Iranian Financial Engineering Association(IFEA) International Journal of Finance & Managerial Accounting 2588-4379 13 50 2026 07 01 Examining the Role of Markov Switching in Predicting Volatility in the Tehran Stock Exchange Based on Regional Market Fluctuations Using Heterogeneous Autoregressive (HAR) Models 151 166 24463 10.22034/ijfma.2025.78720.2301 EN Seyed Hossein Hosseini Kebria Department of Business management, Ra.C.,Islamic Azad University, Rasht, Iran Reza Aghajan Nashtaei Department of Business management, Ra.C.,Islamic Azad University, Rasht, Iran Mehrdad Sadrara Assistant Prof Economics and Accounting Group, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran. 0000-0001-7743-3507 Journal Article 2025 08 16 This study investigates the role of Markov switching in predicting volatility in the Tehran Stock Exchange (TSE), incorporating the influence of international market fluctuations (with a focus on regional markets) using Heterogeneous Autoregressive (HAR) models. The research employs 30-minute interval data from the TEPIX index and international indices (including Saudi Arabia, Russia, Dubai, and Turkey) over the period 2022–2024 to model volatility through a Markov regime-switching approach. Key findings reveal that volatility regime shifts in the TSE follow a persistent pattern: a low-volatility regime exhibits a 92% probability of persistence, whereas a high-volatility regime shows only a 25–35% chance of stability. Regional stock market fluctuations play a moderating role during high-volatility regimes, mitigating the amplification of domestic volatility. Negatively correlated markets (e.g., Saudi Arabia) highlight geopolitical influence. The results indicate that Markov regime-switching models, combined with international variables, serve as an effective tool for forecasting TSE volatility. These insights can assist policymakers in risk management and help investors optimize trading strategies. http://www.ijfma.ir/article_24463_eee75242a3b87751a71bfb6ebce5cad0.pdf