Uncertainty and Stock Returns in Energy Markets: A Quantile Regression Approach
Samir Cedic (Linköping University); Alwan Mahmoud (Linköping University); Matteo Manera (University of Milano-Bicocca, Fondazione Eni Enrico Mattei); Gazi Salah Uddin (Linköping University)
C1, G15, Q2, Q3, Q43
Uncertainty, Macroeconomic Conditions, Renewable Energy, Stock Returns, Quantile Regression
The aim of this paper is to analyze the relationship between different types of uncertainty and stock returns of the renewable energy and the oil & gas sectors. We use the quantile regression approach developed by Koenker and d’Orey (1987; 1994) to assess which uncertainties are the potential drivers of stock returns under different market conditions. We find that the bioenergy and the oil & gas sectors are most sensitive to uncertainties. Both sectors are affected by financial, euro currency, geopolitical and economic policy uncertainties. Our results have several policy implications. Climate policy makers can prioritize policies that support bioenergy in order to reduce the potentially negative effects of uncertainties on bioenergy investment. Investors aiming to diversify their portfolio should be aware that many uncertainties are common drivers of bioenergy and oil & gas returns, the connectedness between assets of these energy types could therefore increase when uncertainty increases.
Suggested citation: S. Cedic, A. Mahmoud, M. Manera, G. S. Uddin, (2021), ‘Uncertainty and Stock Returns in Energy Markets: A Quantile Regression Approach’, Nota di Lavoro 11.2021, Milano, Italy: Fondazione Eni Enrico Mattei