This course adds a third day to the popular Energy Statistical Analysis seminar to allow the time needed for a more in-depth discussion and explanation of many important topics. Additionally, this three-day course is designed as a hand-on workshop. Not only will you learn about practical energy statistical techniques and tools, but you will practice building statistical models in a workshop format.
Learn why companies continue to be exposed to significant energy and electricity related price risk, and how risk and value are properly quantified. Energy and electricity companies worldwide depend on accurate information about the risks and opportunities facing day to day decisions. Statistical analysis is frequently misapplied and many companies find that «a little bit of knowledge is a dangerous thing.»
This comprehensive three-day program is designed to provide a solid understanding of key statistical and analytic tools used in the energy and electric power markets. Through a combination of lecture and hands-on exercises that you will complete using your own laptop, participants will learn and practice key energy applications of statistical modeling. Be armed with the tools and methods needed to properly analyze and measure data to reduce risk and increase earnings for your organization.
A laptop is required.
What You Will Learn
Correlation & regression analysis; real option analysis; the Black-Scholes option pricing model; binomial trees; GARCH Models; the measurement of energy price risk; and how to use correlation and regression analysis for maintaining a competitive edge. • Workshop exercises will have you building forecast models including time series and financial engineering price models including Geometric Brownian Motion and Mean Reversion Jump Diffusion. • How to minimize price risk through operational design flexibility; measure forward price volatility and adapt Value-at-Risk concepts (VaR) for the Energy Industry. • Workshop exercises will have you building VaR models, calculating volatility and simulating complex energy projects. • Use actual case studies to examine 1) how Monte Carlo simulation is used to value renewable energy, demand response programs and energy storage projects; 2) bench-marking techniques used for estimating the incremental cost savings of expanding existing operations; and 3) real-option value of generation assets and power purchase agreements. • Actual workshop problems and case studies will look at statistical applications and tools most frequently used in the energy industry. • Learn the four manage statistical metrics.