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Univariate and Multivariate GARCH Models Applied to the CARBS Indices

In: Advances in Panel Data Analysis in Applied Economic Research

Author

Listed:
  • Coenraad C. A. Labuschagne

    (University of Johannesburg)

  • Niel Oberholzer

    (University of Johannesburg)

  • Pierre J. Venter

    (University of Johannesburg)

Abstract

The purpose of this paper is to estimate the calibrated parameters of different univariate and multivariate generalised autoregressive conditional heteroskedasticity (GARCH) family models. It is unrealistic to assume that volatility of financial returns is constant. In the empirical analysis, the symmetric GARCH and asymmetric GJR-GARCH and EGARCH models were estimated for the CARBS (Canada, Australia, Russia, Brazil, and South Africa) indices and a global minimum variance portfolio (GMVP); the best fitting model was determined using the AIC and BIC. The asymmetric terms of the GJR-GARCH and EGARCH models indicate signs of the leverage effect. The information criterion suggests that the EGARCH model is the best fitting model for the CARBS indices and the GMVP.

Suggested Citation

  • Coenraad C. A. Labuschagne & Niel Oberholzer & Pierre J. Venter, 2018. "Univariate and Multivariate GARCH Models Applied to the CARBS Indices," Springer Proceedings in Business and Economics, in: Nicholas Tsounis & Aspasia Vlachvei (ed.), Advances in Panel Data Analysis in Applied Economic Research, chapter 0, pages 69-83, Springer.
    • Handle: RePEc:spr:prbchp:978-3-319-70055-7_6
    DOI: 10.1007/978-3-319-70055-7_6
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