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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
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    References listed on IDEAS

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    1. Chava, Sudheer & Purnanandam, Amiyatosh, 2010. "CEOs versus CFOs: Incentives and corporate policies," Journal of Financial Economics, Elsevier, vol. 97(2), pages 263-278, August.
    2. 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.
    3. 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.
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    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.

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