Forecasting Tehran Stock Market Volatility: The Role of Volatility Regime Persistence and the Moderating Effect of International Volatility within the Markov-Switching HAR-MS Model Framework

Document Type : Original Article

Authors
Seyed Hossein Hosseini Kebria 1
Reza Aghajan Nashtaei 1
Mehrdad Sadrara 2
1 Department of 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/jik.2025.78817.4829
Abstract
This study investigates the forecasting of volatility in the Tehran Stock Exchange by employing a Markov-Switching Heterogeneous Autoregressive (MS-HAR) model, while considering the impact of international market volatilities (including regional stock markets and the oil market). Using high-frequency (30-minute) data of the Tehran Price Index (TEPIX) and realized volatility indices of selected international markets (Saudi Arabia, Russia, Dubai, and crude oil prices) during the period 2022-2024, the MS-HAR model was estimated.
Key findings reveal that volatility regimes in the Tehran Stock Exchange follow an asymmetric persistence pattern. Specifically, the low-volatility regime exhibits very high persistence (92% probability of continuation), while the high-volatility regime is unstable (65-75% probability of continuation). The main innovation of this research lies in demonstrating the moderating role of global volatilities during high-volatility regimes, meaning that increased volatilities in international markets prevent the exaggeration of domestic volatilities. Additionally, a significant negative correlation between Iran's market volatility and the Saudi Arabian market reveals the geopolitical dimension of influence.
The results indicate that the MS-HAR model, combined with international variables, is an effective tool for forecasting volatility in the Tehran Stock Exchange. These findings can serve as a basis for designing volatility regime monitoring tools and formulating efficient risk management strategies for investors and policymakers.
Keywords
Markov Switching
Volatility Forecasting
HAR Model
International Volatilities
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