IDEAS home Printed from https://ideas.repec.org/a/ddj/fseeai/y2024i1p16-29.html
   My bibliography  Save this article

Forecasting Volatility Spillovers Using Advanced GARCH Models: Empirical Evidence for Developed Stock Markets from Austria and USA

Author

Listed:
  • Bharat Kumar Meher

    (PG Department of Commerce, Purnea University, Purnia, Bihar, India)

  • Puja Kumari

    (Department of Commerce and Business Management, Ranchi University, Ranchi, India)

  • Ramona Birau

    (Faculty of Economic Science, University Constantin Brancusi of Tg-Jiu, Romania)

  • Cristi Spulbar

    (University of Craiova, Faculty of Economics and Business Administration, Craiova, Romania)

  • Abhishek Anand

    (PG Department of Economics, Purnea University, Purnia, Bihar, India)

  • Ion Florescu

    (University of Craiova, "Eugeniu Carada" Doctoral School of Economic Sciences, Craiova, Romania)

Abstract

The research study voyage commences with the foundational objective of fitting a suitable Generalized Autoregressive Conditional Heteroscedastic (GARCH) model to assess market volatility, a fundamental pillar of financial analysis. This research embarks on an ambitious quest to predict and understand stock market volatility within the realms of the DJIA and S&P 500 of USA and ATX index of Austria using different sophisticated GARCH models. The dataset used in this study comprises daily stock market data for two key indices: the S&P 500 Index, representing the USA stock market, and the ATX Index, representing the Austria stock market. Additionally, the DJIA Index, another representative of the USA stock market, was included. The dataset consists of 5967 daily observations over the specified time period from January 3, 2000, to September 21, 2023. The observation of results, analysis and discussion depicts that PARCH model shows most promising results and found suitable to model the volatility patterns of the selected indices. The findings and methodologies presented in this paper can be seen as a solid foundation upon which to build future investigations, refining our ability to anticipate market movements and make informed decisions in an uncertain financial landscape. In closing, this research not only contributes to the body of knowledge in financial econometrics but also underscores the importance of modeling long-term stock market behavior with precision and diligence.

Suggested Citation

  • Bharat Kumar Meher & Puja Kumari & Ramona Birau & Cristi Spulbar & Abhishek Anand & Ion Florescu, 2024. "Forecasting Volatility Spillovers Using Advanced GARCH Models: Empirical Evidence for Developed Stock Markets from Austria and USA," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 1, pages 16-29.
    • Handle: RePEc:ddj:fseeai:y:2024:i:1:p:16-29
    DOI: 10.35219/eai15840409383
    as

    Download full text from publisher

    File URL: http://eia.feaa.ugal.ro/images/eia/2024_1/Meher%20et%20al.pdf
    Download Restriction: no
    File URL: https://libkey.io/10.35219/eai15840409383?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. Luc Bauwens & Sébastien Laurent & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109, January.
    2. Neenu Chalissery & Suhaib Anagreh & Mohamed Nishad T. & Mosab I. Tabash, 2022. "Mapping the Trend, Application and Forecasting Performance of Asymmetric GARCH Models: A Review Based on Bibliometric Analysis," JRFM, MDPI, vol. 15(9), pages 1-23, September.
    3. Chava, Sudheer & Purnanandam, Amiyatosh, 2010. "CEOs versus CFOs: Incentives and corporate policies," Journal of Financial Economics, Elsevier, vol. 97(2), pages 263-278, August.
    4. Bonga, Wellington Garikai, 2019. "Stock Market Volatility Analysis using GARCH Family Models: Evidence from Zimbabwe Stock Exchange," MPRA Paper 94201, University Library of Munich, Germany.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Shreevastava Aman & Raza Shahil & Bharat Kumar Meher & Ramona Birau & Anand Abhishek & Mircea Laurentiu Simion & Nadia Tudora Cirjan, 2024. "Exploring Advanced GARCH Models for Analyzing Asymmetric Volatility Dynamics for the Emerging Stock Market in Hungary: An Empirical Case Study," Economics and Applied Informatics, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, issue 2, pages 41-52.

    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. Cui, Xueting & Zhu, Shushang & Sun, Xiaoling & Li, Duan, 2013. "Nonlinear portfolio selection using approximate parametric Value-at-Risk," Journal of Banking & Finance, Elsevier, vol. 37(6), pages 2124-2139.
    2. Fogel, Kathy & Jandik, Tomas & McCumber, William R., 2018. "CFO social capital and private debt," Journal of Corporate Finance, Elsevier, vol. 52(C), pages 28-52.
    3. Joanna Olbrys, 2013. "Asymmetric impact of innovations on volatility in the case of the US and CEEC-3 markets: EGARCH based approach," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 13, pages 33-50.
    4. Cavit Pakel & Neil Shephard & Kevin Sheppard, 2009. "Nuisance parameters, composite likelihoods and a panel of GARCH models," Economics Papers 2009-W12, Economics Group, Nuffield College, University of Oxford.
    5. Bauwens, Luc & Xu, Yongdeng, 2023. "DCC- and DECO-HEAVY: Multivariate GARCH models based on realized variances and correlations," International Journal of Forecasting, Elsevier, vol. 39(2), pages 938-955.
    6. Piotr Fiszeder, 2011. "Minimum Variance Portfolio Selection for Large Number of Stocks – Application of Time-Varying Covariance Matrices," Dynamic Econometric Models, Uniwersytet Mikolaja Kopernika, vol. 11, pages 87-98.
    7. Julyerme M. Tonin & Carlos M. R. Vieira & Rui M. de Sousa Fragoso & João G. Martines Filho, 2020. "Conditional correlation and volatility between spot and futures markets for soybean and corn," Agribusiness, John Wiley & Sons, Ltd., vol. 36(4), pages 707-724, October.
    8. Ulrike Malmendier & Vincenzo Pezone & Hui Zheng, 2023. "Managerial Duties and Managerial Biases," Management Science, INFORMS, vol. 69(6), pages 3174-3201, June.
    9. Pedersen, Rasmus Søndergaard, 2016. "Targeting Estimation Of Ccc-Garch Models With Infinite Fourth Moments," Econometric Theory, Cambridge University Press, vol. 32(2), pages 498-531, April.
    10. João Caldeira & Guilherme Moura & André A.P. Santos, 2012. "Portfolio optimization using a parsimonious multivariate GARCH model: application to the Brazilian stock market," Economics Bulletin, AccessEcon, vol. 32(3), pages 1848-1857.
    11. Guochang Wang & Wai Keung Li & Ke Zhu, 2018. "New HSIC-based tests for independence between two stationary multivariate time series," Papers 1804.09866, arXiv.org.
    12. Sudip Datta & Mai Iskandar-Datta, 2014. "Upper-echelon executive human capital and compensation: Generalist vs specialist skills," Strategic Management Journal, Wiley Blackwell, vol. 35(12), pages 1853-1866, December.
    13. Chen Tong & Peter Reinhard Hansen & Ilya Archakov, 2024. "Cluster GARCH," Papers 2406.06860, arXiv.org.
    14. Cavit Pakel & Neil Shephard & Kevin Sheppard & Robert F. Engle, 2021. "Fitting Vast Dimensional Time-Varying Covariance Models," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 39(3), pages 652-668, July.
    15. Cristiana Tudor & Aura Girlovan & Gabriel Robert Saiu & Daniel Dumitru Guse, 2025. "Asymmetric Shocks and Pension Fund Volatility: A GARCH Approach with Macroeconomic Predictors to an Unexplored Emerging Market," Mathematics, MDPI, vol. 13(7), pages 1-29, March.
    16. SILVESTRINI, Andrea & VEREDAS, David, 2005. "Temporal aggregation of univariate linear time series models," LIDAM Discussion Papers CORE 2005059, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    17. Chamizo, Álvaro & Novales, Alfonso, 2021. "Evaluation of market risk associated with hedging a credit derivative portfolio," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 411-430.
    18. Santos, André A.P. & Nogales, Francisco J. & Ruiz, Esther & Dijk, Dick Van, 2012. "Optimal portfolios with minimum capital requirements," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 1928-1942.
    19. Yu-Pin Hu & Ruey S. Tsay, 2014. "Principal Volatility Component Analysis," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 32(2), pages 153-164, April.
    20. Asai, Manabu & McAleer, Michael, 2015. "Leverage and feedback effects on multifactor Wishart stochastic volatility for option pricing," Journal of Econometrics, Elsevier, vol. 187(2), pages 436-446.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:ddj:fseeai:y:2024:i:1:p:16-29. 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: Gianina Mihai (email available below). General contact details of provider: https://edirc.repec.org/data/fegalro.html .

    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.