Modelling Stock Market Volatility in India Using Univariate GARCH Models

  • Pramath Nath ACHARYA P G Department of Commerce Berhampur University, Berhampur, Odisha, India
  • Srinivasan KALIYAPERUMAL Business Studies Department Higher College of Technology, Muscat, Sultanate of Oman
  • Rudra Prasanna MAHAPATRA P G Department of Commerce Berhampur University, Berhampur, Odisha, India


This study examines the volatility pattern of Indian stock market based on the daily price data of National Stock Exchange and Bombay Stock Exchange over the period 1996-2017. The analysis is carried out by using various GARCH models to capture symmetric as well as asymmetric effects. The study suggests the presence of asymmetry and PGARCH model as the best fit model among all the selected models.


[1] Abdalla, S.Z.S. 2012. Modeling stock returns volatility: Empirical evidence from Saudi Stock Exchange, International Research Journal of Finance and Economics, 85: 166-179.
[2] Bahadur, S.G.C. 2008. Volatility analysis of Nepalese stock market, The Journal of Nepalese Business Studies, 5(1): 76-83. DOI:
[3] Banumathy, K., and Azhagaiah, R. 2015. Modelling stock market volatility: Evidence from India, Managing Global Transition, 13(1): 27-42. Available at: (Accessed on 5/5/2018)
[4] Birau, R., Trivedi, J., and Antonescu, M. 2015. Modeling S&P Bombay Exchange BANKEX Index Volatility Pattern using GARCH Model, Procedia Economics and Finance, 32: 520-525. DOI:
[5] Bollerslev, T. 1986. Generalized autoregressive conditional Heteroskedasticity, Journal of Econometrics, 31 (3): 307-327. DOI:
[6] Chang, C.L., and McAleer, M. 2017. The correct regularity condition and interpretation of asymmetry in EGARCH, Economics Letters, 161: 52-55. DOI:
[7] Chong, C.W., Ahmad, M.I., and Abdullah, M.Y. 1998. Performance of GARCH Models in forecasting Stock Market Volatility, Journal of Forecasting, 18: 333-343. DOI:<333::AID-FOR742>3.0.CO;2-K
[8] Ding, Z., Granger, C.W.J., and Engle, R.F. 1993. A long memory property of stock market returns and a new model. Journal of Empirical Finance, 1: 83-106. DOI:
[9] Floros, C. 2008. Modelling volatility using GARCH Models: Evidence from Egypt and Israel, Middle Eastern Finance and Economics, 2: 31-41.
[10] Glosten, L.R; Jagannathan, R., Runkle, D.E. 1993. On the relation between the expected value and the volatility of the nominal excess returns on stocks. Journal of Finance, 48(5): 1779-1791. DOI:
[11] Kalotychou, E., and Staikouras. S.K. 2009. An overview of the issues surrounding stock market volatility, in Stock market volatility, edited by G.N. Gregoriou, Chapman Hall-CRC/Taylor and Francis, New York, USA, Available at: SSRN:
[12] Kannadhasan, M., Thakur, B.P.S., Aramvalarthan, S., and Radhakrishnan, A. 2018. Modelling volatility in emerging capital market: The case of Indian capital market, Academy of Accounting and Financial Studies Journal, 22(1): 1-11. Available at: (Accessed on 07/08/2018)
[13] Karmakar, M. 2005. Modelling conditional volatility of the Indian Stock Markets, Vikalp, 30(3): 21-37. DOI:
[14] Karmakar, M. 2006. Stock market volatility in the Long Run, 1961-2005, Economic and Political Weekly, 41(18): 1796-1802.
[15] Kaur, H. 2004. Time varying volatility in the Indian Stock Market, Vikalp, 29(4): 25-42. DOI:
[16] Khedri, S., and Muhammad, N. 2008. Empirical analysis of the UAE Stock Market Volatility, International Research Journal of Finance and Economics, (15): 249-260.
[17] Kumar, R, and Dhankar, R.S. 2010. Empirical analysis of conditional Heteroskedasticity in time series of stock returns and asymmetric effect on volatility, Global Business Review, 11(1): 21-33. DOI:
[18] McAleer, M., and Hafner, C.M. 2014. A one line derivation of EGARCH, Eonometrics, 2 (2): 92-97. DOI:
[19] McMIlan, D.G., and Speight, A.E.H. 2004. Daily volatility forecasts: Reassessing the performance of GARCH Models, Journal of Forecasting, 23 (6): 449-458. DOI:
[20] Nelson, D.B. 1991. Conditional Heteroskedasticity in asset returns: A new approach. Econometrica, 59(2): 347–370. DOI:
[21] Oberholzer, N., and Venter, P. 2015. Univariate GARCH models applied to the JSE/FTSE stock indices, presented at International Conference on Applied Economics, 2-4 July 2015, Kazan, Russia and published in Procedia Economics and Finance, 24: 491-500, DOI:
[22] Panait, I., and Salavescu, E.O. 2012. Using GARCH-in-Mean Model to investigate volatility and persistence at different frequencies for Bucharest stock exchange, during 1997-2012, Theoretical and Applied Economics, XIX(5): 55-76. Available at: (Accessed on 05/05/2018).
[23] Pandey, A. 2005. Volatility models and their performance in Indian capital market, Vikalp, 30(2): 26-45. DOI:
[24] Singh, V.K., and Ahmad, N. 2011. Modelling S&P CNX index volatility with GARCH Class Volatility Models: Empirical evidence from India, Indian Journal of Finance, 5(2): 34-47.
[25] Singh, Y.P., and Babbar, S.K. 2010. Volatility patterns in bank stock returns in India, Indian Journal of Commerce, 63(1): 1-20.
[26] Tripathy, T., Gil-Alana, L.A. 2015. Modelling time varying volatility in the Indian stock returns: Some empirical evidence, Review of Development Finance, 5(2): 91-97. DOI:
[27] Zakoian, J. 1994. Threshold generalized autoregressive conditional Heteroskedasticity Models. Journal of Economic Dynamics and Control, 18(5): 931–955. DOI:
How to Cite
ACHARYA, Pramath Nath; KALIYAPERUMAL, Srinivasan; MAHAPATRA, Rudra Prasanna. Modelling Stock Market Volatility in India Using Univariate GARCH Models. Journal of Advanced Studies in Finance, [S.l.], v. 10, n. 1, p. 56-66, aug. 2019. ISSN 2068-8393. Available at: <>. Date accessed: 23 jan. 2022. doi:
Journal of Advanced Studies in Finance