Course description
Advance Derivatives Markets, Hedging, and Risk Management is a two-day instructor led course presented by the energy training experts at Mennta Energy Solutions. This highly applied and practical energy training course is designed for energy risk practitioners interested in enhancing their knowledge of best practices in valuation, hedging and risk management of derivatives portfolios.
Delegates are introduced to the most commonly used derivatives pricing models in energy trading organizations such as closed-form solutions and Monte Carlo simulation. The main price processes for energy risk analysis such as Geometric Brownian Motion and Mean-reverting models are illustrated with pricing and risk analysis examples.
This advanced trading course builds on the concepts introduced in DPH1 and DPH2 and explores advanced strategies used to price, hedge and manage the risk of derivatives in leading energy trading organizations. Delegates learn about the practical applications of the models and strategies from the point of view of users of those models, not the quantitative developers.
Advanced market risk management topics such as marginal VaR analysis, backtesting VaR models and Extreme Value Theory (EVT) as well as risk risk metrics such as Earnings at Risk (EaR), Cash Flow at Risk (CFaR) and Economic Capital are covered with practical examples. Several case studies illustrate how to set an effective system of risk limits and risk-adjusted performance measurement.
DPH3 also covers best practices in counterparty risk management. Metrics such as Potential Future Exposure (PFE) and Credit Valuation Adjustments (CVA) are introduced in the context of contract valuation and risk charges.
This highly interactive workshop uses current case studies, Excel exercises and group discussions to reinforce the concepts presented in the lectures.
Please note: a laptop and Excel version 2007 or later is required in order to engage in market data.
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Suitability
- 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
Content
301: 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
302: 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.
- Case study: How to perform hundreds of simulations for risk analysis in any existing spreadsheet 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)
303: Energy Price Behaviour: Overview of forward curve models
- Forward curve behavior in oil, gas and power markets
- Review of spot price models: GBM, GBM w/ MR, MRJD
- Multi-factor and multi-commodity models: Structured Monte Carlo (Cholesky-based) vs. Principal component analysis (PCA).
- Excel exercises with PCA and Structured Monte Carlo Simulation.
- Case study: Designing stress tests with Principal Component Analysis (PCA)
- Seasonality and Forward Curve Simulations
304: Value at Risk Methodologies
- Review of probability and statistics for VaR analysis
- Variance-covariance, delta-normal or Riskmetrics VaR
- Historical Simulation
- Monte Carlo Simulation
- Step by step calculation in Excel
- Pros and cons of the different methodologies
Day 2
305: Enterprise Risk Management and Key Risk Indicators (KRIs)
- Enterprise Risk Management and Risk Metrics
- Earnings at risk, Cash Flow at risk and Gross Margin at risk for multiple maturities
- Margin-at-risk calculation and liquidity risk management
- Excel Case study: Multi-step Earnings at Risk calculation for an energy producer
- Economic capital and RAROC
- Case Study: Calculation of economic capital and pre-trade risk charges
306: Counterparty Risk Management
- Counterparty risk trading in energy trading
- Current Exposure, Expected Exposure vs. potential future exposure
- Potential exposure and the role of margin, collateral and settlements.
- Excel case study: Calculating PFE for Commodity Swaps and Physical Forwards
- Counterparty Valuation Adjustments (CVA)
- Counterparty VaR and dynamic potential exposure. Adding default probabilities.
- Using CVA and PFE to set counterparty limits and credit charges
307: Advanced Market Risk Management for Energy Trading
- Case study: How to game VaR
- Overcoming known problems with VaR models
- Introducing Volatilities as Risk Factors
- Advanced Historical Simulation (HS): EWMA HS and Volatility-Updated HS
- Marginal VaR analysis: Applications for Hedging and Risk Management
- Backtesting VaR models: Statistical tests
- Tail “heaviness” and Tail “asymmetry”: ETL and other Risk measures.
- Extreme Value Theory VaR and ETL
- Integrating stress tests into the tail analysis.
308: Advanced Valuation topics and volumetric risk pricing and hedging
- Pricing options with volatility surfaces
- Step by step calculations with delta and moneyness surfaces
- Skew-adjusted delta and gammas
- Implied volatilities and implied price distributions
- Stochastic volatility models in commodity markets
- Valuation and hedging of exposures with volumetric risk: Understanding Gamma risk
- Introduction to Least-squares Monte Carlo
- Excel case study: Pricing an American option with the Longstaff-Schwartz method