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An Empirical Analysis of Volatility and Asymmetric Behaviour: Case of NSE and BSE


Affiliations
1 Assistant Professor, Department of Commerce, Govt. College Dujana, Jhajjar, Haryana-124102, India And Research Scolar at: School of Management & Commerce, K.R. Mangalam University, Gurugram, Haryana-122103, India
2 School of Management & Commerce K.R. Mangalam University, Gurugram, Haryana-122103, India
3 Assistant Professor Department of MBA, Maharaja Agarsen Institute of Technology (IP University), Rohini, Delhi-110086,, India
 

The price, return and different events in stock market are uncertain but this uncertainty can provide insight for making investment decisions, if volatility is measured through appropriate model. In this research work, efforts are made to examine and compare the symmetric and asymmetric volatility in two major stock markets of India through the application of econometric models i.e. GARCH, TGARCH and EGARCH. Daily closing prices of NSE (Nifty-50) and BSE (Sensex) from 1st April 2010 to 31st March 2022 is used for the examination purpose. The results show that volatility in Indian market is persisted for a long time The asymmetric behaviour of volatility is also observed in Indian market. Findings are useful to design dynamic pricing, hedging and portfolio management strategies to all market participants.

Keywords

Stock Market Volatility, GARCH Models, Volatility clustering, TGARCH, EGARCH.
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  • Abbas Rizvi, S. K., Naqvi, B., Bordes, C., & Mirza, N. J. E. r.-E. i. (2014). Inflation volatility: an Asian perspective. Economic research - Ekonomska istraživanja, Vol. 27 No. 1, 2014., 27(1), 280-303.

  • Abdalla, S. Z. S., Winker, P. J. I. J. o. E., & Finance. (2012). Modelling stock market volatility using univariate GARCH models: Evidence from Sudan and Egypt. International Journal of Economics and Finance, 4(8), 161-176.

  • AbdElaal, M. A. J. I. R. J. o. F., & Economics. (2011). Modeling and forecasting time varying stock return volatility in the Egyptian stock market. International Research Journal of Finance and Economics, Issue 78 (2011)(78).

  • Aliyev, F. (2019). Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul. International Journal of Financial Studies, 2019 7(2), 27.

  • Arouri, M. E. H., Jouini, J., & Nguyen, D. K. J. E. E. (2012). On the impacts of oil price fluctuations on European equity markets: Volatility spillover and hedging effectiveness. Energy Economics, 34(2), 611-617.

  • Banumathy, K., & Azhagaiah, R. J. M. G. T. I. R. J. (2015). Modelling Stock Market Volatility: Evidence from India. Managing Global Transitions International Research Journal issn 1854-6935 13(2).

  • Bekaert, G., Engstrom, E., & Ermolov, A. (2015). Bad environments, good environments: A non-Gaussian asymmetric volatility model. Journal of Econometrics, 186(1), 258-275. doi:https://doi.org/10.1016/j. jeconom.2014.06.021

  • Black, F. (1976). Studies of stock market volatility changes. 1976 Proceedings of the American Statistical Association Bisiness and Economic Statistics Section. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics,31(3), 307-327. doi:https://doi.org/10.1016/0304- 4076(86)90063-1

  • Bollerslev, T. J. C. R. p. (2008). Glossary to arch (garch). CREATES Research Paper 2008-49, 49. doi:https://dx.doi.org/10.2139/ssrn.1263250

  • Brooks, C., & Burke, S. P. (2003). Information criteria for GARCH model selection. The European Journal of Finance, 9(6), 557-580. doi:10.1080/1351847021000029188

  • Caiado, J. J. E. d. G. (2004). Modelling and forecasting the volatility of the portuguese stock index PSI-20. Estudos de Gestão, 9(1), 3-22.

  • Campbell, J. Y., Lo, A. W., MacKinlay, A. C., & Whitelaw, R. F. (1998). THE ECONOMETRICS OF FINANCIAL MARKETS. Macroeconomic Dynamics, 2(4), 559-562. doi:10.1017/S1365100598009092

  • Chen, X., & Ghysels, E. (2011). News—Good or Bad—and Its Impact on Volatility Predictions over Multiple Horizons. The Review of Financial Studies, 24(1), 46-81. doi:10.1093/rfs/hhq071

  • Dixit, J. K., & Agrawal, V. (2020). Foresight for stock market volatility – a study in the Indian perspective. foresight, 22(1), 1-13. doi:10.1108/FS-05-2019-0040

  • Dixit, J. K., Agrawal, V., & Agarwal, S. (2022). Measuring stock market volatility - a study in India’s perspective. International Journal of Economic Policy in Emerging Economies, 16(1), 1-13. doi:10.1504/IJEPEE.2022.124655

  • Emenike, K. O. (2010). Modelling stock returns volatility in Nigeria using GARCH models.

  • Engle, R. F. (1982). Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation. Econometrica, 50(4), 987–1007. doi:https://doi.org/10.2307/1912773

  • Ezzat, H. (2012). The application of GARCH and EGARCH in modeling the volatility of daily stock returns during massive shocks: The empirical case of Egypt. International Research Journal of Finance and Economics, August 2012(NO. 96), 143-154.

  • Fama, E. F. (1965). The Behavior of Stock-Market Prices. The Journal of Business, 38(1), 34-105.

  • Floros, C. J. M. E. F., & Economics. (2008). Modelling volatility using GARCH models: evidence from Egypt and Israel. (2), 31-41.

  • Franses, P. H., & Van Dijk, D. (1996). Forecasting stock market volatility using (nonlinear) Garch models. Journal of Forecasting, 15(3), 229-235. doi:https://doi. org/10.1002/(SICI)1099-131X(199604)15:3<229::AID-FOR620>3.0.CO;2-3

  • Gokcan, S. (2000). Forecasting volatility of emerging stock markets: linear versus non-linear GARCH models. Journal of Forecasting, 19(6), 499-504. doi:https:// doi.org/10.1002/1099-131X(200011)19:6<499::AID-FOR745>3.0.CO;2-P

  • Goudarzi, H., & Ramanarayanan, C. (2011). Modeling asymmetric volatility in the Indian stock market. International Journal of Business Management, 6(3), 221.

  • Gujarati, N., & Porter, D. J. I., New York. (2009). Basic Econometrics. International Edition McGraw-Hill/Irwin, A Business Unit of The McGraw-Hill Companies.

  • Gupta, R. K., Jindal, N., & Gupta, A. J. I. J. I. E. M. (2014). Conditional volatility and stock market behavior in NSE. Int. Journal of Innovation in Engineering and Management 3, 16-20.

  • Henry, O. (1998). Modelling the asymmetry of stock market volatility. Applied Financial Economics, 8(2), 145-153. doi:10.1080/096031098333122

  • Irfan, M., Irfan, M., & Awais, M. (2010). Modeling volatility of short-term interest rates by ARCH family models: Evidence from Pakistan and India. World Applied Sciences Journal, 9(10), 1089-2010.

  • Jarque, C. M., & Bera, A. K. (1987). A Test for Normality of Observations and Regression Residuals. International Statistical Review / Revue Internationale de Statistique, 55(2), 163-172. doi:10.2307/1403192

  • Joshi, P. (2010). Modeling volatility in emerging stock markets of India and China. Journal of Quantitative Economics, 8(1), 86-94.

  • Karmakar, M. (2005). Modeling conditional volatility of the Indian stock markets. Vikalpa, 30(3), 21-38.

  • Karmakar, M. (2007). Asymmetric Volatility and Risk-return Relationship in the Indian Stock Market. South Asia Economic Journal, 8(1), 99-116. doi:10.1177/139156140600800106

  • Kumar, B., & Singh, P. (2008). Volatility modeling, seasonality and risk-return relationship in GARCH-in-mean framework: the case of Indian stock and commodity markets. Paper presented at the The 5th Conference of Asia- Pacific Association of Derivatives Paper.

  • Kundu, S., & Sarkar, N. (2016). Return and volatility interdependences in up and down markets across developed and emerging countries. Research in International Business and Finance, 36, 297-311. doi:https://doi.org/10.1016/j. ribaf.2015.09.023

  • Kyriakou, I., Pouliasis, P. K., & Papapostolou, N. C. (2016). Jumps and stochastic volatility in crude oil prices and advances in average option pricing. Quantitative Finance, 16(12), 1859-1873. doi:10.1080/14697688.2016.12117 98

  • Leung, M. T., Daouk, H., & Chen, A.-S. (2000). Forecasting stock indices: a comparison of classification and level estimation models. International Journal of Forecasting, 16(2), 173-190. doi:https://doi.org/10.1016/S0169- 2070(99)00048-5

  • Levendis, J. D. (2018). Time series econometrics: Springer.

  • Liu, W., & Morley, B. (2009). Volatility Forecasting in the Hang Seng Index using the GARCH Approach. Asia-Pacific Financial Markets, 16(1), 51-63. doi:10.1007/ s10690-009-9086-4

  • Malepati, V., Challa, M. L., & Kolusu, S. N. R. (2019). Stock Market Volatility Modeling and Forecasting with a Special Reference to BSE Sensex. In R. Biswas & M. Michaelides (Eds.), Essays in Financial Economics (Vol. 35, pp. 105-118): Emerald Publishing Limited.

  • Mehta, K., & Sharma, R. (2011). Measurement of Time Varying Volatility of Indian Stock Market through GARCH Model. Asia Pacific Business Review, 7(3), 34- 46. doi:10.1177/097324701100700304

  • Miron, D., & Tudor, C. J. R. J. o. E. F. (2010). Asymmetric conditional volatility models: Empirical estimation and comparison of forecasting accuracy. Romanian Journal of Economic Forecasting – 3/2010, 13(3), 74-92.

  • Mittal, A. K. a. G., N. (2012). Modeling the volatility of India Stock Market. International Journal of Research in IT & Management, 2(1), 1-23.

  • Moreira, A., & Muir, T. (2017). Volatility-Managed Portfolios. The Journal of Finance, 72(4), 1611-1644. doi:https://doi.org/10.1111/jofi.12513

  • Nelson, D. B. (1991). Conditional Heteroskedasticity in Asset Returns: A New Approach. Econometrica, 59(2), 347-370. doi:10.2307/2938260

  • Okičić, J. (2015). An Empirical Analysis Of Stock Returns And Volatility: The Case Of Stock Markets From Central And Eastern Europe. South East European Journal of Economics and Business, 9(1), 7-15. doi:doi:10.2478/jeb-2014- 0005

  • Predescu, O. M., & Stancu, S. (2011). Portfolio Risk Analysis using ARCH and GARCH models in the context of the global financial crisis. Theoretical Applied economics, 2(2), 75.

  • Rohatgi, V. K., & Saleh, A. M. E. (2015). An introduction to probability and statistics: John Wiley & Sons.

  • Schwert, G. W. (1990). Stock Market Volatility. Financial Analysts Journal, 46(3), 23-34. doi:10.2469/faj.v46.n3.23

  • Singhania, M., & Anchalia, J. (2013). Volatility in Asian stock markets and global financial crisis. Journal of Advances in Management Research, 10(3), 333-351. doi:10.1108/JAMR-01-2013-0010

  • Siourounis, G. D. (2002). Modelling volatility and testing for efficiency in emerging capital markets: the case of the Athens stock exchange. Applied Financial Economics, 12(1), 47-55. doi:10.1080/09603100110088003

  • Srinivasan, P. (2011). Modeling and forecasting the stock market volatility of S&P 500 index using GARCH models. IUP Journal of Behavioral Finance; Hyderabad, 8(1), 51-69.

  • Srinivasan, P., & Ibrahim, P. (2010). Forecasting Stock Market Volatility of Bse- 30 Index Using Garch Models. Asia Pacific Business Review, 6(3), 47-60. doi:10.1177/097324701000600304

  • Srivastava, A. (2008). Volatility of Indian Stock Market: An Emperical Evidence. Asia Pacific Business Review, 4(4), 53-61. doi:10.1177/097324700800400405

  • Stock, J. H., & Watson, M. W. (2004). Combination forecasts of output growth in a seven-country data set. Journal of Forecasting, 23(6), 405-430. doi:https://doi. org/10.1002/for.928

  • Subudhi, R.N. (2019), «Testing of Hypothesis: Concepts and Applications», Subudhi, R.N. and Mishra, S. (Ed.) Methodological Issues in Management Research: Advances, Challenges, and the Way Ahead, Emerald Publishing Limited. pp. 127-143. https://doi.org/10.1108/978-1-78973-973-220191009

  • Susruth, M. (2017). Application of GARCH Models to Forecast Financial Volatility of Daily Returns: An Empirical study on the Indian Stock Market. Asian Journal of Management, 8(2), 192-200. doi:http://dx.doi.org/10.5958/2321- 5763.2017.00030.0

  • Tripathy, T., & Gil-Alana Luis, A. (2015). Modelling time-varying volatility in the Indian stock returns :

  • some empirical evidence. Review of Development Finance, 5(2), 91-97. doi:10.1016/j. rdf.2015.04.002

  • Umar, M., Mirza, N., Rizvi, S. K. A., & Furqan, M. (2023). Asymmetric volatility structure of equity returns:

  • Evidence from an emerging market. The Quarterly Review of Economics and Finance, 87, 330-336. doi:https://doi.org/10.1016/j.qref.2021.04.016

  • Zivot, E. (2009). Practical Issues in the Analysis of Univariate GARCH Models. In T. Mikosch, J.-P. Kreiß, R. A. Davis, & T. G. Andersen (Eds.), Handbook of Financial Time Series (pp. 113-155). Berlin, Heidelberg: Springer Berlin Heidelberg.


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  • An Empirical Analysis of Volatility and Asymmetric Behaviour: Case of NSE and BSE

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Authors

Sunita
Assistant Professor, Department of Commerce, Govt. College Dujana, Jhajjar, Haryana-124102, India And Research Scolar at: School of Management & Commerce, K.R. Mangalam University, Gurugram, Haryana-122103, India
Anshika Prakash
School of Management & Commerce K.R. Mangalam University, Gurugram, Haryana-122103, India
Ritu Gupta
Assistant Professor Department of MBA, Maharaja Agarsen Institute of Technology (IP University), Rohini, Delhi-110086,, India

Abstract


The price, return and different events in stock market are uncertain but this uncertainty can provide insight for making investment decisions, if volatility is measured through appropriate model. In this research work, efforts are made to examine and compare the symmetric and asymmetric volatility in two major stock markets of India through the application of econometric models i.e. GARCH, TGARCH and EGARCH. Daily closing prices of NSE (Nifty-50) and BSE (Sensex) from 1st April 2010 to 31st March 2022 is used for the examination purpose. The results show that volatility in Indian market is persisted for a long time The asymmetric behaviour of volatility is also observed in Indian market. Findings are useful to design dynamic pricing, hedging and portfolio management strategies to all market participants.

Keywords


Stock Market Volatility, GARCH Models, Volatility clustering, TGARCH, EGARCH.

References





DOI: https://doi.org/10.23862/kiit-parikalpana%2F2023%2Fv19%2Fi2%2F223464