Modelling and Investigating the Differences and Similarities in the Volatility of the Stocks Return in Tehran Stock Exchange Using the Hybrid Model PANEL-GARCH

Document Type: Research Paper


1 Department Of Accounting , Kashan Branch , Islamic Azad University , Kashan , Iran

2 Department of Accounting, Kashan Branch, Islamic Azad University, Kashan, Iran


Efficient financial markets with high degree of transparency do not substantiate the hypothesis that there are differences in the volatility of return. Generally, there are factors rejecting any perfect similarity in the volatility of return in the emerging stock markets, as previous studies in Iran have confirmed the complete difference. On the other hand, the hybrid model PANEL-GARCH has the benefit of high process accuracy, suggesting that the evaluation of the similarity in the volatility of return at the level of market or industry constituent units is better than the simple technique of time series GARCH model for the entire market (instead of evaluation at unit levels). Therefore, the present study intends to investigate complete similarities or differences in the volatility of return in Iran's industries. Results showed that the assumption of complete difference in the volatility of return in the industries did not hold. The results of this process for Iran's industries covering the timespan between 16/2/2013 to 18/3/2017 showed that there are similarities in terms of the y-intercept of conditional mean and variance equations (1.1) PANEL-GARCH between the volatility of stock returns of 23 industries in the Tehran Stock Exchange as confirmed by LRT test. 


[1] Apergis, N., Inflation uncertainty and growth: Evidence from panel data. Australian Economic Papers, 2005, 44(2), P. 186-197.

[2] Babaei, A., Investigating volatility of stock return in Tehran Stock Exchange using panel data and GARCH model. Master's thesis, Faculty of Management and Economics, Sharif University of Technology, 2008.

[3] Bekaert, G., and Harvey, C.R., Emerging equity market volatility. Journal of Financial Economics, 1997, 43, P.29-77.

[4] Bildirici, M, and Ersin, O.O., Improving forecasts of GARCH family models with the artificial neural networks: An application to the daily returns in Istanbul stock exchange. Expert Systems with Applications, 2009, 36, P.7355-7362.

[5] Bollerslev, T., Generalized autoregressive conditional heteroscedasticity. Journal of Econometrics, 1986, 31, P. 307-327.

[6] Cameron, A.C., Trivedi, P.K., Micro econometrics methods and applications. Cambridge University Press, 2005.

[7] Ceremeno, R., and Grier, K., Modelling GARCH processes in panel data: Theory, simulations and Examples. working paper, 2001. (university of Oklahama and CIDE).

[8] Engle. R. F., Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom Inflation. Econometrica, 1982, 50, P.987-1007.

[9] Hsiao C., Analysis of panel data. Th3edn, America: Cambridge University Press, 2014.

[10] Im, K.S., Pesaran, M.H., and Shin, Y., Testing for unit roots in heterogeneous panels. Journal of Economics, 2003, 115(1), P.53-74.

[11] Keshavarz Haddad, Gh., Econometry of Micro data statistics and policy evaluation. Tehran: Ney publications, 2016.

[12] Keshavarz Haddad, Gh., Babaei, A., Modeling the volatility of cash returns in Tehran Stock Exchange using panel data and GARCH model. Financial Research, 2011, 31(13), P.212-234

[13] Kitazawa, Y., Expotential regression of dynamic panel data models. Economics Letters, 2000, 73 (1), P. 56-78

[14] Kling, G., Mergers during the first and second phase globalization: Success, Insider trading and the role of regulation. Unpublished Thesis, Faculty of Economics, Eberhard-Karls-Universität Tübingen, Germany, 2004.

[15] Kovacic, Z., Forecasting volatility: Evidence from the Macedonian stock exchange. MPRA Paper, 2007, 53(19), P.1-47.

[16] Leroy, S. F, Porter R. D., The present value relation: tests based on implied variance bounds. Econometrica, 1981, 49(3), P. 555-574.

[17] Pan, H., Zhang Z., Forecasting financial volatility: Evidence from Chine’s stock market. Working papers in economics and finance, 2006, 6(2), P. 234-257.

[18] Rostami, M.R., Moghaddasi Bayat, M., and Maghami, R., Analysis of the relationship between non-systematic risk and stock returns based on Multiple Regression. Financial Management Outlook Quarterly, 2016, 16, P. 45-53.

[19] Saeedi, H., Mohammadi, Sh., Forecasting fluctuations of market returns using the hybrid model GARCH - Neural Network, Stock Exchange Quarterly, 2012, 16, P. 456-476.

[20] Tsay R.S., Analysis of financial time series. 3th edn, Canada: John Wiley and Son. Inc, 2010.