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A note on the estimated GARCH coefficients from the S&P1500 universe

Author

Listed:
  • Georgios Bampinas
  • Konstantinos Ladopoulos
  • Theodore Panagiotidis

Abstract

We employ 1440 stocks listed in the S&P Composite 1500 Index of the NYSE. Three benchmark GARCH models are estimated for the returns of each individual stock under three alternative distributions (Normal, t and GED). We provide summary statistics for all the GARCH coefficients derived from 11,520 regressions. The EGARCH model with GED errors emerges as the preferred choice for the individual stocks in the S&P 1500 universe when non-negativity and stationarity constraints in the conditional variance are imposed. 57% of the constraint’s violations are taking place in the S&P small cap stocks.

Suggested Citation

  • Georgios Bampinas & Konstantinos Ladopoulos & Theodore Panagiotidis, 2018. "A note on the estimated GARCH coefficients from the S&P1500 universe," Applied Economics, Taylor & Francis Journals, vol. 50(34-35), pages 3647-3653, July.
    • Handle: RePEc:taf:applec:v:50:y:2018:i:34-35:p:3647-3653
    DOI: 10.1080/00036846.2018.1436155
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    Blog mentions

    As found by EconAcademics.org, the blog aggregator for Economics research:
    1. Here's What I've Been Reading
      by Dave Giles in Econometrics Beat: Dave Giles' Blog on 2017-05-05 18:37:00

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    Cited by:

    1. Chatzitzisi, Evanthia & Fountas, Stilianos & Panagiotidis, Theodore, 2021. "Another look at calendar anomalies," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 823-840.
    2. repec:rim:rimwps:18-13 is not listed on IDEAS
    3. Abadir, Karim M. & Luati, Alessandra & Paruolo, Paolo, 2023. "GARCH density and functional forecasts," Journal of Econometrics, Elsevier, vol. 235(2), pages 470-483.

    More about this item

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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