14
December

Energy Risk Modelling

This two-day in-depth workshop is dedicated for risk management professionals, analysts and traders wanting to gain insights into risk modelling of energy markets.
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Learn how to measure and model risk in energy portfolios

More volatile energy markets, combined with complex trading and hedging portfolios have increased the need for measuring risk of individual contracts as well as for whole portfolios. Enterprise risk management (ERM) at a corporate level has also become important. Furthermore, understanding the dynamics and determinants of volatility, correlation and risk in energy markets is essential.

For questions, feel free to contact:

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Day 1: Energy Risk Modelling

Risk and return characteristics of energy spot futures markets

Excel data addin from Montel
Cases from European energy futures and spot markets (Nordic, German, UK, and more)

Risk measures:

Volatility and Correlation
Value at Risk (VaR)
Expected Tail Loss (ETL)

Factor models for electricity spot markets

How to model spot price and spot price distributions. How fundamentals like fuel prices, forecast of demand and supply, wind and solar influence the price formation. How the sensitivities to fundamentals changes over time and over the levels of electricity prices. How to model and predict the price distribution.

Case study: The German electricity spot market

Methods applied: Static regression analysis, Rolling regression analysis, Quantile regression.

Day 2: Energy Risk Modelling

Statistical trading models for energy futures

Trend strategies involving one energy commodity
Spread trading strategies between energy markets (calendar spread and cross commodity spreads)
Measuring performance and risk in trading positions

Case studies recent trading losses in oil, natural gas and electricity futures and option markets

(China Aviation Oil, Amaranth, Bank of Montreal, Einar Aas, Optionsellers.Com)

Modeling volatility and correlation in energy markets

Moving average models for volatility and correlation
GARCH models
Models based on implied volatility
Models based on intradaily data

Value at risk' models for energy commodity portfolios:

Parametric VaR models (Risk Metrics and others)
Extreme Value Theory (EVT) models
Non-Parametric VaR models (Historical simulation/Filtered historical simulation)
Semi-Parametric VaR models (Quantile regression)
Stress testing and scenario analysis
VaR Model risk
Backtesting of VaR models

Modelling joint wind and price risk with copulas

Pitfalls of correlations
Introduction to copulas
Capturing different marginal distributions, assymetric tail behaviour and complex non-linear features
Simulation and risk analysis using copulas
Case: Danish wind production and prices

Data/Excel cases are given for each lecture and handed out together with power point presentations before the seminar starts.

The participant will receive:
- Course slides
- Notes
- Articles
- Data
- Excel applications
- References to books and article within energy risk modelling

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Speakers

Professor Sjur Westgaard

MSc and Phd of Industrial Economics from Norwegian University of Science and Technology and a MSc of Finance from Norwegian School of Business and Economics

Professor Westgaard has previously worked as an investment portfolio manager for an insurance company, a project manager for a consultant company and as a credit analyst for an international bank. Currently he is professor at the Norwegian University of Science and Technology and an Adjunct Professor at the Norwegian University of Life Sciences – Center for Commodity Market Analysis.

His teaching involves corporate finance, derivatives and real options, empirical finance and financial risk management. He is one of the founders and editors of Journal of Commodity Markets. He is also an associate editor of Journal of Energy Markets and Journal of Banking and Finance. His main research interest include risk modelling of energy markets. He has recently also been a project manager for two energy research projects involving the research counsil of Norway, power companies, and academic institutions in Europe.