IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/51584.html
   My bibliography  Save this paper

The Application of GARCH Methods in Modeling Volatility Using Sector Indices from the Egyptian Exchange

Author

Listed:
  • Ezzat, Hassan

Abstract

This paper examines sector specific volatility in order to determine how different sectors respond to volatility shocks within the same equity market. The Egyptian Exchange sector indices are used where firms are disaggregated and classified into twelve different sectors. Volatility is modeled using GARCH, EGARCH and TGARCH in order to examine the temporal volatility dynamics of each specific industry. Stylized facts such as volatility clustering, long memory and the leverage effect are investigated for each sector. Furthermore, the data is divided into two periods. The first period includes sector returns prior to the Egyptian revolution of January 25th 2011. This period was characterized by tranquil volatility. The second period includes the period of the revolution extending one and a half years after the revolution till June 30th 2012. This period was characterized by turbulent volatility. The findings indicate that TGARCH is the preferred model providing successful model specification for all sector indices during both periods. Although the stylized facts where apparent for most sectors for both periods, there was strong evidence of heterogeneous response of sector volatility due to the exogenous shocks of the revolution.

Suggested Citation

  • Ezzat, Hassan, 2012. "The Application of GARCH Methods in Modeling Volatility Using Sector Indices from the Egyptian Exchange," MPRA Paper 51584, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:51584
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/51584/1/MPRA_paper_51584.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    2. Matteo Luciani & David Veredas, 2012. "A model for vast panels of volatilities," Working Papers 1230, Banco de España.
    3. Rui Castro & Gian Luca Clementi & Yoonsoo Lee, 2015. "Cross Sectoral Variation in the Volatility of Plant Level Idiosyncratic Shocks," Journal of Industrial Economics, Wiley Blackwell, vol. 63(1), pages 1-29, March.
    4. Poterba, James M & Summers, Lawrence H, 1986. "The Persistence of Volatility and Stock Market Fluctuations," American Economic Review, American Economic Association, vol. 76(5), pages 1142-1151, December.
    5. Dongweí Su, 2003. "Risk, Return and Regulation in Chinese Stock Markets," World Scientific Book Chapters, in: Chinese Stock Markets A Research Handbook, chapter 3, pages 75-122, World Scientific Publishing Co. Pte. Ltd..
    6. Tse, Y. K., 1991. "Stock returns volatility in the Tokyo stock exchange," Japan and the World Economy, Elsevier, vol. 3(3), pages 285-298, November.
    7. Malmsten, Hans, 2004. "Evaluating exponential GARCH models," SSE/EFI Working Paper Series in Economics and Finance 564, Stockholm School of Economics, revised 03 Sep 2004.
    8. Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
    9. Ser-Huang Poon & Clive W.J. Granger, 2003. "Forecasting Volatility in Financial Markets: A Review," Journal of Economic Literature, American Economic Association, vol. 41(2), pages 478-539, June.
    10. Kate Adjaoute & Martin Bruand & Rajna Gibson‐Asner, 1998. "On the Predictability of the Stock Market Volatility: Does History Matter?," European Financial Management, European Financial Management Association, vol. 4(3), pages 293-319, November.
    11. Tran MANH Tuyen, 2011. "Modeling Volatility Using GARCH Models: Evidence from Vietnam," Economics Bulletin, AccessEcon, vol. 31(3), pages 1935-1942.
    12. Dimson, Elroy & Marsh, Paul, 1990. "Volatility forecasting without data-snooping," Journal of Banking & Finance, Elsevier, vol. 14(2-3), pages 399-421, August.
    13. Baillie, Richard T. & DeGennaro, Ramon P., 1990. "Stock Returns and Volatility," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 25(2), pages 203-214, June.
    14. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    15. Mohammad Najand, 2002. "Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models," The Financial Review, Eastern Finance Association, vol. 37(1), pages 93-104, February.
    16. De Santis, Giorgio & imrohoroglu, Selahattin, 1997. "Stock returns and volatility in emerging financial markets," Journal of International Money and Finance, Elsevier, vol. 16(4), pages 561-579, August.
    17. Arshanapalli, Bala & Doukas, John & Lang, Larry H. P., 1997. "Common volatility in the industrial structure of global capital markets," Journal of International Money and Finance, Elsevier, vol. 16(2), pages 189-209, April.
    18. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
    19. Mr. Mauro Mecagni & Maged Sawky Sourial, 1999. "The Egyptian Stock Market: Efficiency Tests and Volatility Effects," IMF Working Papers 1999/048, International Monetary Fund.
    20. Akgiray, Vedat, 1989. "Conditional Heteroscedasticity in Time Series of Stock Returns: Evidence and Forecasts," The Journal of Business, University of Chicago Press, vol. 62(1), pages 55-80, January.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Bahmani, Mohammad & Sheikh Ahmadi, Sayed Amir & Sanginabadi, Bahram, 2013. "Return Volatility and Asymmetric News of Computer Industry stocks in Tehran Stock Exchange (TEX)," MPRA Paper 70793, University Library of Munich, Germany, revised 15 Mar 2014.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ezzat, Hassan, 2012. "The Application of GARCH and EGARCH in Modeling the Volatility of Daily Stock Returns During Massive Shocks: The Empirical Case of Egypt," MPRA Paper 50530, University Library of Munich, Germany.
    2. Ercan Balaban & Asli Bayar & Robert Faff, 2006. "Forecasting stock market volatility: Further international evidence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(2), pages 171-188.
    3. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    4. Brailsford, Timothy J. & Faff, Robert W., 1996. "An evaluation of volatility forecasting techniques," Journal of Banking & Finance, Elsevier, vol. 20(3), pages 419-438, April.
    5. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    6. Ioannis A. Tampakoudis & Demetres N. Subeniotis & Ioannis G. Kroustalis, 2012. "Modelling volatility during the current financial crisis: an empirical analysis of the US and the UK stock markets," International Journal of Trade and Global Markets, Inderscience Enterprises Ltd, vol. 5(3/4), pages 171-194.
    7. El Jebari, Ouael & Hakmaoui, Abdelati, 2018. "GARCH Family Models vs EWMA: Which is the Best Model to Forecast Volatility of the Moroccan Stock Exchange Market? || Modelos de la familia GARCH vs EWMA: ¿cuál es el mejor modelo para pronosticar la ," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 26(1), pages 237-249, Diciembre.
    8. David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
    9. M. Kabir Hassan & Anisul M. Islam & Syed Abul Basher, 2000. "Market Efficiency, Time-Varying Volatility and Equity Returns in Bangladesh Stock Market," Working Papers 2002_6, York University, Department of Economics, revised Jun 2002.
    10. Blair, Bevan J. & Poon, Ser-Huang & Taylor, Stephen J., 2001. "Forecasting S&P 100 volatility: the incremental information content of implied volatilities and high-frequency index returns," Journal of Econometrics, Elsevier, vol. 105(1), pages 5-26, November.
    11. Novkovska, Blagica & Serafimovic, Gordana, 2018. "Recognizing The Vulnerability Of Generation Z To Economic And Social Risks," UTMS Journal of Economics, University of Tourism and Management, Skopje, Macedonia, vol. 9(1), pages 29-37.
    12. Kanungo, Rama Prasad, 2021. "Uncertainty of M&As under asymmetric estimation," Journal of Business Research, Elsevier, vol. 122(C), pages 774-793.
    13. Kiymaz, Halil & Berument, Hakan, 2003. "The day of the week effect on stock market volatility and volume: International evidence," Review of Financial Economics, Elsevier, vol. 12(4), pages 363-380.
    14. Shiyi Chen & Wolfgang K. Härdle & Kiho Jeong, 2010. "Forecasting volatility with support vector machine-based GARCH model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 29(4), pages 406-433.
    15. Balaban, Ercan & Ozgen, Tolga, 2016. "Trading session effects on stock returns and their conditional volatility: Firm-level evidence from a European Union accession country," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 446(C), pages 264-271.
    16. Bali, Turan G. & Weinbaum, David, 2007. "A conditional extreme value volatility estimator based on high-frequency returns," Journal of Economic Dynamics and Control, Elsevier, vol. 31(2), pages 361-397, February.
    17. Elyasiani, Elyas & Mansur, Iqbal, 1998. "Sensitivity of the bank stock returns distribution to changes in the level and volatility of interest rate: A GARCH-M model," Journal of Banking & Finance, Elsevier, vol. 22(5), pages 535-563, May.
    18. Twm Evans & David McMillan, 2007. "Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1421-1430.
    19. repec:awi:wpaper:0472 is not listed on IDEAS
    20. Miralles-Quirós, José Luis & Daza-Izquierdo, Julio, 2015. "Do DOW returns really influence the intraday Spanish stock market behavior?," Research in International Business and Finance, Elsevier, vol. 33(C), pages 99-126.
    21. LeBaron, Blake, 2003. "Non-Linear Time Series Models in Empirical Finance,: Philip Hans Franses and Dick van Dijk, Cambridge University Press, Cambridge, 2000, 296 pp., Paperback, ISBN 0-521-77965-0, $33, [UK pound]22.95, [," International Journal of Forecasting, Elsevier, vol. 19(4), pages 751-752.

    More about this item

    Keywords

    Egyptian Exchange; EGARCH; TGARCH; Idiosyncratic Risk; Revolution;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • D53 - Microeconomics - - General Equilibrium and Disequilibrium - - - Financial Markets
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:51584. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.