Natural Gas Storage Valuation using Approximate Dynamic Programming

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

Talk on Real Options using LSM

Today I presented on the topic of real options analysis using an approximate dynamic programming technique called the Least Squares Monte Carlo algorithm. The method was applied to real estate development. The model can be downloaded here (Update the talk is on YouTube here): Real Options Talk FinalDownload The default inputs and parameters of the … Continue reading Talk on Real Options using LSM

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

Valuing R&D and Patents with Real Options Analysis

Patents and R&D projects, such as the development of new pharmaceuticals, are subject to substantial uncertainty in both the investment phase, which can be long and costly, as well as uncertainty about the market prospects of a commercialized product. Additionally managers possess operational flexibility allowing them to abandon projects that either become too costly to … Continue reading Valuing R&D and Patents with Real Options Analysis

Real Options Valuation with Simulation

Introduction Many finance academics have long touted the superiority of the real options valuation approach for capital investment analysis over the traditional discounted cash flow method. The classic academic text on real options is Dixit & Pindyck (1994). They dedicated their book to "The Future" as they foretell of a time when real options analysis … Continue reading Real Options Valuation with Simulation

Modeling Construction Cost Uncertainty

Introduction In previous posts I have demonstrated how to use Monte Carlo simulation to handle absorption risk, vacancy and occupancy dynamics, and sale price (or rental rate) risks for real estate developments and investments. In addition to those risks, real estate construction projects are notorious for being over budget and behind schedule. Therefore in this … Continue reading Modeling Construction Cost Uncertainty

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