IDEAS home Printed from https://ideas.repec.org/a/sae/globus/v12y2011i2p319-329.html
   My bibliography  Save this article

Non Linearity and Heteroskedasticity Effect on Stock Returns Volatility

Author

Listed:
  • Rakesh Kumar

    (Rakesh Kumar is Assistant Professor in Department of Business Studies, Deen Dayal Upadhyaya College, University of Delhi, New Delhi, India. E-mail: saini_rakeshindia@yahoo.co.in)

  • Raj S. Dhankar

    (Raj S. Dhankar is Professor of Finance and Dean, Faculty of Management Studies, University of Delhi, New Delhi, India, and was Visiting Professor, Faculty of Business Administration, Lakehead University, Ontario, Canada, while this study was carried out. E-mail: raj_sdhankar@rediffmail.com)

Abstract

This article investigates the asymmetric nature of US stock market return and effect of heteroskedasticity on stock return volatility. Further, this study also analyzes the relationship between stock returns and conditional volatility, and standard residuals. The monthly opening and closing prices of S & P 500 are used for the period from January 1950 to December 2007. The study applies Ljung-Box statistics to examine the autocorrelation in stock returns and GARCH (1, 1) and TAR-GARCH (1, 1) to test the heteroscedasticy, and asymmetric nature of stock returns respectively. The results of the study suggest the presence of non linearity, heteroskedasticity effect and asymmetric nature of stock returns. It also finds no correlation between stock returns and conditional volatility, however, the relationship between stock returns and standardized residuals is found positively significant. These findings bring out the essential elements of the modern investment theory that investors adjust their investment decisions with respect to expected volatility, however, they tend to earn extra risk premium for unexpected volatility. This study provides a robustness test of the conditional volatility and asymmetric impact of good and bad news.

Suggested Citation

  • Rakesh Kumar & Raj S. Dhankar, 2011. "Non Linearity and Heteroskedasticity Effect on Stock Returns Volatility," Global Business Review, International Management Institute, vol. 12(2), pages 319-329, June.
    • Handle: RePEc:sae:globus:v:12:y:2011:i:2:p:319-329
    DOI: 10.1177/097215091101200209
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/097215091101200209
    Download Restriction: no
    File URL: https://libkey.io/10.1177/097215091101200209?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Sentana, Enrique & Wadhwani, Sushil B, 1992. "Feedback Traders and Stock Return Autocorrelations: Evidence from a Century of Daily Data," Economic Journal, Royal Economic Society, vol. 102(411), pages 415-425, March.
    2. Campbell, John Y. & Hentschel, Ludger, 1992. "No news is good news *1: An asymmetric model of changing volatility in stock returns," Journal of Financial Economics, Elsevier, vol. 31(3), pages 281-318, June.
    3. Turner, Christopher M. & Startz, Richard & Nelson, Charles R., 1989. "A Markov model of heteroskedasticity, risk, and learning in the stock market," Journal of Financial Economics, Elsevier, vol. 25(1), pages 3-22, November.
    4. Aggarwal, Reena & Inclan, Carla & Leal, Ricardo, 1999. "Volatility in Emerging Stock Markets," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(1), pages 33-55, March.
    5. 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.
    6. Engle, Robert F & Ng, Victor K, 1993. "Measuring and Testing the Impact of News on Volatility," Journal of Finance, American Finance Association, vol. 48(5), pages 1749-1778, December.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Morgan, I G, 1976. "Stock Prices and Heteroscedasticity," The Journal of Business, University of Chicago Press, vol. 49(4), pages 496-508, October.
    9. Chiang, Thomas C & Doong, Shuh-Chyi, 2001. "Empirical Analysis of Stock Returns and Volatility: Evidence from Seven Asian Stock Markets Based on TAR-GARCH Model," Review of Quantitative Finance and Accounting, Springer, vol. 17(3), pages 301-318, November.
    10. 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.
    11. Toshiaki Watanabe, 2002. "Margin requirements, positive feedback trading, and stock return autocorrelations: the case of Japan," Applied Financial Economics, Taylor & Francis Journals, vol. 12(6), pages 395-403.
    12. 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.
    13. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    14. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    15. French, Kenneth R. & Schwert, G. William & Stambaugh, Robert F., 1987. "Expected stock returns and volatility," Journal of Financial Economics, Elsevier, vol. 19(1), pages 3-29, September.
    16. Robert W. Faff & Michael D. McKenzie, 2007. "The relationship between implied volatility and autocorrelation," International Journal of Managerial Finance, Emerald Group Publishing Limited, vol. 3(2), pages 191-196, April.
    17. 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)

    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. Rakesh Kumar & Raj S. Dhankar, 2010. "Empirical Analysis of Conditional Heteroskedasticity in Time Series of Stock Returns and Asymmetric Effect on Volatility," Global Business Review, International Management Institute, vol. 11(1), pages 21-33, January.
    2. Thomas C. Chiang & Cathy W.S. Chen & Mike K.P. So, 2007. "Asymmetric Return and Volatility Responses to Composite News from Stock Markets," Multinational Finance Journal, Multinational Finance Journal, vol. 11(3-4), pages 179-210, September.
    3. Donaldson, R. Glen & Kamstra, Mark, 1997. "An artificial neural network-GARCH model for international stock return volatility," Journal of Empirical Finance, Elsevier, vol. 4(1), pages 17-46, January.
    4. 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.
    5. 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.
    6. Wu, Guojun & Xiao, Zhijie, 2002. "A generalized partially linear model of asymmetric volatility," Journal of Empirical Finance, Elsevier, vol. 9(3), pages 287-319, August.
    7. Haque Mahfuzul & Hassan M. Kabir & Maroney Neal C & Sackley William H, 2004. "An Empirical Examination of Stability, Predictability, and Volatility of Middle Eastern and African Emerging Stock Markets," Review of Middle East Economics and Finance, De Gruyter, vol. 2(1), pages 18-41, April.
    8. Kim, Dongcheol & Kon, Stanley J., 1999. "Structural change and time dependence in models of stock returns," Journal of Empirical Finance, Elsevier, vol. 6(3), pages 283-308, September.
    9. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521770415.
    10. Sei‐Wan Kim & Bong‐Soo Lee, 2008. "Stock Returns, Asymmetric Volatility, Risk Aversion, And Business Cycle: Some New Evidence," Economic Inquiry, Western Economic Association International, vol. 46(2), pages 131-148, April.
    11. 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.
    12. Yueh-Neng Lin & Ken Hung, 2008. "Is Volatility Priced?," Annals of Economics and Finance, Society for AEF, vol. 9(1), pages 39-75, May.
    13. Franses,Philip Hans & Dijk,Dick van, 2000. "Non-Linear Time Series Models in Empirical Finance," Cambridge Books, Cambridge University Press, number 9780521779654.
    14. Shekar Bose & Hafizur Rahman, 2022. "Are News Effects Necessarily Asymmetric? Evidence from Bangladesh Stock Market," SAGE Open, , vol. 12(4), pages 21582440221, October.
    15. Nam, Kiseok & Pyun, Chong Soo & Avard, Stephen L., 2001. "Asymmetric reverting behavior of short-horizon stock returns: An evidence of stock market overreaction," Journal of Banking & Finance, Elsevier, vol. 25(4), pages 807-824, April.
    16. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    17. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2005. "Volatility forecasting," CFS Working Paper Series 2005/08, Center for Financial Studies (CFS).
    18. Halil Kiymaz & Hakan Berument, 2003. "The day of the week effect on stock market volatility and volume: International evidence," Review of Financial Economics, John Wiley & Sons, vol. 12(4), pages 363-380.
    19. 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.
    20. Venetis, Ioannis A. & Peel, David, 2005. "Non-linearity in stock index returns: the volatility and serial correlation relationship," Economic Modelling, Elsevier, vol. 22(1), pages 1-19, January.

    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:sae:globus:v:12:y:2011:i:2:p:319-329. 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: SAGE Publications (email available below). General contact details of provider: http://www.imi.edu/ .

    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.