Univariate and Multivariate GARCH Models Applied to the CARBS Indices

  • Coenraad C. A. Labuschagne
  • Niel Oberholzer
  • Pierre J. Venter
Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

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.

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Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Coenraad C. A. Labuschagne
    • 1
  • Niel Oberholzer
    • 1
  • Pierre J. Venter
    • 1
  1. 1.Department of Finance and Investment ManagementUniversity of JohannesburgJohannesburgSouth Africa

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