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Abstract
1- Introduction
2- Theoretical background
3- Database
4- Empirical results and robustness test
5- Conclusions
References
Abstract
Fossil fuels supply most of the energy we need for many functions but alternative energy global consumption is expected to increase in the future supported by great incentives, advances in technologies, and the depletion of fuel oil reserves. In that context, investors begin to consider the possibility of investing in the alternative energy sector using different assets such as the Exchange Traded Funds (ETFs). We evaluate the out-of-sample performance of four strategies using the returns and volatility forecasts from a VAR-ADCC approach. We provide evidence that Alternative Energy ETFs clearly outperform Energy ETFs and, therefore, they are a real alternative for investors. These findings are relevant not only for academics but also for active professional managers who can use this technique to add value to their investment strategies.
Introduction
Recent years have been characterized by significant increases and decreases of oil and gas prices. That volatile behavior, jointly with some movements in favor of reducing economic dependences on fossil fuels, leads investors to consider the overweight of the Alternative Energy sector, which encompasses wind, solar, geothermal, biomass, biofuels, hydro, wave and tidal energies, on their portfolios. This sector is expected to experiment a fast growth over the next decades due to the depletion of fossil fuel reserves, greater incentives for renewables and the advances in technologies which have made green power more feasible. From the analysis of the previous empirical literature, we find two ways of analyzing energy markets. Firstly, we observe that several studies have mainly focused on using different multivariate volatility models to calculate optimal hedge ratios and optimal portfolio weights that minimize risk between two assets, such as crude oil spots and futures (Chang et al., 2011), or between oil prices and the stock prices of clean energy and technology companies (Sadorsky, 2012), but also to observe spillovers and interactions commonly between pairs of energy markets or energy and other markets, see Henriques and Sadorsky (2008), Mollick and Assefa (2013), Efimova and Serletis (2014), Lin et al. (2014), and more recently Maghyereh et al. (2017) and Kyritsis and Serletis (2018). Their results reveal different dynamic correlations and hedge ratios and suggest that a better understanding of volatility links is crucial for portfolio management. Secondly, studies related to alternative energies, see Silva and Cortez (2016), Reboredo et al. (2017) and Rezec and Scholtens (2017), use different linear regression models based on excess returns and additional factors like those proposed by Fama and French (1993) or risk factors used by Carhart (1996), Bollen and Busse (2001) and Inchauspe et al. (2015) in order to analyze the performance of renewable energies. All of them suggest that renewable energies underperform the respective benchmarks and, therefore, they are not a financially attractive portfolio investment.