Hedging and Optimization of Energy Asset Portfolios

Hedging and optimization techniques are useful tools to manage the levels of risk of portfolios. These tools in energy markets are highly recommendable due to their sizes and volatilities. This study uses stock share prices of oil and gas companies of Latin America and other regions and two future contracts for oil. The study proposes the selection of minimum risk portfolios and the calculation of efficient frontiers using different risk measures, one of them coherent. The price return series are transformed into new series to improve granularity and gain extension. Conditional risk measures are calculated through simulation using Gaussian and Extreme Value functions and Copulas-t. We apply non-linear programming techniques to find optimal hedging portfolios and efficient frontiers with the new series and the simulated conditional risk measures. Finally, we comment on using Machine Learning as an alternative way to help solve the proposed problems.