DUBLIN, Sept. 16, 2019 /PRNewswire/ -- The "In-Depth: Practical Statistical Analysis for the Energy & Power Markets" conference 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.
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.
Who Should Attend:
Among those who will benefit from this seminar include energy and electric power executives; attorneys; government regulators; traders & trading support staff; marketing, sales, purchasing & risk management personnel; accountants & auditors; plant operators; engineers; and corporate planners. Types of companies that typically attend this program include energy producers and marketers; utilities; banks & financial houses; industrial companies; accounting, consulting & law firms; municipal utilities; government regulators and electric generators.
- 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
- 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
The Energy Forecasting Toolbox
- Historical Trend Analysis
- Univariate Time Series
- Multivariate Time Series
- Econometric Models
- Bayesian Estimation
- End-Use Models
- Engineering or Process Models
- Network Models
- Game Theory
- 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
- 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
- Exercise: Valuing Combustion Turbines using Real Options
- Exercise: Valuing Gas Storage using Real Options
For more information about this conference visit https://www.researchandmarkets.com/r/5biwr9
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Laura Wood, Senior Manager
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