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Estimating Volatility Clustering Using Gjr-Garch Model: A Case Study For German Stock Market

Author

Listed:
  • RACHANA BAID

    (NATIONAL INSTITUTE OF SECURITIES MARKETS, INDIA)

  • CRISTI SPULBAR

    (FACULTY OF ECONOMICS AND BUSINESS ADMINISTRATION, UNIVERSITY OF CRAIOVA, CRAIOVA, ROMANIA)

  • JATIN TRIVEDI

    (NATIONAL INSTITUTE OF SECURITIES MARKETS, INDIA)

  • RAMONA BIRAU

    (FACULTY OF ECONOMIC SCIENCE, UNIVERSITY CONSTANTIN BRANCUSI, TG-JIU, ROMANIA)

  • ANCA IOANA IACOB (TROTO)

    (UNIVERSITY OF CRAIOVA, DOCTORAL SCHOOL OF ECONOMIC SCIENCES, CRAIOVA, ROMANIA)

Abstract

The purpose of this article is to concentrate on the stylized data in the financial series of the major index DAX of the German stock market. Moreover, we investigated the effects of positive and negative news on the volatility of the stock market of Germany, such as DAX index. One of the most fascinating topics for investor research is the financial market volatility of an emerging financial market. Because of this, factorial risks and the likelihood of larger returns are increased. We take into account daily OBS (observations) in the number of 4037 for the sample period January 2007 to November 2022. The study used the GJR-GARCH, or Generalized Autoregressive Conditional Heteroskedisticity type model. We discovered that the DAX index financial series feature a dynamic volatility scale. The GJR-GARCH model was fitted and the stronger impact of innovations was discovered.

Suggested Citation

  • Rachana Baid & Cristi Spulbar & Jatin Trivedi & Ramona Birau & Anca Ioana Iacob (Troto), 2022. "Estimating Volatility Clustering Using Gjr-Garch Model: A Case Study For German Stock Market," Annals - Economy Series, Constantin Brancusi University, Faculty of Economics, vol. 6, pages 4-10, December.
    • Handle: RePEc:cbu:jrnlec:y:2022:v:6:p:4-10
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    References listed on IDEAS

    as
    1. Kaan Celebi & Michaela Hönig, 2019. "The Impact of Macroeconomic Factors on the German Stock Market: Evidence for the Crisis, Pre- and Post-Crisis Periods," IJFS, MDPI, vol. 7(2), pages 1-13, March.
    2. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    3. Oussama Tilfani & Paulo Ferreira & Andreia Dionisio & My Youssef El Boukfaoui, 2020. "EU Stock Markets vs. Germany, UK and US: Analysis of Dynamic Comovements Using Time-Varying DCCA Correlation Coefficients," JRFM, MDPI, vol. 13(5), pages 1-23, May.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    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, 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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