Introduction Recently I gave a talk on real options valuation using the Least Squares Monte Carlo method (LSM). LSM is a variant of Regression Monte Carlo (RMC) methods within the field of approximate dynamic programming (ADP). The goal of ADP is to overcome the "curse of dimensionality" that plagues traditional approaches to solving sequential optimization … Continue reading Natural Gas Storage Valuation using Approximate Dynamic Programming
Energy
Swing Options – A Modeling Language Comparison
To become significantly more reliable, code must become more transparent. In particular, nested conditions and loops must be viewed with great suspicion. Complicated control flows confuse programmers. Messy code often hides bugs.— Bjarne Stroustruphttps://www.stroustrup.com/Software-for-infrastructure.pdf In the previous post I discussed the valuation of energy swing options, which I created in the Analytica modeling language. However … Continue reading Swing Options – A Modeling Language Comparison
Valuing Swing Options with Monte Carlo Simulation
Introduction This post provides an example of how to value a natural gas swing option using the Least Squares Monte Carlo method (LSM). Swing options are common option contracts in the energy industry. They allow managers flexibility in determining both the timing and quantity of delivery for energy and related commodities. These option contracts are … Continue reading Valuing Swing Options with Monte Carlo Simulation
A Markov Chain Model for Synthetic Wind Energy Time Series
Introduction An essential element and input into a Wind Energy Feasibility Study is an accurate forecast of the wind resource at the project site. As scientists and mathematicians have turned their attention to this problem, there has been an increasing interest in using simulation based methods to generate synthetic time series of wind speeds. The … Continue reading A Markov Chain Model for Synthetic Wind Energy Time Series
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