamic Behaviors, Financial Market Crash, Risk - Capital Market, Price FluctuationsAbstract
Objective: The purpose of the present study is to analyze dynamic behaviors and estimate the financial market crash rate in Iran using the Black–Scholes, Heston, conditional crash rate, and escape velocity models to explain the dynamics of market fluctuations and forecast crash risk.
Methodology: This research is applied in purpose and descriptive–analytical in nature, falling within the category of post-event studies. The data include the free-float stock index, cash return index, top 50 active firms index, industrial index, and financial index of Iran’s capital market over a ten-year period. Initially, the intrinsic values of the indices were calculated using the Black–Scholes and Heston asset pricing models to identify overvaluation or undervaluation conditions. The outputs of these models were then used as inputs for crash models, including the Maximum Crash Model, Conditional Crash Rate Model, and Escape Velocity Model. Statistical tests such as the unit root tests (ADF and PP), heteroscedasticity test (ARCH), dynamic quantile regression, and conditional convergence, model length, and loss function tests were applied to assess model stability and accuracy.
Findings: The results indicated that the return data of all indices were stationary at the 99% confidence level. Both the multivariate GARCH(1,1) Black–Scholes model and the Heston model demonstrated significant performance in estimating the intrinsic value of indices. The free-float stock index and the top 50 active firms index played a moderating role in market volatility and crash risk, whereas the cash return index served as a predictive variable and the financial index acted as an accelerator of market crashes. The Escape Velocity Model exhibited higher accuracy than the Maximum Crash Model in predicting market crash rates under crisis conditions. Stability and convergence tests also confirmed the predictive validity of the combined model at a significance level below 5%.
Conclusion: The findings reveal that integrating classical asset pricing models with dynamic and nonlinear crash models provides an effective framework for analyzing and forecasting market fluctuations and crash risk in Iran’s capital market. These models can serve as practical tools for investors, regulatory bodies, and economic policymakers to enhance market stability and efficiency.
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References
Azadi, K., Aziz Mohammadi, H., Tasdikari, M. J., & Khedmatgozar, H. (2021). The Effect of Financial Statement Readability on Stock Price Crash Risk and Shareholder Behavior. Scientific Journal of Financial Accounting Knowledge, 8(1), 121-144. https://jfak.journals.ikiu.ac.ir/article_2376.html
Darabi, M., Zomorodian, H., Bani Mahd, M., & Fallah Shams, F. (2024). Estimating the Rate of Stock Market Crash in Iran with Emphasis on the Dynamic Behaviors of Free Float Stock Index and Cash Returns. Journal of Financial Management and Investment, 15(2), 55-79. https://jafci.com/index.php/jafci/article/view/112?articlesBySimilarityPage=2
Di Giuli, A., & Laux, P. A. (2022). The effect of media-linked directors on financing and external governance. Journal of Financial Economics, 145(2), 103-131. https://doi.org/10.1016/j.jfineco.2021.07.017
Du, M. (2023). Locked-in at home: The gender difference in analyst forecasts after the COVID-19 school closures. Journal of Accounting and Economics, 76(1), Article 101603. https://doi.org/10.1016/j.jacceco.2023.101603
Hanelt, A., Firk, S., Hildebrandt, B., & Kolbe, L. M. (2021). Digital M&A, digital innovation, and firm performance: An empirical investigation. European Journal of Information Systems, 30(1), 3-26. https://doi.org/10.1080/0960085X.2020.1747365
He, B., Chen, H., Li, Z., & Zhou, X. (2023). Information and communication technology and innovation performance of firms: Evidence from Chinese listed state-owned enterprises. International Review of Economics & Finance, 88, 47-59. https://doi.org/10.1016/j.iref.2023.06.007
Huang, Q., Fang, J., Xue, X., & Gao, H. (2023). Does digital innovation cause better ESG performance? An empirical test of a-listed firms in China. Research in International Business and Finance, 66, Article 102049. https://doi.org/10.1016/j.ribaf.2023.102049
Jiang, K., Du, X., & Chen, Z. (2022). Firms' digitalization and stock price crash risk. International Review of Financial Analysis, 82, Article 102196. https://doi.org/10.1016/j.irfa.2022.102196
Khajavi, M., & Zare, H. (2021). The Impact of Audit Quality on Stock Price Crash Risk in the Tehran Stock Exchange. Journal of Accounting Research, 7(2), 99-120. https://www.sid.ir/FileServer/SF/3971395H04228
Kim, J. B., Tseng, K., Wang, J. J., & Xi, Y. (2024). Policy uncertainty, bad news disclosure, and stock price crash risk. Journal of Empirical Finance, 78, Article 101512. https://doi.org/10.1016/j.jempfin.2024.101512
Li, D., Chen, Y., & Miao, J. (2022). Does ICT create a new driving force for manufacturing?-Evidence from Chinese manufacturing firms. Telecommunications Policy, 46(1), Article 102229. https://doi.org/10.1016/j.telpol.2021.102229
Li, H., Lu, L., Lin, Z., & Meng, T. (2024). Digital innovation and corporate social responsibility performance: Evidence from firms' digital patents. Technological Forecasting and Social Change, 207, Article 123626. https://doi.org/10.1016/j.techfore.2024.123626
Liu, Y., Dong, J., Mei, L., & Shen, R. (2023). Digital innovation and performance of manufacturing firms: An affordance perspective. Technovation, 119, Article 102458. https://doi.org/10.1016/j.technovation.2022.102458
Liu, Z., Zhou, J., & Li, J. (2023). How do family firms respond strategically to the digital transformation trend: Disclosing symbolic cues or making substantive changes? Journal of Business Research, 155, Article 113395. https://doi.org/10.1016/j.jbusres.2022.113395
Mirzaei, A., Zamani, F., & Kavyani, Z. (2024). The Relationship Between Ownership Structure Characteristics and Stock Price Crash Risk in Companies Listed on Tehran Stock Exchange. Journal of Management Accounting, 13(2), 65-85. https://civilica.com/doc/2122573/
Richardson, G., Taylor, G., & Hasan, M. (2024). Income-shifting arrangements of US multinational corporations and future stock price crash risk. Journal of Accounting Literature. https://doi.org/10.1108/JAL-12-2023-0214
Salehi, N., Mohammadi, R., & Karimi, M. (2023). Examining the Impact of Financial Constraints on Stock Price Crash Risk of Companies Listed in Tehran Stock Exchange. Journal of Economics and Capital Market Management, 12(4), 87-108. https://civilica.com/doc/2083134/
Wang, Q., & Qiu, M. (2023). Minority shareholders' activism and stock price crash risk: Evidence from China. International Review of Financial Analysis, 87, Article 102594. https://doi.org/10.1016/j.irfa.2023.102594
Yang, C. H. (2022). How artificial intelligence technology affects productivity and employment: Firm-level evidence from Taiwan. Research Policy, 51(6), Article 104536. https://doi.org/10.1016/j.respol.2022.104536
Yuan, L., Tao, J., Li, H., & Dai, P. (2024). Narrative innovation disclosure and stock price crash risk: Evidence from Chinese listed firms. Research in International Business and Finance, 71, Article 102479. https://doi.org/10.1016/j.ribaf.2024.102479
Zalbigi, A., Fattahi, S., & Ghaderi, N. (2023). The Effect of Economic Policy Uncertainty on Stock Price Crash Risk in the Iranian Capital Market. Journal of Financial Economics, 9(3), 33-50. https://www.jamv.ir/article_182869_77af8ca22289f61b363d8aa4b2f1370d.pdf
Zhao, Y., Li, H., Miao, Z., & Li, K. (2025). Digital M&As, knowledge distance, and labor productivity: Technical and organizational perspective. Economic Modelling, 147, 107064. https://doi.org/10.1016/j.econmod.2025.107064
Zhu, Z. Y., Xie, H. M., & Chen, L. (2023). ICT industry innovation: Knowledge structure and research agenda. Technological Forecasting and Social Change, 189, Article 122361. https://doi.org/10.1016/j.techfore.2023.122361
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Copyright (c) 2025 Masoumeh Darabi, Gholamreza Zomorodian, Bahman Banimahd, Mirfeiz Fallah Shams (Author)
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