Dynamic Conditional Correlation Between Green and Grey ETF Markets Using cDCC-MGARCH Model

19 Pages Posted: 30 Nov 2023 Last revised: 8 Dec 2023

Date Written: October 3, 2023


Using the cDCC form of the multivariate GARCH models (MGARCH), this paper examines the time-varying conditional correlations among green renewable, grey non-renewable, and the conventional investment strategy in the exchange-traded funds (ETFs) markets. Daily excess returns for the largest energy ETFs are used as proxies for the US energy sector over the period of 25 June 2008 to 9 May 2023. The empirical results find that the AR(1)-GARCH(1, 1)-cDCC model with t-distribution to be the best fit. The results indicate that the time-varying correlations between green and grey energy ETFs are between 0.42 and 0.55 and statistically significant at 10%, with lesser degree of persistence in green energy, while there is a high significant co-movement between the grey energy and the traditional investment strategy. This, in turn, implies that investing in green energy ETFs provides better diversification. These results provide important implications for policymakers, portfolio managers and investors on the benefits of portfolio diversification in energy markets amid the current global energy crisis.

Keywords: cDCC-MGARCH, energy sector, exchange-traded funds, oil market

JEL Classification: C32, G10, Q42

Suggested Citation

Algarhi, Amr Saber, Dynamic Conditional Correlation Between Green and Grey ETF Markets Using cDCC-MGARCH Model (October 3, 2023). Available at SSRN: https://ssrn.com/abstract=4632706 or http://dx.doi.org/10.2139/ssrn.4632706

Amr Saber Algarhi (Contact Author)

Sheffield Hallam University ( email )

City Campus, Pond Street
Sheffield, S1 1WB
United Kingdom

Do you have negative results from your research you’d like to share?

Paper statistics

Abstract Views
PlumX Metrics