Univariate and Multivariate GARCH Models Applied to the CARBS Indices

Coenraad C. A. Labuschagne (ccalabuschagne@uj.ac.za), Niel Oberholzer and Pierre J. Venter (pventer@uj.ac.za)
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Coenraad C. A. Labuschagne: University of Johannesburg
Niel Oberholzer: University of Johannesburg
Pierre J. Venter: University of Johannesburg

Chapter Chapter 6 in Advances in Panel Data Analysis in Applied Economic Research, 2018, pp 69-83 from Springer

Abstract: 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.

Keywords: Generalized Autoregressive Conditional Heteroskedasticity (GARCH); Multivariate GARCH; GARCH Models; Global Minimum Variance Portfolio (GMVP); EGARCH Model (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-319-70055-7_6

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DOI: 10.1007/978-3-319-70055-7_6

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