Dynamic conditional correlation between green and grey energy ETF markets using cDCC-MGARCH model

ALGARHI, Amr Saber (2023). Dynamic conditional correlation between green and grey energy ETF markets using cDCC-MGARCH model. Applied Economics Letters.

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	1117 Public Health and Health Services; 1402 Applied Economics; 1502 Banking, Finance and Investment; Economics; Finance; 3502 Banking, finance and investment; 3801 Applied economics; 3899 Other economics
Identification Number:	https://doi.org/10.1080/13504851.2023.2289896
SWORD Depositor:	Symplectic Elements
Depositing User:	Symplectic Elements
Date Deposited:	14 Dec 2023 13:52
Last Modified:	14 Dec 2023 14:36
URI:	https://shura.shu.ac.uk/id/eprint/32881

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