International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Examining the Role of Markov Switching in Predicting Volatility in the Tehran Stock Exchange Based on Regional Market Fluctuations Using Heterogeneous Autoregressive (HAR) Models

Document Type : Original Article

Authors
Seyed Hossein Hosseini Kebria 1
Reza Aghajan Nashtaei 1
Mehrdad Sadrara 2
1 Department of Business management, Ra.C.,Islamic Azad University, Rasht, Iran
2 Assistant Prof Economics and Accounting Group, Faculty of Literature and Humanities, University of Guilan, Rasht, Iran.
10.22034/ijfma.2025.78720.2301
Abstract
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.
Keywords
Markov Switching | Volatility Forecasting | HAR Model | International Volatility Spillovers
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