مدل سازی نوسانات بازده بورس اوراق بهادار تهران مدل MRS-FI-TGARCH و FI-TGARCH
محورهای موضوعی : دانش سرمایه‌گذاری
هاجر مرادیان
1
(دانشجوی دکتری،گروه اقتصاد، واحد شیراز، دانشگاه آزاداسلامی، شیراز، ایران)
علی حقیقت
2
(استادیار و عضو هیات علمی گروه اقتصاد، واحد شیراز، دانشگاه آزاداسلامی، شیراز، ایران (نویسنده مسئول))
هاشم زارع
3
(استادیار و عضو هیات علمی گروه اقتصاد، واحد شیراز، دانشگاه آزاداسلامی، شیراز، ایران.)
مهرزاد ابراهیمی
4
(استادیار و عضو هیات علمی گروه اقتصاد، واحد شیراز، دانشگاه آزاداسلامی، شیراز، ایران.)
کلید واژه: مدل سازی, بازده سهام, مارکوف, حافظه بلندمدت, تقارن,
چکیده مقاله :
هدف این مقاله افزایش انعطاف پذیری مدل سازی نوسانات بازار سرمایه می باشد. این امربا معرفی مدل MRS-FI-TGARCH برای اولین بار در دنیا انجام می گیرد. به این منظور از شاخص هفتگی قیمت بورس اوراق بهادار تهران طی سالهای ۲۰۰۹ تا ۲۰۱۷ استفاده می شود .پارامترها قابلیت تغییر با رژیم را دارند. نتایج نشان داد دو رژیم رونق، با بازده انتظاری بالا و نوسان بالا و رژیم رکود، با بازده انتظاری پایین و نوسان پایینوجود دارند. افزودن قابلیت پیش بینی اثرات نامتقارن وحافظه بلند مدت نوآوری مدل جدید است. معناداری ضریب منفی اثرات نامتقارن دررژیم رونق نشان می دهد اثر اخبار بد بر نوسانات، کمتر از اخبار خوب است . معنادارنبودن آن در رژیم رکود ، بیانگرمتقارن بودن اثرات اخبار خوب و بد است. دررژیم رونق، حافظه نامحدود وجود دارد اما دررژیم رکود اثر نوسانات با نرخ هیپربولیک کاهش می یابد.
The aim of this paper is to expand flexibility of modeling in capital market fluctuations. We achieve the goal by introducing MRS-FITGARCH model for the first time in the world. We use weekly TEPIX changes from 2009 to 2017. The parameters could change through the regimes. Results show that there are two regimes; regime one with high return mean and high return variance and regime two with low return mean and low return variance. Adding asymmetric effects and long memory potential prediction, are the novation of our new model. Valued Negative asymmetric effects coefficient results that bad news effects on the fluctuations were less than good news. It was not to be valued in regime tow and it means, good news and bad news has the symmetric effects in this regime. In regime one, there is unlimited long memory coefficient but in regime two fluctuations effects decreases in hyperbolic rate.
* Abouwafia, Hashem, Chambers, Marcus J. (2015). Monetary policy, exchange rates and stock prices in the Middle East region. International Review of Financial Analysis Vol 37, PP: 14–28
* Ardia, David. (2009). Bayesian estimation of a Markov-switching threshold asymmetric GARCH model with Student-t innovations. Econometrics Journal Vol 12, PP: 105–126
* Aloui, C., Jammazi, R. (2009). The Effects of Crude Oil Shocks on Stock Market Shifts Behavior: A Regimes Witching Approach. Energy Economics Vol 31 (5) , PP: 789–799
* Bae, J., Kim, C. J., Nelson, C. R. (2007). Why are stock returns and volatility negatively correlated? Journal of Empirical Finance Vol 14, 41–58
* Bauwens, L., Preminger, A., Rombouts, J. V. K. (2006). Regime Switching GARCH Models. CORE Discussion Paper, Vol 11, PP: 47- 63
* Bohl, M. T, Essid, B, Siklos, P. L. (2012) "Do Short Selling Restrictions Destabilize Stock Returns? Lessons from Taiwan" The Quarterly Review of Economics and Finance, Vol 52, PP 198–206
* Bollerslev, T., Mikkelsen, H. (1996). Modelling and pricing long memory in stock market volatility. J. Econometrics Vol 73, PP: 151–184
* Chkili, W., Nguyen, D.K. (2014) Exchange rate movements and stock market returns in a regime-switching environment: Evidence for BRICS countries, Research in International Business and Finance. Vol 31. PP: 46-56
* Chkili, Walid, Aloui, Chaker, NguyenDuc, Khuong. (2012). Asymmetric effects and long memory in dynamic volatility relationships between stock returns and exchange rates. Journal of International Financial Markets Institutions & Money. Vol 22, PP: 738-757
* Chortareas, Georgios and et al. (2012).Switching to floating exchange rates, Devaluations, and stock returns in MENA countries, International Review of
* Financial Analysis Vol 21, PP: 119–127
* Diamantis, P. F. (2008). Financial liberalization and changes in the dynamic behaviour of emerging market volatility: evidence from four Latin American equity markets. Research in International Business and Finance Vol 22, PP: 362–377
* Ding, Z., C. W. J. Granger, and R. F. Engle. (1993). A long memory property of stock market returns and a new model, Journal of Empirical Finance Vol 1, 83–106
* Dunne, Peter, Hau, Harald, Moore Michael. (2010). International order flows: Explaining equity and exchange rate returns. Journal of International Money and Finance. Vol 29. PP: 358-386
* Fakhfekh, M , Hachicha ,N , Jawadi,F , Selmi ,N , Idi, Cheffou (2016). Measuring volatility persistence for conventional and Islamic banks: An FI-EGARCH approach. Emerging Markets Review. Vol 27, PP : 84–99
* Granger, C. W. J. (1981). Some properties of time series data and their use in econometric model specification. Journal of Econometrics, Vol 16, PP: 121-130
* Guidolin, M., Timmermann, A. (2006). An econometric model of nonlinear dynamics in the joint distribution of stock and bond returns. Journal of Applied Econometrics Vol 21, PP: 1–22
* Hamilton, J. D. (2008). Oil and the macroeconomy, In: Durlauf, S., Blume, L. (Eds.) , The New Palgrave Dictionary of Economics, 2nd Ed. Palgrave MacMilan Ltd.
* Hwang. Y (2001) "Asymmetric long memory GARCH in exchange return.” Economics Letters,Vol 73, PP: 1–5
* Henry, O. (2009). Regime switching in the relationship between equity returns and short-term interest rates. Journal of Banking and Finance Vol 33, PP: 405–414
* Imran, et al. (2010). Causal Relationship between Macroeconomic Indicators and Stock Exchange Prices in Pakistan. African Journal of Business Management.Vol. 4(3).
* Ismail, M. T., Isa, Z. (2008). Identifying regime shifts in Malaysian stock market returns. International Research Journal of Finance and Economics Vol 15, PP: 44–57.
* Kutty, G. (2010). The relationship between exchange rates and stock prices: the case of Mexico. North American Journal of Finance and Banking Research Vol 4, PP: 1–12.
* Liang, C.C, Lin, J.B, Hsu, H.C (2013). "Reexamining the relationships between stock prices and exchange rates in ASEAN-5 using panel Granger causality approach. “Economic Modelling, Vol 32, PP : 560–563
* Lin, C.H, (2012). "The comovement between exchange rates and stock prices in the Asian emerging market." International Review of Economics and Finance, Volu 22, PP: 161–172
* Maheu, J. M., McCurdy, T. H. (2000). Identifying bull and bear markets in stock returns. Journal of Business and Economic Statistics Vol 18, PP: 100–112.
* Moore .Tomoe, Wang.Ping. (2014). Dynamic linkage between real exchange rates and stock prices: Evidence from developed and emerging Asian markets. International Review of Economics and Finance Vol 29, PP: 1-11
* Schaller, H. (Norden, S., 1997). Regime switching in stock market returns. Applied Financial Economics Vol 7, PP: 177–192.
* Skovmand, David. (2015). Modeling tail distributions with regime switching GARCH models. Master Thesis. Copenhagen Business School
* Tsen, W. H, (2017). "Real exchange rate returns and real stock price returns. ” International Review of Economics & Finance. Vol. 49, PP: 340–352
* Turner, M. C., Startz, R., Nelson, C. F. (1989). A Markov model of heteroskedasticity, risk, and learning in the stock market. Journal of Financial Economics Vol 25, PP: 3–22.
* Walid, C., Chaker, A., Masood, O., Fry, J. (2011). Stock market volatility and exchange rates in emerging countries: A Markov-state switching approach, Emerging Markets Review Vol 12, 272-292.
* Wang, P., Theobald, M. (2008). Regime-switching volatility of six East Asian emerging stock markets. Research in International Business and Finance Vol 22, PP: 267–283.
* Xiu Jin & Yao Jin, (2007), “Empirical Study of ARFIMA Model Based on Fractional Differencing”, Physica vol 377, PP: 138–154.
* Yang, S. Y., Doong, S. C. (2004). Price and volatility spillovers between stock prices and exchange rates: empirical evidence from the G-7 countries. International Journal of Business and Economics Vol 3, PP: 139–153.
* Zakoin, J-M (1992). Threshold heteroskedasity models. jornal of economic dynamics and control. vol 18. PP: 931-955
_||_