07-08 November 2022
||$ (USD) 2,620.00
30 September 2022
*Prices do not include VAT, GST, or any other local taxes. All applicable taxes will be added to the invoice.
**Please register by the deadline to help us ensure sufficient attendance and avoid postponing the course.
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 up-to-date version of Office would be an advantage in order to engage in market data; however it is not essential.
301: Energy Price Behavior: 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
- Modelling hourly and sub-hourly prices in power markets
302: Introduction to Derivatives Pricing Models
- Mark-to-market vs. mark-to-model. Conceptual Interpretation.
- 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 for European and Path-dependent options
- Case study: How to add thousands of simulations in Excel
- Binomial and trinomial trees. Case Study: Pricing an American option.
- Counterparty valuation adjustments (CVA) and liquidity bid-ask adjustments
303: Energy Price Behavior: Overview of forward curve and spread models
- Spot and Forward curve behavior in oil, gas and power markets
- Case study: Evolution of forward curves for oil and gas
- Multi-factor and multi-commodity models
- Structured Monte Carlo (Cholesky) with correlated random shocks
- Principal component analysis (PCA): Main uses and applications
- Excel exercises with PCA and Structured Monte Carlo Simulation.
- Case study: Destination option analysis and valuation with Monte Carlo
304: Market Risk Management for Energy Trading (III)
- Review of probability and statistics for VaR analysis
- Variance-covariance, Historical and Monte Carlo Simulation
- Case study: How to game VaR
- Overcoming known problems with VaR models
- Case study: VaR for option portfolios with volatilities as risk factors
- Advanced Historical Simulation (HS): EWMA HS and Volatility-Updated HS
- Tail “heaviness” and Tail “asymmetry”: ETL and other Risk measures.
- Extreme Value Theory VaR and ETL
- Integrating stress tests into the tail analysis.
305: Enterprise Risk Management and Key Risk Indicators (KRIs)
- Risk Metrics and Enterprise Risk Management
- Value and flow metrics: Main uses and differences
- 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
- Estimating default probabilities and internal rating systems
- 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)
- Using CVA and PFE to set counterparty limits and credit charges
307: Market Risk Management: Marginal VaR analysis and Attribution
- Deconstructing Risk: Marginal VaR
- Explaining VaR changes with a risk attribution report
- Marginal VaR analysis: Applications for Hedging and Risk Management
- Minimum Variance Ratio using Volatility and Correlation Analysis
- How to find the VaR minimizing position: Trade risk profiles and best hedges
- Pitfalls of correlation as a measure of dependence
- Short-term correlation vs. long term co-movement (cointegration)
308: Advanced Valuation topics in pricing and hedging
- Pricing options with volatility surfaces
- Stochastic volatility models in commodity markets
- Case study: Simulating forward prices with stochastic volatility
- Valuation and hedging of exposures with volumetric risk: Understanding Gamma risk
- Demand-driven uncertainty and volumetric risk (e.g. weather-driven loads)
- Supply-driven uncertainty and volumetric risks (e.g. renewables, operations risks)
- Case study: Hedging strategies for wind and solar generation