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Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100

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  • Aliyev, Fuzuli
  • Ajayi, Richard
  • Gasim, Nijat

Abstract

This paper models and estimates the volatility of nonfinancial, innovative and hi-tech focused stock index, the Nasdaq-100, using univariate asymmetric GARCH models. We employ EGARCH and GJR-GARCH using daily data over the period January 4, 2000 through March 19, 2019. We find that the volatility shocks on the index returns are quite persistent. Furthermore, our findings show that the index has leverage effect, and the impact of shocks is asymmetric, whereby the impacts of negative shocks on volatility are higher than those of positive shocks of the same magnitude. The financial implication of the findings for investors is that Nasdaq-100 index returns’ volatility exhibits clustering, and this permits investors to establish future positions in expectation of this characteristic.

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  • Aliyev, Fuzuli & Ajayi, Richard & Gasim, Nijat, 2020. "Modelling asymmetric market volatility with univariate GARCH models: Evidence from Nasdaq-100," The Journal of Economic Asymmetries, Elsevier, vol. 22(C).
    • Handle: RePEc:eee:joecas:v:22:y:2020:i:c:s1703494920300141
    DOI: 10.1016/j.jeca.2020.e00167
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    as
    1. Paresh Kumar Narayan, 2005. "Are the Australian and New Zealand stock prices nonlinear with a unit root?," Applied Economics, Taylor & Francis Journals, vol. 37(18), pages 2161-2166.
    2. Terui, Nobuhiko & van Dijk, Herman K., 2002. "Combined forecasts from linear and nonlinear time series models," International Journal of Forecasting, Elsevier, vol. 18(3), pages 421-438.
    3. Chu, Carlin C.F. & Lam, K.P., 2011. "Modeling intraday volatility: A new consideration," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 388-418, July.
    4. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    5. Brooks,Chris, 2008. "RATS Handbook to Accompany Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9780521896955, January.
    6. Lu, Yang K. & Perron, Pierre, 2010. "Modeling and forecasting stock return volatility using a random level shift model," Journal of Empirical Finance, Elsevier, vol. 17(1), pages 138-156, January.
    7. William Schwert, G., 2002. "Stock volatility in the new millennium: how wacky is Nasdaq?," Journal of Monetary Economics, Elsevier, vol. 49(1), pages 3-26, January.
    8. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    9. Aye, Goodness C. & Balcilar, Mehmet & Demirer, Riza & Gupta, Rangan, 2018. "Firm-level political risk and asymmetric volatility," The Journal of Economic Asymmetries, Elsevier, vol. 18(C), pages 1-1.
    10. Rahman, Shafiqur & Lee, Cheng-few & Ang, Kian Ping, 2002. "Intraday Return Volatility Process: Evidence from NASDAQ Stocks," Review of Quantitative Finance and Accounting, Springer, vol. 19(2), pages 155-180, September.
    11. 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.
    12. Andersen, Torben G. & Bollerslev, Tim, 1997. "Intraday periodicity and volatility persistence in financial markets," Journal of Empirical Finance, Elsevier, vol. 4(2-3), pages 115-158, June.
    13. Guglielmo Maria Caporale & Luis A. Gil‐Alana, 2012. "Estimating persistence in the volatility of asset returns with signal plus noise models," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 17(1), pages 23-30, January.
    14. Chen, Chun-Hung & Yu, Wei-Choun & Zivot, Eric, 2012. "Predicting stock volatility using after-hours information: Evidence from the NASDAQ actively traded stocks," International Journal of Forecasting, Elsevier, vol. 28(2), pages 366-383.
    15. Cizeau, Pierre & Liu, Yanhui & Meyer, Martin & Peng, C.-K. & Eugene Stanley, H., 1997. "Volatility distribution in the S&P500 stock index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 245(3), pages 441-445.
    16. 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.
    17. 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.
    18. García-Centeno, María del Carmen & Fernández-Avilés, Gema & Montero, José María, 2010. "Asymmetries in the Volatility of Precious Metals Returns: The TA-ARSV Modelling Strategy," The Journal of Economic Asymmetries, Elsevier, vol. 7(1), pages 23-41.
    19. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    20. Enders, Walter & Lee, Junsoo, 2012. "The flexible Fourier form and Dickey–Fuller type unit root tests," Economics Letters, Elsevier, vol. 117(1), pages 196-199.
    21. Andersen, Torben G. & Bollerslev, Tim & Diebold, Francis X. & Ebens, Heiko, 2001. "The distribution of realized stock return volatility," Journal of Financial Economics, Elsevier, vol. 61(1), pages 43-76, July.
    22. Eric Zivot, 2008. "Practical Issues in the Analysis of Univariate GARCH Models," Working Papers UWEC-2008-03-FC, University of Washington, Department of Economics.
    23. Fuzuli Aliyev, 2019. "Testing Market Efficiency with Nonlinear Methods: Evidence from Borsa Istanbul," IJFS, MDPI, vol. 7(2), pages 1-11, June.
    24. Erginbay Ugurlu & Eleftherios Thalassinos & Yusuf Muratoglu, 2014. "Modeling Volatility in the Stock Markets using GARCH Models: European Emerging Economies and Turkey," International Journal of Economics & Business Administration (IJEBA), International Journal of Economics & Business Administration (IJEBA), vol. 0(3), pages 72-87.
    25. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    26. John D. Levendis, 2018. "Time Series Econometrics," Springer Texts in Business and Economics, Springer, number 978-3-319-98282-3, August.
    27. Chris Brooks & Simon Burke, 2003. "Information criteria for GARCH model selection," The European Journal of Finance, Taylor & Francis Journals, vol. 9(6), pages 557-580.
    28. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    29. Pierre Cizeau & Yanhui Liu & Martin Meyer & C. -K. Peng & H. Eugene Stanley, 1997. "Volatility distribution in the S&P500 Stock Index," Papers cond-mat/9708143, arXiv.org.
    30. 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.
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    4. B M, Lithin & chakraborty, Suman & iyer, Vishwanathan & M N, Nikhil & ledwani, Sanket, 2022. "Modeling asymmetric sovereign bond yield volatility with univariate GARCH models: Evidence from India," MPRA Paper 117067, University Library of Munich, Germany, revised 05 Jan 2023.
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