Energy Portfolio Optimisation & Electricity Price Forecasting Forum
The city of Düsseldorf is hosting the third edition of the “Energy Portfolio Optimisation and Electricity Price Forecasting Forum” at the beginning of April 2017. Amidst renowned speakers of Eon, Uniper, Statoil and the London Business School, KYOS will also host a presentation.
Presentation: The Evaluation of Dynamic Hedging Strategies
On Tuesday 5 April, at 9.30 Cyriel de Jong from KYOS will deliver his views on the “Evaluation of Dynamic Hedging Strategies”. His presentation will cover the following points:
• Earnings-at-Risk applied to energy portfolios
• Different approaches for the calculation of dynamic delta hedges
• Transforming hedges to tradable contracts
• Monte Carlo analysis versus back tests
• Case study results: gas storage and power plants
Please refer to the Agenda to see the full list of speakers and case studies.
AtRisk Model
The KYOS model AtRisk will provide the basis for the presentation. The AtRisk model consists of two modules: Cashflow-at-Risk and Earnings-at-Risk. It provides companies information about potential future losses in their contract and asset portfolio. Both risk metrics rely on Monte Carlo simulations of future prices and calculate a distribution of outcomes. The CfaR and EaR applications are delivered as one AtRisk model, which also provides recommendations about the optimal hedges to minimize the risk.
The AtRisk model is fully integrated with both the KYOS KyStore and KyPlant model. KyStore is developed for traders and portfolio managers in natural gas markets. The gas storage optimization software raises revenues from gas storage trading operations, provides accurate valuations and reduces risk with adequate hedge recommendations. The model uses advanced stochastics including Least Squares Monte Carlo techniques to capture the full optionality in gas storage facilities.
KyPlant supports traders and plant owners in power markets. The power plant optimization software determines the value of power plants by quickly calculating the optimal dispatch. It also supports trading decisions and reports exposures. The real options-based model combines actual plant and contract characteristics with realistic price simulations. It can be applied to real physical power plants, but also to virtual power plants, spark and dark spread options, and power options.
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