Energy Derivatives Pricing, Hedging and Risk Management

Energy Derivatives Pricing, Hedging and Risk Management

Course Summary
This two-day course provides delegates with a comprehensive overview of energy derivatives instruments and markets as well as an introduction to current best practices in pricing, risk management and hedging.
The main goal is to provide front, middle and back office personnel as well as anyone involved in working with energy derivatives (electricity, gas, coal, oil) with the proper tools in order to use them effectively as well as price, hedge and manage the risk of various derivatives structures.
The course is highly interactive and delegate participation and discussion are encouraged. Numerous case studies illustrate how firms with energy risk exposures hedge and manage their risks with derivatives instruments. The case studies are tightly linked with the concepts presented in the lectures.
Also, several group exercises will allow delegates to apply a variety of tools to hedge and control market exposures.
Please note: a laptop and up-to-date version of Excel software is required in order to engage in market data.
What you will learn

• How to implement current best practices in hedging and pricing energy derivatives structures in your organization
  
• Practical tips to build Forward Curves in illiquid markets
  
• Understand the most commonly used energy derivatives structures in the marketplace
  
• Determine hedge effectiveness for Derivatives using correlation analysis
  
• Develop a conceptual understanding of energy derivatives pricing models used for mark-to-market: Monte Carlo simulation, closed-form solutions and trees
  
• Apply the new FAS 157 rules regarding fair value of derivatives
  
• Apply the unique features of energy price behavior including mean reversion, seasonality and price jumps
  
• How to use derivatives sensitivities or "Greeks" for hedging purposes
  
• Apply various models of spot and forward price behaviour to price energy derivatives
  
• Measure the market risk of energy derivatives portfolios using Value at Risk and other risk models
  8CPE creditsawarded for this course .
  
  Suitability:
  Institutions: energy trading firms, energy companies, firms with large energy exposures (eg airlines, mining firms), energy hedge funds, banks, insurance firms, brokerage firms, risk management consultancies and service providers
  Other firms with energy risk exposures: airlines, mining firms, insurance firms
  
  
  
  • Energy trading managers and energy traders
    
  • Structured product groups
    
  • Energy risk managers at trading and energy firms
    
  • Controllers, financial, back and middle-office personnel, compliance officers
    
  • Derivatives trading and structuring
    
  • Energy analytics, research and development
    
  • Finance and Treasury Personnel in charge of derivatives valuation
    
  • Internal audit personnel involved with derivatives activities
    
  
  Course content:
  Revie- of Energy Price Behavior, Probability and Statistics
  
  
  
  • Overvie- of energy price behavior; seasonality; mean reversion; spikes
    
  • Spot vs. forward prices; price indices and market liquidity
    
  • Building Forward Curves in illiquid energy markets
    
  • Volatility structure in energy markets; spot vs. forwards
    
  • Calculating volatility and correlation.
    
  • Excel exercises with hands-on calculations of various risk statistics [NEW EXERCISES]
    
  
  Revie- of Main Energy Derivatives Structures
  
  
  
  • Ho- to unbundle embedded risk structures in energy contracts
    
  • Building blocks: long vs. short; option types; volumes; strike price; exercise style; underlying; trigger event/contingency; payoff type
    
  • Futures and forwards; fixed for floating and basis swaps
    
  • European and American options; caps, floors, collars and straddles
    
  • Spread or basis swaps and options
    
  • Average price and average strike options (Asian); options on swaps (swaptions)
    
  
  Mark-to-Market vs. Mark-to-Model: Introduction to Derivatives Pricing Models:
  
  
  
  • Mark-to-market vs. mark-to-model. Conceptual interpretation.
    
  • Overvie- of FAS 157 and three "fair value" levels. Adding the liquidity dimension [NEW]
    
  • Closed-form solutions (formulas). Case Study: Pricing an Option using Black 76 in Excel.
    
  • Monte Carlo simulation based models. Case Study: Generating Random Paths in Excel.
    
  • Binomial and trinomial trees. Case Study: Pricing an American option.
    
  • Understanding option sensitivities through the "Greeks" Case Study: Calculating and Visualizing "Greeks" in Excel
    
  • Implied volatility calculation; implied volatility skews and surfaces in energy markets. Case Study: Bank of Montreal Energy Derivatives Fiasco. [NEW]
    
  
  Energy Price Behaviour: Overvie- of Single Factor vs. Multi-Factor models
  
  
  
  • Spot price models for energy and commodity markets
    
  • Geometric Brownian motion; uses and limitations
    
  • Mean reversion; limit of variance ("terminal distribution"). Case Study: Simulating a mean-reverting process in Excel. [NEW]
    
  • Energy price behaviour: multi-factor and multi-commodity models
    
  • Case Study: principal component analysis in Excel [new]
    
  • Adding mean-reversion and jumps to the normal model
    
  • Case studies; locational, calendar and inter-commodity spreads
    
  
  Correlation and Dependence in Energy Markets
  
  
  
  • Understanding correlation
    
  • Seasonality and correlations
    
  • FAS 133 and hedge effectiveness. Ex-ante vs. ex-post tests. [NEW]
    
  • Case Study: Hedging Strategy by Airlines [NEW]
    
  • Minimum variance ratio using volatility and correlation analysis [NEW]
    
  • Correlations between spot and forward contracts
    
  • Case study: natural gas location basis risk
    
  • Case Study: Amaranth's US$6 billion failed Bet on Calendar Gas Spreads [NEW]
    
  
  Overvie- of Market Risk Management for Energy Trading
  
  
  
  • Market risk and "risk factors"
    
  • Understanding VaR and Expected Tail Loss (ETL)
    
  • Overvie- of methodologies: analytic, Monte Carlo and historical simulation
    
  • Determining the main contribution from risk factor exposures with Marginal VaR
    
  • Case study: interpretation of market risk disclosures for large energy firm
    
  • Case study: calculating VaR in a spreadsheet
    
  • Counterparty risk for derivatives: potential future exposure [NEW]
    
  
  Stress Testing for Energy and Commodity Firms
  
  
  
  • Designing and conducting stress tests for derivatives portfolios
    
  • Benefits of stress tests
    
  • Integrating stress tests in the risk modeling process
    
  • Stress tests for crude and products; gas; electricity [NEW]
    
  • Standard and Poors Liquidity Survey [NEW]
    
  • Exercise: stress test committee meeting