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Energy Risk Modelling Course 2026

Learn how to measure, model, and manage risk in complex energy portfolios from leading industry experts Sjur Westgaard and Morten Risstad.

Register for Energy Risk Modelling Course 2026

Energy markets are becoming more volatile. Fast.

For decades, energy markets were relatively stable, with well-understood price dynamics and manageable levels of risk. Portfolios were simpler, and risk was often assessed using static or historical approaches.

That situation has changed. Increased market integration, geopolitical uncertainty, weather-driven supply shocks, and the rapid growth of renewables have introduced new layers of volatility and complexity. Energy companies now operate across spot, futures, and options markets, with portfolios that span multiple time horizons and risk factors.

As a result, measuring and managing risk at both portfolio and corporate level has become more demanding. Understanding volatility, correlation, and tail risk, and how these evolve over time, is now essential for making informed decisions in energy markets.

What you’ll learn

This course takes a practical and in-depth look at how risk can be measured, modelled, and managed in energy markets using modern quantitative methods. It combines core theory with hands-on applications, using real market data and commonly used tools such as Excel, R, and Python.

Over two intensive days, you will learn how different risk measures behave in volatile energy markets, how advanced models improve risk estimates, and how these insights can be used in portfolio management and enterprise risk management. The focus is on understanding why models work, when they fail, and how to apply them responsibly in practice.

This training course will teach you how to:

  • Measure and interpret risk in energy spot, futures, and options markets

  • Model volatility, correlation, and tail risk using parametric, semi-parametric, and non-parametric approaches

  • Apply and compare Value at Risk and Expected Shortfall models, including advanced methods

  • Stress test energy portfolios using historical and hypothetical scenarios

  • Backtest risk models and understand their strengths and limitations

  • Integrate price risk with other risk factors in an enterprise risk management context

  • Use quantitative risk outputs to support portfolio and hedging decisions

You’ll benefit most from this training if you:

Work in trading, risk, or market analysis

  • Are responsible for measuring and monitoring market risk

  • Want to improve risk forecasting and stress testing

Manage energy portfolios or corporate risk

  • Oversee hedging strategies or portfolio risk across different time horizons

  • Need a stronger quantitative basis for risk-related decisions

Work with data, models, or analytics

  • Use Excel, R, or Python in your role

  • Want a clearer understanding of the risk models used in practice

This course is a good fit if you work with energy market risk and want to strengthen your ability to measure, model, and manage it in a practical and structured way.

You do not need to be a specialist quant, but you should be comfortable working with data, models, and quantitative concepts. A working knowledge of finance or energy markets will help you get the most out of the course.

If you are unsure whether it fits your role, just ask. We are happy to help you decide.

For questions, please contact:

Day 1: Energy Markets, Risk Measures & Volatility Modelling

09:00 – 09:15: Welcome and Course Introduction

  • Overview of course objectives and structure

  • Participant introductions and experience sharing

  • Software/tools setup: Excel, R, Python

  • Dataset overview: historical prices futures and spot markets, option markets

09:15 – 10:00: Introduction to Energy Markets and Financial Returns

  • Energy markets overview: electricity, gas, oil, coal, carbon

  • Spot, futures, and options markets

  • Key price characteristics:
    - Volatility clustering
    - Fat tails and skewness
    - Mean reversion and seasonality

  • Return calculation and analysis
    - Descriptive statistics (mean, variance, skewness, kurtosis)
    - Visualization: histograms, time series, QQ plots

Hands-on: Return calculations and visual diagnostics in Excel/Python/R

10:00 – 10:15 Break

10:15 – 11:00: Risk Measures in Energy Markets

  • Key measures: volatility, correlation, VaR, Expected Shortfall

  • Historical vs. parametric approaches

  • How risk evolves over time (e.g. rolling volatility and exponential weighted volatility)

  • Interpreting and comparing tail risk in different markets for long and short positions

11:00 – 11:15 Break

11:15 – 12:00: Volatility Modelling

  • Volatility modelling approaches:
    - EWMA
    - GARCH family (normal, t, skewed t, cornish fisher, evt)
    - Realized volatility from high-frequency data
    - Implied volatility from option markets

  • Model diagnostics and forecasting

12:00 – 13:15 Lunch break

13:15 – 14:00: PCA and Correlation Modelling

  • Estimating dynamic covariances: EWMA, rolling windows

  • Multivariate volatility models:
    - Constant and dynamic conditional correlations (CCC, DCC)

  • Principal Component Analysis (PCA):
    - Interpreting forward curve shapes
    - Reducing dimensions for large correlation matrices

  • Hands-on: PCA on energy futures curves in R/Python

14:00 – 15:15 Break

15:15 – 16:00: Copulas

  • Understanding tail dependencies and non-linear relationships between assets

  • Understanding different stand alone distributions for different assets

  • Risk management using copula models

  • Hands-on: Copula-based dependency modelling in Excel/R/Python

Day 2: Advanced Risk Models, Stress Testing & Portfolio Integration

09:00 – 09:15: Recap and Day 2 Kickoff

  • Key learnings from Day 1

  • Goals and structure of Day 2

09:15 – 10:00: Advanced VaR and ES Models I

  • VaR/ES modelling:
    - Risk Metrics
    - Filtered Historical Simulation (FHS)
    - Cornish-Fisher expansion (CF)
    - Student T models
    - Extreme Value Theory (EVT)

  • Hands-on: Comparing risk models using real energy return data in Excel/R/Python

10:00 – 10:15 Break

10:15 – 11:00: Advanced VaR and ES Models II

  • Quantile Regression (QEG)

  • Semi-parametric Regression

  • Hands-on: Comparing risk models using real energy return data in Excel/R/Python

11:00 – 11:15 Break

11:15 – 12:00: Backtesting VaR and ES

  • Backtesting process and performance metrics

  • VaR tests (independent tests and Coverage tests)

  • ES tests

  • State of the art regression based tests of VaR and ES

  • Hands-on: Backtesting energy risk models in R and Pyhton

12:00 – 13:15 Lunch break

13:15 – 14:00: Stress Testing and Scenario Analysis

  • Historical vs. hypothetical scenarios

  • Constructing and applying stress tests to energy portfolios

  • Scenario design for geopolitical events, supply shocks, weather events

  • Workshop: Design and implement a stress test for energy positions using R and Pyhton

14:00 – 14:15 Break

14:15 – 15:00: Enterprise Risk Management (ERM) in Energy Firms

  • Integrated risk view: combining price, volume, FX, interest rate, and credit risk

  • Simulating future cash flows under uncertainty

  • Assessing hedge effectiveness and risk mitigation

  • Risk factor mapping and simulation

  • Portfolio aggregation and diversification effects

  • Strategy comparison under uncertainty

  • Hands-on: Cash flow simulation and hedge analysis in Excel

15:15 – 16:00: Future Directions in Energy Risk Modelling

  • Emerging topics in forecasting risk
    - Machine learning in risk estimation
    - Example hybrid models (PCA + Semiprarametric Regression)

  • Hands-on: Advanced risk models in R & Python

16:00 – 16:15: Summary

  • Summary of the course

  • Other topics not dealt with

Speakers

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

Sjur Westgaard is a Professor of Finance at the Norwegian University of Science and Technology (NTNU) and an Adjunct Professor at Inland Norway University of Applied Sciences, where he is affiliated with the Center for Business Analytics. His work focuses on financial risk management, energy and commodity markets, and economic and financial forecasting.

He has extensive experience from both academia and industry, having previously worked as an investment portfolio manager for an insurance company, a project manager in consulting, and a credit analyst in an international bank. His teaching and research cover corporate finance, derivatives, empirical finance, and risk modelling, with a particular emphasis on risk forecasting in energy and commodity markets.

Sjur is one of the founders and editors of the Journal of Commodity Markets and serves as an associate editor of the Journal of Energy Markets. He has led and contributed to several large research projects involving energy companies, academic institutions, and the Research Council of Norway.

Morten Risstad

Associate Professor Department of Industrial Economics and Technology Management Norwegian University of Science and Technology

Morten Risstad is an Associate Professor at the Department of Industrial Economics and Technology Management at the Norwegian University of Science and Technology (NTNU). His academic work focuses on empirical finance, asset pricing, derivatives, and financial risk management.

He holds a PhD in Industrial Economics and Technology Management from NTNU, an MSc in Finance from Nord University, and is a Certified European Financial Analyst (CEFA) from NHH. Prior to joining academia, Morten worked in consulting, multinational industrial companies, and financial institutions, with roles related to financial reporting, corporate finance, trading, and risk management.

Morten is also part of the research team at the Norwegian Open AI Lab, where he contributes to research at the intersection of data, analytics, and decision-making.