Real Options Valuation in Energy Investment Projects: Modeling Hedging Strategies Using Genetic Algorithm Software

Complex real options models for project economics simulation and modeling must contend with several sources of uncertainty, and the arrays of options to be considered typically include several options to invest in information generation and evaluation. Monte Carlo simulation is considered a good way to address these modeling challenges, but it does not lend itself well to optimization problems. This paper presents a model of dynamic hedging aimed at optimization and using genetic algorithms and Monte Carlo simulation. This approach offers new possibilities for energy investment applications of dynamic hedging based on real options theory. The modeling summarized in this study relied on the Excel-based commercial software RiskOptimizer for a simple case (with known values) and for a more complex real options case involving investment in information. The results from several experimental runs are presented, with suggestions for improvement of the software – the strengths and weaknesses of RiskOptimizer are pointed out in this context – and for new directions for research.

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