IDEAS home Printed from https://ideas.repec.org/a/gam/jrisks/v12y2024i5p76-d1388278.html
   My bibliography  Save this article

Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100

Author

Listed:
  • Elchin Suleymanov

    (National Observatory on Labour Market and Social Protection Affairs, Baku AZ1005, Azerbaijan
    Department of Finance, Baku Engineering University, Khirdalan AZ0101, Azerbaijan)

  • Magsud Gubadli

    (Department of Finance, Baku Engineering University, Khirdalan AZ0101, Azerbaijan
    School of Business, Khazar University, Baku AZ1005, Azerbaijan)

  • Ulvi Yagubov

    (Department of Finance, Baku Engineering University, Khirdalan AZ0101, Azerbaijan
    School of Business, Azerbaijan State University of Economics, Baku AZ1005, Azerbaijan)

Abstract

The present study aimed to investigate the presence of asymmetric stochastic volatility and leverage effects within the Nasdaq-100 index. This index is widely regarded as an important indicator for investors. We focused on the nine leading stocks within the index, which are highly popular and hold significant weight in the investment world. These stocks are Netflix, PayPal, Google, Intel, Microsoft, Amazon, Tesla, Apple, and Meta. The study covered the period between 3 January 2017 and 30 January 2023, and we employed the EViews and WinBUGS applications to conduct the analysis. We began by calculating the logarithmic difference to obtain the return series. We then performed a sample test with 100,000 iterations, excluding the first 10,000 samples to eliminate the initial bias of the coefficients. This left us with 90,000 samples for analysis. Using the results of the asymmetric stochastic volatility model, we evaluated both the Nasdaq-100 index as a whole and the volatility persistence, predictability, and correlation levels of individual stocks. This allowed us to evaluate the ability of individual stocks to represent the characteristics of the Nasdaq-100 index. Our findings revealed a dense clustering of volatility, both for the Nasdaq-100 index and the nine individual stocks. We observed that this volatility is continuous but has a predictable impact on variability. Moreover, apart from Intel, all the stocks in the model exhibited both leverage effects and the presence of asymmetric relationships, as did the Nasdaq-100 index. Overall, our results show that the characteristics of stocks in the model are like the volatility characteristic of the Nasdaq-100 index and can represent it.

Suggested Citation

  • Elchin Suleymanov & Magsud Gubadli & Ulvi Yagubov, 2024. "Test of Volatile Behaviors with the Asymmetric Stochastic Volatility Model: An Implementation on Nasdaq-100," Risks, MDPI, vol. 12(5), pages 1-20, May.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:5:p:76-:d:1388278
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-9091/12/5/76/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-9091/12/5/76/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ishihara, Tsunehiro & Omori, Yasuhiro, 2012. "Efficient Bayesian estimation of a multivariate stochastic volatility model with cross leverage and heavy-tailed errors," Computational Statistics & Data Analysis, Elsevier, vol. 56(11), pages 3674-3689.
    2. Hentschel, Ludger, 1995. "All in the family Nesting symmetric and asymmetric GARCH models," Journal of Financial Economics, Elsevier, vol. 39(1), pages 71-104, September.
    3. Ding, Haoyuan & Chong, Terence Tai-leung & Park, Sung Y., 2014. "Nonlinear dependence between stock and real estate markets in China," Economics Letters, Elsevier, vol. 124(3), pages 526-529.
    4. Alexander, Carol & Lazar, Emese & Stanescu, Silvia, 2021. "Analytic moments for GJR-GARCH (1, 1) processes," International Journal of Forecasting, Elsevier, vol. 37(1), pages 105-124.
    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. Campbell, John Y & Kim, Sangjoon & Lettau, Martin, 1998. "Dispersion and Volatility in Stock Returns: An Empirical Investigation," CEPR Discussion Papers 1923, C.E.P.R. Discussion Papers.
    2. Asai, Manabu & McAleer, Michael, 2015. "Forecasting co-volatilities via factor models with asymmetry and long memory in realized covariance," Journal of Econometrics, Elsevier, vol. 189(2), pages 251-262.
    3. Manabu Asai & Michael McAleer, 2009. "Dynamic Conditional Correlations for Asymmetric Processes," CARF F-Series CARF-F-168, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    4. Changli He & Annastiina Silvennoinen & Timo Teräsvirta, 2008. "Parameterizing Unconditional Skewness in Models for Financial Time Series," Journal of Financial Econometrics, Oxford University Press, vol. 6(2), pages 208-230, Spring.
    5. Xuan Vinh Vo & Kevin Daly, 2008. "Volatility amongst firms in the Dow Jones Eurostoxx50 Index," Applied Financial Economics, Taylor & Francis Journals, vol. 18(7), pages 569-582.
    6. Chia, Ricky Chee-Jiun & Liew, Venus Khim-Sen & Syed Khalid Wafa, Syed Azizi Wafa, 2006. "Calendar anomalies in the Malaysian stock market," MPRA Paper 516, University Library of Munich, Germany.
    7. Manabu Asai & Michael McAleer, 2011. "Alternative Asymmetric Stochastic Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 30(5), pages 548-564, October.
    8. Christoffersen, Peter & Jacobs, Kris & Ornthanalai, Chayawat & Wang, Yintian, 2008. "Option valuation with long-run and short-run volatility components," Journal of Financial Economics, Elsevier, vol. 90(3), pages 272-297, December.
    9. Ishihara, Tsunehiro & Omori, Yasuhiro & Asai, Manabu, 2016. "Matrix exponential stochastic volatility with cross leverage," Computational Statistics & Data Analysis, Elsevier, vol. 100(C), pages 331-350.
    10. Tzu-Yi Yang & Yu-Tai Yang, 2015. "A Study on the Asymmetry of the News Aspect of the Stock Market: Evidence from Three Institutional Investors in the Taiwan Stock Market," Panoeconomicus, Savez ekonomista Vojvodine, Novi Sad, Serbia, vol. 62(3), pages 361-383, June.
    11. Nikolaos A. Kyriazis, 2021. "A Survey on Volatility Fluctuations in the Decentralized Cryptocurrency Financial Assets," JRFM, MDPI, vol. 14(7), pages 1-46, June.
    12. Su, Chi-Wei & Huang, Shi-Wen & Qin, Meng & Umar, Muhammad, 2021. "Does crude oil price stimulate economic policy uncertainty in BRICS?," Pacific-Basin Finance Journal, Elsevier, vol. 66(C).
    13. Fuenzalida, Darcy & Mongrut, Samuel & Arteaga, Jaime Raúl & Erausquin, Alexander, 2013. "Good corporate governance: Does it pay in Peru?," Journal of Business Research, Elsevier, vol. 66(10), pages 1759-1770.
    14. Sun, Yiguo & Stengos, Thanasis, 2006. "Semiparametric efficient adaptive estimation of asymmetric GARCH models," Journal of Econometrics, Elsevier, vol. 133(1), pages 373-386, July.
    15. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    16. Meitz, Mika & Saikkonen, Pentti, 2008. "Ergodicity, Mixing, And Existence Of Moments Of A Class Of Markov Models With Applications To Garch And Acd Models," Econometric Theory, Cambridge University Press, vol. 24(5), pages 1291-1320, October.
    17. 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.
    18. Chan-Lau, Jorge A. & Ivaschenko, Iryna, 2003. "Asian Flu or Wall Street virus? Tech and non-tech spillovers in the United States and Asia," Journal of Multinational Financial Management, Elsevier, vol. 13(4-5), pages 303-322, December.
    19. Dominique Guegan & Bertrand K. Hassani, 2019. "Risk Measurement," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-02119256, HAL.
    20. Sofia Anyfantaki & Antonis Demos, 2016. "Estimation and Properties of a Time-Varying EGARCH(1,1) in Mean Model," Econometric Reviews, Taylor & Francis Journals, vol. 35(2), pages 293-310, February.

    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:gam:jrisks:v:12:y:2024:i:5:p:76-:d:1388278. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.