Energy Derivatives Pricing, Hedging and Risk Management - DPH2
CPE Credits Awarded: 16
Categories: Trading, Derivatives, Hedging and Risk Management, Global Association of Risk Professionals (GARP) Approved Course
Energy Derivatives Pricing, Hedging and Risk Management (DPH2) is the premiere intermediate course on energy and commodity hedging, market risk measurement and management and derivatives pricing.
The course starts with a review of energy price behavior, and applied probability and statistics using Excel exercises with hands-on calculations. After being introduced to the building blocks of risk analysis, delegates conduct several hands-on exercises that show how to estimate volatilities, correlations and calculate Value at Risk and other risk metrics.
Delegates are introduced to the most commonly used derivatives pricing models in energy trading organizations such as closed-form solutions, binomial trees, and Monte Carlo simulation. The main stochastic price processes for energy risk analysis such as Geometric Brownian Motion and Mean-reverting models are illustrated with pricing and risk analysis examples.
Delegates are also introduced to implied volatility skews and “Greeks” to measure and manage the risk of option books as well as relevant accounting rules for valuation and hedge accounting such as IAS 39/IFRS9.
The course concludes by providing in-depth applications of market risk management of energy portfolios, with particular emphasis on VaR, Stress Tests and Backtesting. Delegates conduct hands-on calculations for variance-covariance, Monte Carlo and Historical Simulation VaR for energy portfolios.
The topics covered in DPH1 and DPH2 can assist delegates preparing for GARP’s Energy Risk Professional (ERP) exam.
Recommended courses: Energy Derivatives Markets, Instruments and Hedging (DPH1), North America Gas and Power Trading and Risk Management (NAGP), Power and Risk management for Utilities (PRM)
Please note: a laptop and up-to-date version of Excel software is required in order to engage in market data. Not sure if you have the appropriate experience? Click here to test yourself on the knowledge necessary for this course.
Please note: a laptop and up-to-date version of Excel software is required in order to engage in market data.
Not sure if you have the appropriate experience? Click here to test yourself on the knowledge necessary for this course.
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The Oxford Princeton Programme is registered with GARP as an Approved Provider of continuing professional education (CPE) credits. The Oxford Princeton Programme has determined that this program qualifies for 16 GARP CPE credit hours. If you are a Certified FRM or ERP, please record this activity in your Credit Tracker at www.garp.org/cpe
- Market risk managers
- Energy traders
- Trading managers
- End-users of derivatives in corporations
- Credit risk analysts
- Risk consultants
- Risk and audit committee members
- CFOs and treasury managers
- Finance department personnel
- Compliance managers
- Middle and back-office personnel
- Treasurers and treasury analysts
- Chief risk officers
201: Review of Energy Price Behavior, Probability and Statistics
- Overview of energy price behavior; seasonality; mean reversion; spikes
- Volatility structure in energy markets; spot vs. forwards
- Probability distributions; moments of a distribution, histograms and QQ plots.
- Excel exercises with hands-on calculations of volatilities, correlations
- Case study: VaR calculation for a single exposure.
- Calculating and interpreting rolling window volatilities and correlations in Excel
202: Energy derivatives: Exchange and OTC traded derivatives and embedded options
- How to unbundle embedded risk structures in energy contracts
- A step-by-step process to identify optionality in energy contracts and derivatives
- Review of main derivatives structures: Futures and forwards; fixed for floating and basis swaps; European and American options; Basis swaps and options; Structured Products
- Exercise: Analysis of structured swaps
- Case study: Overview of Swing and Take-or-Pay Contracts
203. Analysis of Derivative Strategies
- Zero-cost collars. Uses and misuses.
- Case Study: Using Zero Cost Collars in a Hedging Programme
- Three-way Collars: Aggressive vs. Conservative strategies
- Volatility Plays: Straddles and Strangles
- Comparing the risk and benefits of various hedging strategies
- Identifying price and volatility views with price and volatility risk matrices
204: Introduction to Derivatives Pricing Models
- Mark-to-market vs. mark-to-model. Conceptual Interpretation.
- Review of IFRS 7/9 and three ‘fair value’ levels. Adding the liquidity dimension
- Closed-form solutions (formulas). Case Study: Pricing Options using Black 76 in Excel.
- Implied Volatility. Skews and Surfaces. Delta and moneyness surfaces
- Case Study: Bank of Montreal Natural Gas derivatives mispricing
- Monte Carlo simulation based models. Pricing an Average Price Option.
- Binomial and trinomial trees. Case Study: Pricing an American option.
- Counterparty valuation adjustments (CVA) and liquidity adjustments (bid-ask)
205: Understanding option sensitivities through the "Greeks"
- Delta and Gamma, Vega and Theta: Definition, calculation and main uses.
- Case study: calculating and visualizing "Greeks" in Excel
- Case Study: P/L decomposition of a Trading Book using Greeks
- Analyzing the dynamics of delta, gamma and vega for a straddle position
- Skew adjusted delta and gamma
206: Energy Price Behaviour: Overview of spot price models
- Spot price models for energy and commodity markets
- Understanding price processes and parameter calibration
- Geometric Brownian motion (GBM) and Mean reversion
- Case Study: Simulating prices with GBM and a mean-reverting process in Excel.
- Jump diffusion with mean reversion (MRJD) processes
- Case study: How to perform hundreds of simulations for risk analysis in any existing spreadsheet in Excel
207: Market Risk Management for Energy Trading
- Best practices of market risk management in energy markets
- Understanding VaR and Expected tail loss (ETL)
- Overview of methodologies: analytic, Monte Carlo and historical simulation
- Case study: interpretation of market risk disclosures for large energy firm
- Oil, power and gas specific issues
- Case study: calculating VaR in a spreadsheet using different methodologies
208: Stress Testing and Backtesting for Energy and Commodity Firms
- Designing and conducting stress tests for energy portfolios
- Benefits of stress tests
- Standard & Poors liquidity risk survey and Stress Testing
- Integrating stress tests in the risk modeling process
- Stress tests for crude and products; gas; electricity
- Exercise: Creating and presenting stress test reports
- Backtesting analysis
DR CARLOS BLANCO is an expert in energy, commodity, and financial risk management and modeling. He has been a faculty member of the Oxford Princeton Programme since 2004, where he teaches the Derivatives Pricing Hedging and Risk Management Certificate Programme as well as courses on Counterparty Risk Management and Gas and Power Trading and Risk Management.
He has published over 100 articles on financial, energy, and commodity trading, hedging and risk management. He is the founder and managing director of a risk management advisory firm with clients in North America, Europe, Africa and Asia. Carlos is a former VP, Risk Solutions at Financial Engineering Associates. There, he worked over six years as an essential contributor in the development of the energy derivatives valuation and risk management models of the firm. He also provided leading-edge risk advisory and educational services to over 500 energy and commodity trading firms and financial institutions worldwide. He also managed the world-class support and professional services department within the firm. Prior to FEA, Carlos worked for a hedge fund in the Midwest and an asset management firm in Madrid, Spain. He is a former regional director of the Professional Risk Managers’ International Association (PRMIA).
“The instructor was extremely knowledgeable and the materials/tools will be useful for years to come in my organization!” S.H., Imperial Irrigation
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