Energyforum
Energy Trading and Energy Derivatives
28-30 October 2008, Barcelona, Spain
Day 1 9.00–17.00
Morning session
Dynamics in energy markets and energy prices
Trading energy derivatives
- Where are commodities and their derivatives being traded
- Exchange versus brokered trades
- Clearing versus credit risk
- Developments in trading markets
Energy price characteristics: Volatility, seasonality, mean-reversion, spikes
- Evolution of volatility in energy markets
- Volatility calculation approaches
- Comparing spot with forward volatility: jumps and mean-reversion
- Causes of seasonality
- Measuring correlations
Case study
SETTING UP A SIMULATIONS MODEL FOR ENERGY PRICES
Goal of this case is to build a multi-factor and multi-commodity simulation model. We start with the simple case of Geometric Brownian motion for 1 commodity and successively extend the model
to more than one commodity, and more than one maturity.
Afternoon session
Energy derivatives, hedges and volatility Energy derivatives:
Understanding the characteristics of forwards, futures, swaps, options
- Trading in energy forwards and futures
- The energy forward curve: backwardation and contango
-
Fixed-for-floating swaps:
who trades them and why - Comparing swaps, forwards and futures
- European, American, and Asian options.
- Calls and puts
- Constructing pay-off diagrams
Case study
CAPS AND FLOORS
Analyzing the potential risks and rewards of acapped end-user natural gas contract: exchanging upside potential for downside insurance
Main principles behind the valuation and hedging of derivatives:
- Applying arbitrage to the valuation of forwards and futures
- Delta, gamma and other option Greeks
- Delta, gamma and vega hedging
- The Black and Black-Scholes formula for pricing options on forwards and futures
- Implied volatility
- Volatility curves
Case study
IMPLEMENTING A DELTA-GAMMA HEDGE
How much can be secured using static and dynamic delta-gamma hedging of an energy option?
Simulation game 1
Goal of this first game is to make participants familiar with the trading of forwards and futures. They learn how they can use the products to speculate and to hedge spot price risk. They learn how to interpret news and the trades of other participants.
Day 2 9.00–16.00
Morning session
Valuation of energy derivatives
- Valuation of energy derivatives
- The principles of a good valuation approach
- Mark-to-market versus mark-to-model
- Historical simulation
- Price trees to value American-style options
- Monte Carlo simulation to value options in energy markets
Case study
CHOOSING WHEN TO EXERCISE AN EXTENSION OPTION
Using historical price data of crude oil prices, we analyze the value of an option to extend a contract to a longer maturity. We implement and compare the value coming out of the Black-Scholes formula, price trees and Monte Carlo simulation.
Structured energy products:
- Swing options, take-or-pay, tolling and other structured products
- Financial engineering
- Breaking down complex structures in basic products
- Commodity spreads: sparks, darks and cracks
- The least-squares Monte Carlo approach for valuing American style options
- Using simulations to analyze potential returns of complex structures.
Afternoon session
Risk management and trading strategies
The risk-management framework
- The role of trading in an organization
- Risk management and risk control
- Trading books and trading limits
- Stop loss limits
- Credit and counter-party risk
Value-at-Risk (VaR)
- Three calculation methods for VaR: pros and cons
- VaR in an organization to allocate risk capital
- Implementation of VaR
- Limitations of VaR and the need for stress-testing
Trading and hedging strategies: Optimising return while minimising risk
- Hedging against spot price risk
- Expected and unexpected hedging costs
- Hedging from a utility perspective
- Hedging from an end-user perspective: comparing fixed versus variable
- Looking for the natural hedge
Case Metallgesellschaft
- The Metallgesellschaft case: what went wrong with the roll-over hedge
- Setting up a roll-over hedge
- Impact of non-perfect correlations and the volatility curve on the roll-over hedge
Simulation game 2 – “Trading energy derivatives under risk control ”
The goal of the simulation game is to get a deeper understanding of the trade-off between risk and return in a natural gas trading environment. Participants trade various products to hedge their risk
and to speculate. However, they have to limit the amount of risk they take, because they have to pay for risk capital. As before, participants can react on news and trades of other players.
Day 3 9.00–16.00
Morning session
Real options
Value enhancement with real options analysis
in energy markets:
- When is real options analysis appropriate: flexibility to respond to uncertainty
- Real options in investment analysis and in asset optimization
- Different types of real options: postponing, expanding, switching, terminating
-
Examples of real options in energy markets: gas storage, pipelines, LNG, emission reduction, oil
refining, oil and gas exploration - Trading strategies to capture real option value: dealing with limited trading opportunities
Theory and case study real options
1: Emission uncertainty
We analyze the optimal timing of an investment in a cleaning technology that will yield certified emission rights for a power plant. This project will be analysed using a tree-based method. Participants will have to decide about investment by studying potential risks and rewards of the project.
Afternoon session
Real options
Theory and Case study real options 2 –“Investing in gas storage”
- Different types of storages
-
Identifying the main sources of revenue and risk from 3 different applications:
––Serving the customer portfolio
––Providing gas to the power plants
––Trading - How to assess the storage needs for the portfolio
- How to assess the storage needs for the power plants
- The option to trade spot or forward
- The intrinsic and rolling intrinsic value of storage
-
Storage strategies based on spot trading:
––The impact of volatility, mean-reversion and spikes
––Applying the least-squares Monte Carlo approach to storage
––Market liquidity issues
