DUBLIN–(BUSINESS WIRE)–The “In-Depth: Practical Statistical Analysis for the Energy & Power Markets” training has been added to ResearchAndMarkets.com’s offering.
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 with any version of EXCEL.
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.
Prerequisites and Advance Preparation
This fundamental level group live seminar has no prerequisites. No advance preparation is required before the seminar.
Key Topics Covered:
DAY ONE:
The Basics of Deterministic vs. Probabilistic Thinking for Energy Applications
Basics of data science – Information from Data
Descriptive Statistics, Means, Standard Deviations, Distribution Shapes
Frequency Distributions and Confidence Intervals
Implications of the Empirical Rule, Transformations and Probability
Fundamental Modeling Tools and Simulation
Exercise: Setting up a Monte Carlo Simulation to Evaluate Project Value and Risk
Application: Calculating Value at Risk (VaR)
The Linear Method and
The Quadratic Method
Historic Simulation Method
Monte Carlo Method
Exercise: Calculating VaR Using Three Different Methods
Application: Hedging Energy Exposure
Understanding the “Greeks
How and when to Hedge
Delta Hedging
Dynamic Hedging
Gamma Hedging
Application: Component Risk Analysis
Payoff Diagrams
Portfolio VaR Diagram
CAPM, RAROC and the Sharp Ratio
Calculating Load Following Supply Risk
Layered Hedging using Statistical Triggers
Exercise: Customer Migration Model Estimating Migration out of Standard Offer Service
Exercise: Measuring Load Following Supply Risk
Exercise: Measuring Intermittent Renewable Supply Risk
Correlation and Regression Analysis for Maintaining the Competitive Edge
Univariate and Multivariate Analysis
Hypotheses Testing
Testing for Equal Means and Variances
Control Charts
DAY TWO:
The Energy Forecasting Toolbox
Historical Trend Analysis
Univariate Time Series
Multivariate Time Series
Econometric Models
Bayesian Estimation
End-Use Models
Engineering or Process Models
Optimization
Network Models
Simulation
Game Theory
Scenarios
Surveys
Case Study: Statistical Reports that Everyone Can Understand
Case Study: Benchmarking to Industry Standards- GTS Steel vs. KCPL
Exercise: Building Regressions and Forecasting, PDF’s, CDF’s and Payoff Diagrams
Exercise: Calculating Hedge Ratios, Constructing an Energy Hedge and a Weather Hedge
Exercise: Using Forecasts in Monte Carlo Simulation to Calculate Risk Premium
DAY Three:
Introduction to Real Options Analysis
Details of Option Model Implementation
Real Options and Net Present Value (NPV) Analysis
Estimating Volatility and Uncertainty In Historical Prices
Black-Scholes, Binomial Trees, and GARCH Models
Geometric Brownian Motion and Mean Reversion
Application: Minimizing Price Risk through Operational Design Flexibility
Application: Real Option Value of Demand Response and the Smart Grid
Exercise: Calculating Volatility
Exercise: Simulating Prices using GBM and Mean Reversion Monte Carlo Models A
Exercise: Valuing Combustion Turbines using Real Options
Exercise: Valuing Gas Storage using Real Options
For more information about this training visit https://www.researchandmarkets.com/r/qyj0in
About ResearchAndMarkets.com
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