“On the Relationship Between GHGs and Global Temperature Anomalies: Multi-level Rolling Analysis and Copula Calibration”, by Agliardi Elettra, Alexopoulos Thomas & Cech Christian (2018) in Environmental and Resource Economics, 10.1007/s10640-018-0259-3.
Abstract: The relationship between GHG emissions and global warming is studied through multi-level rolling analysis to assess whether or not there are increasing rates in global climate change as a result of higher levels of anthropogenic emissions, as we move forward in time. Furthermore, in order to assess whether we observe tail dependence, representing simultaneous occurrence of extreme events, we employ copula methods. Our main findings suggest a constant effect of emissions on temperature anomalies especially in the last decades. On the other hand we observe positive upper tail dependence in our copula analyses. This implies a comparably high probability of joint extreme large values (i.e., high temperatures and emission concentrations). As a guide to policy, it suggests to keep down extreme events in emissions to prevent possibilities of extreme warmings.
“The growing importance of natural gas as a predictor for retail electricity prices in US”, by Thomas A. Alexopoulos (2017) in Energy, DOI
Abstract: In the literature, there are tests about long and or short Granger causality between primary energy sources such as natural gas and secondary energy sources like electricity. Nevertheless the existence of a causal relationship or not, cannot clearly illustrate the dynamics in their relationship over time. Towards this direction, we apply a one step ahead rolling forecast and examine the performance of the average cost of natural gas as a predictor for retail electricity prices at national and regional level over time. Our analysis answers if, how and when the cost of natural gas becomes a significant predictor of electricity prices. Besides lower natural gas prices, the existence of sufficient gas infrastructures or a competitive market environment or both of them is needed in order to couple retail electricity prices with the cost of natural gas. This growing importance must be taken into account from policy makers by allowing additional gas based stations in the main-grid but at the same time avoiding the risks from the natural gas price volatility.
“Carbon intensity as a proxy for environmental performance and the informational content of the EPI”, by Dimitrios D. Thomakos & Thomas A. Alexopoulos (2016), in Energy Policy, DOI
Abstract: We analyze the relationship between carbon intensity and EPI and find that the informational content of EPI is in large part explainable by the state of economic growth and level of carbon intensity, with the second variable being already an increasing function of emissions and a decreasing function of economic well being. Carbon intensity has the largest explanatory power for EPI rankings and consistently produces the correct, anticipated, negative sign in its relationship to EPI. Second in importance are the renewable energy sources, which also produce consistent results with respect to their impact on the EPI but with much lower explanatory power. Our results suggest that advanced countries should, as they are doing already, implement measures of high quality environmental content while measures for increasing economic growth, while controlling emissions, are appropriate for developing countries. A number of other energy policy implications and the use of new technologies are also discussed in the context of our analysis.
“Functional smoothing for risk management of energy assets”, by Thomas Alexopoulos and Dimitrios Thomakos (2016), in International Journal of Energy and Statistics, DOI
Abstract: In this paper we propose a new method for constructing single-asset investment strategies that can be used for hedging and risk management, with emphasis on the highly volatile energy asset class. The method consists of exploiting three stylized facts of asset returns, momentum, mean reversion and bubbles, by taking non-overlapping segments of the data that are used in a functional-type of analysis. We illustrate the workings of the proposed method with real data on two of the largest energy ETFs and the ETF for the S&P500. Our results show that the proposed method can perform substantially better than a simple rebalancing strategy or the buy and hold benchmark as it exhibits better risk return characteristics. More importantly, it appears that it can identify turning points relatively fast and is thus suitable for being used as a hedging and risk management tool in the highly unstable energy markets.