پویایی بازده و انتقال نامتقارن نوسانات بین بازار سهام و فرابورس ایران با استفاده از مدل نامتقارن دومتغیره BEKK-GARCH

زهرا  اسماعیلی تزنگی; سید یحیی ابطحی; غلام رضا  عسکرزاده دره; حمید  خواجه

Authors

Zahra Esmaili Tazangi Department of Financial Management, Ya.C., Islamic Azad University, Yazd, Iran. Author https://orcid.org/0000-0001-9773-821X
Seyed Yahya Abtahi Department of Financial Management, Ya.C., Islamic Azad University, Yazd, Iran. Author https://orcid.org/0009-0004-9477-3967
Gholam Reza Askarzadeh Dareh Department of Financial Management, Ya.C., Islamic Azad University, Yazd, Iran. Author https://orcid.org/0009-0002-3337-0801
Hamid Khajeh Department of Financial Management, Ya.C., Islamic Azad University, Yazd, Iran. Author https://orcid.org/0009-0003-6821-9578

Keywords:

Volatility transmission, volatility failures, bivariate BEKK, GARCH model, leverage, stock market and OTC market

Abstract

Objective: This study examined the dynamic transmission of returns and asymmetric volatility between Iran’s stock market and over-the-counter market while jointly accounting for volatility breaks, thin trading, and aggregate trading volume.

Methodology: Daily observations for the Iranian stock market and over-the-counter market from September 28, 2009, to February 19, 2025, comprising 3,672 observations, were analyzed. The Iterated Cumulative Sum of Squares algorithm was applied to identify structural breaks in unconditional variance. A linear autoregressive state-space model with time-varying coefficients and the Kalman filter was used to adjust the return series for autocorrelation caused by thin trading. Return transmission, short- and long-run volatility spillovers, and asymmetric shock effects were estimated using a bivariate vector autoregressive model combined with an asymmetric BEKK-GARCH specification. Aggregate trading volume and dummy variables representing volatility breakpoints were subsequently incorporated into the mean and variance equations of the augmented model.

Findings: Unit-root tests indicated that stock-market returns, over-the-counter-market returns, and trading volume were stationary at level. Significant autoregressive coefficients confirmed return-memory effects and demonstrated that previous returns affected current returns in both markets. In the baseline specification, the asymmetry coefficients were statistically insignificant, indicating that positive and negative shocks exerted similar effects on conditional variance. However, after controlling for volatility breakpoints, aggregate trading volume, and thin-trading adjustments, the asymmetry coefficients became significant and leverage effects were confirmed in both markets. Negative shocks generated larger increases in volatility than positive shocks of a comparable magnitude. The results also supported significant dynamic return and volatility transmission and interdependence between the two markets.

Conclusion: Jointly incorporating volatility breaks, trading volume, and thin-trading adjustments provides a more accurate representation of cross-market return transmission and asymmetric volatility behavior. Omitting these factors may distort estimates of volatility persistence and underestimate the stronger response of both markets to adverse shocks.

Downloads

Download data is not yet available.

References

Abakah, E., Alagidede, P., Mensah, L., & Ohene-Asare, K. (2018). Non-Linear Approach to Random Walk Test in Selected African Countries. International Journal of Managerial Finance, 14(3), 362-376. https://doi.org/10.1108/IJMF-10-2017-0235

Abtahi, S. Y., & Khajeh Mahmoudabadi, H. (2019). Threshold Dynamics and Asymmetric Adjustment of Stock and Foreign Exchange Market Returns in Iran. Investment Knowledge, 9(34), 149-165.

Ahn, E., & Lee, J. M. (2006). Volatility Relationship Between Stock Performance and Real Output. Applied Financial Economics, 16(11), 777-784. https://doi.org/10.1080/09603100500424775

Bollerslev, T. (2008). Glossary to ARCH (GARCH). In Volatility and Time Series Econometrics (pp. 81-107). Oxford University Press. https://doi.org/10.2139/ssrn.1263250

Chen, L., Dou, W., & Wang, J. (2021). Leverage Effect and Market Conditions. Journal of Empirical Finance, 60, 1-18. https://doi.org/10.1016/j.jempfin.2020.12.003

Chukhrova, N., & Johannssen, A. (2017). State Space Models and the Kalman-Filter in Stochastic Claims Reserving: Forecasting, Filtering and Smoothing. Risks, 5(2), 30. https://doi.org/10.3390/risks5020030

Cont, R. (2007). Volatility Clustering in Financial Markets: Empirical Facts and Agent-Based Models. In Long Memory in Economics (pp. 289-309). Springer. https://doi.org/10.1007/978-3-540-34625-8_10

Fischer, T., & Krauss, C. (2018). Deep Learning with Long Short-Term Memory Networks for Financial Market Predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054

Forson, J. A., & Janrattanagul, J. (2014). Selected Macroeconomic Variables and Stock Market Movements: Empirical Evidence from Thailand. Contemporary Economics, 8(2), 123-157. https://doi.org/10.5709/ce.1897-9254.138

Girard, E., & Biswas, R. (2007). Trading Volume and Market Volatility: Developed Versus Emerging Stock Markets. Financial Review, 42(3), 429-459. https://doi.org/10.1111/j.1540-6288.2007.00178.x

Guo, J. (2015). Causal Relationship Between Stock Returns and Real Economic Growth in the Pre- and Postcrisis Period: Evidence from China. Applied Economics, 47(1), 12-31. https://doi.org/10.1080/00036846.2014.959653

Harris, R., & Pisedtasalasai, A. (2006). Return and Volatility Spillovers Between Large and Small Stocks in the UK. Journal of Business Finance & Accounting, 33(9-10), 1556-1571. https://doi.org/10.1111/j.1468-5957.2006.00635.x

Harrison, B., & Moore, W. (2012). Stock Market Efficiency, Non-Linearity, Thin Trading and Asymmetric Information in MENA Stock Markets. Economic Issues, 17(1), 77-93.

Harwood, A., & Konidaris, T. (2015). SME Exchanges in Emerging Market Economies: A Stocktaking of Development Practices. https://openknowledge.worldbank.org/bitstream/handle/10986/21381/WPS7160.pdf?sequence=1&isAllowed=y

Hung, J. C., & Lin, Y. Y. (2013). Information Transmission Effects Between Large and Small Capitalization Indices in Tokyo Stock Exchange. Journal of Accounting, Finance and Management Strategy, 8(2), 13-32.

Kahneman, D., & Tversky, A. (2011). Thinking, Fast and Slow. Macmillan.

Kanas, A., & Ioannidis, C. (2010). Causality from Real Stock Returns to Real Activity: Evidence of Regime Dependence. International Journal of Finance & Economics, 15(2), 180-197. https://doi.org/10.1002/ijfe.383

Karmakar, M. (2010). Information Transmission Between Small and Large Stocks in the National Stock Exchange in India: An Empirical Study. The Quarterly Review of Economics and Finance, 50(1), 110-120. https://doi.org/10.1016/j.qref.2009.09.007

Koulakiotis, A., Babalos, V., & Papasyriopoulos, N. (2016). Financial Crisis, Liquidity and Dynamic Linkages Between Large and Small Stocks: Evidence from the Athens Stock Exchange. Journal of International Financial Markets, Institutions and Money, 40, 46-62. https://doi.org/10.1016/j.intfin.2015.06.004

Kuttu, S. (2014). Return and Volatility Dynamics Among Four African Equity Markets: A Multivariate VAR-EGARCH Analysis. Global Finance Journal, 25(1), 56-69. https://doi.org/10.1016/j.gfj.2014.03.001

Liu, X., & Sinclair, P. (2008). Does the Linkage Between Stock Market Performance and Economic Growth Vary Across Greater China? Applied Economics Letters, 15(7), 505-508. https://doi.org/10.1080/13504850500426277

Mahmood, W., & Dinniah, N. (2009). Stock Returns and Macroeconomics Variables: Evidence from the Six Asian-Pacific Countries. International Research Journal of Finance and Economics, 30(1), 154-164.

Naimi, M., Balcilar, M., & Gupta, R. (2020). Forecasting Stock Market Volatility: A Hybrid GARCH-Machine Learning Approach. Applied Economics, 52(55), 6313-6329.

Ngene, G., Tah, K., & Darrat, A. (2017). Long Memory or Structural Breaks: Some Evidence for African Stock Markets. Review of Financial Economics, 34, 61-73. https://doi.org/10.1016/j.rfe.2017.06.003

O'Hara, M. (2015). Market Microstructure Theory. John Wiley & Sons.

Robinson, J., Glean, A., & Moore, W. (2018). How Does News Impact on the Stock Prices of Green Firms in Emerging Markets? Research in International Business and Finance, 45, 446-453. https://doi.org/10.1016/j.ribaf.2017.07.176

Segnon, M., Gupta, R., & Wilfling, B. (2024). Forecasting stock market volatility with regime-switching GARCH-MIDAS: The role of geopolitical risks. International Journal of Forecasting, 40(1), 29-43. https://doi.org/10.1016/j.ijforecast.2022.11.007

Starica, C., & Granger, C. (2005). Nonstationarities in Stock Returns. Review of Economics and Statistics, 87(3), 503-522. https://doi.org/10.1162/0034653054638274

Sun, Y. (2025). The Relationship Between Market Sentiment and Stock Market Volatility. Advances in Economics Management and Political Sciences, 150(1), 85-89. https://doi.org/10.54254/2754-1169/2024.19278

Tang, H. P., Habibullah, M. S., & Puah, C. H. (2008). Stock Market and Economic Growth in Selected Asian Countries. European Journal of Economics, Finance and Administrative Sciences, 2008(7), 1450-2887.

Tessmann, M., Carrasco‑Gutierrez, C., Khodr, O., Magalhães, L. A., & Passos, M. (2024). Volatility transmission and market connectivity of metals and energy commodities: Insights from the spillover index. International Journal of Energy Economics and Policy, 14(3), 609-618. https://doi.org/10.32479/ijeep.15152

Wang, B. (2024). The Effects of the COVID-19 Pandemic Period on Stock Market Return and Volatility. Evidence From the Pakistan Stock Exchange. PLoS One, 19(4), e0295853. https://doi.org/10.1371/journal.pone.0295853

Wang, Y., Li, X., & Peng, Y. (2019). Detecting Structural Breaks in Time Series Using Deep Learning. IEEE Access, 7, 176256-176266. https://doi.org/10.1109/ACCESS.2019.2925561

Zafarpour, A., Sarlak, A., & Haji, G. A. (2023). Investigating the Asymmetric Effects of High-Frequency Trading on the Return of Companies Listed on the Tehran Stock Exchange Using the MS-EGARCH Model. Journal of Financial Economics, 17(65), 333-356.

Zhou, Z. (2025). The impact of investor sentiment fluctuations on stock market liquidity and volatility: Implications for market returns. Finance Research Letters. https://www.sciencedirect.com/science/article/pii/S1544612325020252