10.22099/ijes.2013.2031

Abstract

We have introduced an early warning system for volatility regimes regarding Tehran Stock Exchange using Markov Switching GARCH approach. We have examined whether Tehran Stock Market has calmed down or more specifically, whether the surge in volatility during 2007-2010 global financial crises still affects stock return volatility in Iran.  Doing so, we have used a regime switching GARCH model.  The data consist of 3067 daily observations of the closing value of the Tehran stock market from 29/09/1997 to 09/09/2010. The results indicate that during the crisis period, Tehran stock exchange was in the high-volatility regime. Smoothed probability plots show that the volatility in 2007-2009 was in high volatility regime but at 2009-2010, Volatility turned to low volatility regime. Also, we have introduced an early warning system for forecasting high volatility in Tehran Stock Exchange

Keywords

Article Title [Persian]

آیا بازار سهام تهران پس از بحران مالی جهانی آرام گرفته است؟ رهیافت مارکف سوییچینگ گارچ

Abstract [Persian]

این مقاله با استفاده از رهیافت مارکوف سوییچینگ گارچ یک سیستم هشدار پیش از وقوع را باتوجه به رژیم‌های تلاطمی بازار سهام تهران معرفی می‌نماید. این تحقیق این مسئله را آزمون می‌کند که آیا بازار سهام تهران به آرامش رسیده است یا با صراحت بیشتر، آیا موج تلاطم بحران مالی جهانی 2007-2010 همچنان بر تلاطم بازدهی بازار ایران اثرگذار می‌باشد. برای انجام این کار، از مدل مارکوف سوییچینگ گارچ استفاده شده است.  داده‌ها شامل 3067 مشاهده آخرین شاخص قیمت روزانه بازار سهام تهران از 29/09/1997 تا 09/09/2010 است. نتایج حاکی از آن است که بازار سهام تهران در دوره بحران در رژیم تلاطم بالا قرار داشته است. نمودار احتمال هموار نشان می‌دهد که تلاطم در 2007-2009 در رژیم تلاطم بالا بود، ولی در 2009-2010 بسوی رژیم تلاطم پایین چرخش نموده است. همچنین، ما سیستم هشدار پیش از وقوعی را برای پیش بینی تلاطم در بازار سهام تهران معرفی نموده‌ایم

Keywords [Persian]

  • بازار سهام تهران
  • بحران‌های مالی جهانی
  • مارکوف سوییچینگ گارچ
Abounoori, E., & Nademi, Y. (2011). The asymmetric effect of news on Tehran Stock Exchange volatility. International Journal of Trade, Economics and Finance, 2(4), 323-326.
Abounoori, E., & Erfani, A. (2006). Forecasting probability of currency crisis in OPEC Countries. Journal of Tahghighate Eghtesadi, 73, 167-191.
Abounoori, E., & Erfani, A. (2008). Markov regime switching and forecasting probability of currency crisis in OPEC countries. Journal of Pajouheshname Eghtesadi, 30, 153-174.
Ahn, J. J., Oh, K. J., Kim, T. Y., & Kim, D. H. (2011). Usefulness of support vector machine to develop an early warning system for financial crisis. Expert Systems with Applications, 38(4), 2966-2973.
Albadvi, A., Chaharsooghi, S. K., & Esfahanipour, A. (2007). Decision making in stock trading: An application of PROMETHEE. European Journal of Operational Research, 177(2), 673-683.
Berkmen, P., Gelos, R. G., Rennhack, R., & Walsh, J. (2009). The global financial crisis: Explaining cross-country differences in the output impact. IMF Working Papers, 1-19.
Bollen, S., Gray, N., & Whaley, R. (2000). Regime-Switching in Foreign Exchange Rates: Evidence From Currency Option Prices. Journal of Econometrics, 94, 239-276.
Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31, 307–327.
Bollerslev, T., Engle, R., & Nelson, D. (1994). ARCH Models, in Handbook of Econometrics, ed. by Engle, R., & McFadden, D., chap. 4, pp. 2959-3038. North Holland Press, Amsterdam.
Borio, C., & Lowe, P. (2002). Assessing the risk of banking crises. BIS Quarterly Review, December, 43–54.
Cai, J. (1994). Markov Model of Unconditional Variance in ARCH. Journal of Business and Economics Statistics, 12, 309-316.
Cao, Y. (2012). Aggregating multiple classification results using Choquet integral for financial distress early warning. Expert Systems With Applications, 39(2), 1830-1836.
Chen, L. H., & Guo, T. Y. (2011). Forecasting financial crises for an enterprise by using the Grey Markov forecasting model. Quality & Quantity, 45(4), 911-922.
Choudhry, K. & Klaassen, F. (2001). Have East Asian Stock Markets Calmed Down? Evidence from a Regime-Switching Model.  Working Paper, Amsterdam University.
Davis, E.P., & Karim, D. (2008a). Comparing early warning systems for banking crises. Journal of Financial Stability, 4, 89–120
Demirgüc-Kunt, A., & Detragiache, E. (2005). Cross-country empirical studies of systemic bank distress: A survey. IMF Working Paper No. WP/05/96.
Dueker, M. (1997). Markov Switching in GARCH Processes in Mean Reverting Stock Market Volatility. Journal of Business and Economics Statistics, 15, 26-34.
Duttagupta, R., & Cashin, P. (2008). The anatomy of banking crises. IMF Working  Paper No. WP/08/93
Ebrahimpour, R., Nikoo, H., Masoudnia, S., Yousefi, M. R., & Ghaemi, M. S. (2011). Mixture of MLP-experts for trend forecasting of time series: A case study of the Tehran stock exchange. International Journal of Forecasting, 27(3), 804-816.
Fasanghari, M., & Montazer, G. A (2010). Design and implementation of fuzzy expert system for Tehran Stock Exchange portfolio recommendation. Expert Systems with Applications, 37(9), 6138-6147.
Foster, K. R., & Kharazi, A (2008). Contrarian and momentum returns on Iran's Tehran Stock Exchange. Journal of International Financial Markets, Institutions and Money, 18(1), 16-30.
Gray, S. (1996). Modeling the conditional distribution of interest rates as a regime-switching process.  Journal of Financial Economics, 42, 27-62.
Haas, M., Mittnik, S., & Paolella, M., (2004). A New Approach to Markov-Switching GARCH Models.  Journal of Financial Econometrics, 2, 493-530.
Hamilton, J., & Susmel, R., (1994). Autoregressive Conditional Heteroskedasticity and Changes in Regime. Journal of Econometrics, 64, 307-333.
Hamilton, J. D. (1989).  A new approach to the economic analysis of non-stationary time series and the business cycle.  Econometrica, 57, 357-384. 
Hansen, B.E., (1994). Autoregressive conditional Density Estimation. International Economic Review, 35(3), 705-730.
Kaminsky, L.G., & Reinhart, C.M. (1999). The twin crises; the causes of banking and balance of payments problems. American Economic Review, 89, 473-500.
Kim, C. J. (1994). Dynamic linear models with Markov-switching. Journal of Econometrics, 60(1), 1-22.
Klaassen, F. (2002). Improving GARCH Volatility Forecasts with Regime-Switching GARCH. Empirical Economics, 27, 363-394.
Lamoureux, C., & Lastrapes, W. (1990). Heteroskedasticity in Stock Return Data: Volume versus GARCH Effects. Journal of Finance, 45, 221-229.
Lee, Y. C., & Teng, H. L. (2009). Predicting the financial crisis by Mahalanobis–Taguchi system–Examples of Taiwan’s electronic sector. Expert Systems with Applications, 36(4), 7469-7478.
Lin, C. S., Khan, H. A., Chang, R. Y., & Wang, Y. C. (2008). A new approach to modeling early warning systems for currency crises: Can a machine-learning fuzzy expert system predict the currency crises effectively? Journal of International Money and Finance, 27(7), 1098-1121.
Marcucci, J. (2005). Forecasting stock market volatility with regime-switching GARCH models. Studies in Nonlinear Dynamics and Econometrics, 9, 1-53.
Nasar, Sylvia. (1992). For Fed, a New Set of Tea Leaves, New York Times.
Poon, S. H., & Granger, C. W. (2003). Forecasting volatility in financial markets: A review. Journal of Economic Literature, 41(2), 478-539.
Rose, A. K., & Spiegel, M. M. (2009). Cross-country causes and consequences of the 2008 crisis: early warning (No. w15357). National Bureau of Economic Research.
Shoghi, M. P., & Talaneh, A. (2010). An Analysis of Emerging Markets Returns Volatility: Case of Tehran Stock Exchange. Working Paper Series.
Xinli, W. (2011, November). Genetic Neural Network Model of Forecasting Financial Distress of Listed Companies. In Information Management, Innovation Management and Industrial Engineering (ICIII), 2011 International Conference on (Vol. 1, pp. 487-490). IEEE.
Yahyazadehfar, M., Abounoori, E., & Shababi, H. (2006). Days-of-week effect on Tehran Stock Exchange Returns: An empirical analysis. Iranian Economic Review, 11(16): 149-164