Document Type : Original Article
Authors
1
Ph.D Student in Accounting, Caspian Campus, University of Tehran, Tehran, Iran.
2
Master of financial management, Science and Research Branch, Islamic Azad University, Tehran, Iran.
3
Assistant Professor, Department of Management & Accounting, Shahriar Branch, Islamic Azad University, Shahriar, Iran.
Abstract
This research investigates whether fluctuations in economic policy uncertainty (EPU) are reflected in the volatility behavior of the Tehran Stock Exchange (TSE). Using daily market information from January 2, 2015, to September 23, 2025, along with observations from 183 listed firms, the study develops an empirical framework to evaluate the interaction between uncertainty conditions and equity market performance. The analysis combines several econometric procedures, including ARCH diagnostics, GARCH-based volatility estimation, and GLS regression with an AR(1) adjustment, to identify the characteristics of market volatility and assess the role of EPU. The empirical evidence indicates substantial variability in the TSE index and confirms the existence of time-dependent variance behavior and persistent volatility effects, supporting the use of GARCH specifications. The results show that earlier market disturbances contain significant information for explaining future volatility movements. Furthermore, the estimated GLS model suggests that changes in EPU are associated with a statistically meaningful inverse effect on the dependent variable (coefficient = -0.3451; p-value = 0.0262), providing support for the research hypothesis. The analysis of the Sharpe Ratio also reveals differences in risk-adjusted outcomes across periods characterized by varying uncertainty levels. By focusing on an emerging financial market exposed to repeated policy changes, this study contributes empirical evidence on the interaction between uncertainty conditions and market volatility. The findings suggest that improving policy predictability, monitoring uncertainty indicators, and incorporating uncertainty measures into financial decision processes may help market participants better manage risk exposure.
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