Sequential management of energy and low-carbon portfolios

• Published in: Research in international business and finance 2024-04, Vol.69, p.102263, Article 102263
• Main Authors: Gargallo, Pilar, Lample, Luis, Miguel, Jesús A., Salvador, Manuel
• Format: Article
• Language: English
• Subjects: ADCC-GARCH; Clean energy; EU ETS; Portfolio selection; Risk management; Sequential analysis
• ISSN: 0275-5319; 1878-3384
• DOI: 10.1016/j.ribaf.2024.102263

This study explores the ability of clean energy and European Union Allowance (EUA) assets to diminish portfolio risk when mixed with unclean energy assets. We use a family of Asymmetric Dynamic Conditional Correlation-Generalized AutoRegressive Conditional Heteroskedastic (ADCC-GARCH) models and provide a flexible and adaptive estimation and model selection framework based on a sequential strategy with differently sized estimation and validation windows, as well as different model update frequencies. Through this procedure, we obtain accurate estimations of the conditional covariance matrices of day-to-day asset returns and build adequate optimal minimum variance portfolios. The analyzed period (Jan. 2010–May. 2022) includes the latest crisis episodes (Sovereign debt crisis, Brexit, COVID-19, and the Russian–Ukrainian war). Our findings show that since the 2015 Paris Agreement (the only exception being the pandemic period), investing in clean energy companies and EUAs is an attractive investment in terms of return-risk. These results should provide investors with more incentives to decarbonize their portfolios. [Display omitted] •Sequential methodology to dynamically manage the risk of an investment portfolio.•Possibility that the model used to estimate the covariance matrices might change.•Different estimation and validation window sizes and model updating frequencies.•Quarterly updating is the best strategy and investing in renewables is worthwhile.•Despite Russo-Ukrainian war, low-carbon portfolios remain less risky..