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Scenario Modeling
Generally speaking scenario will represent a specific case of Stress testing where each variable or group of variables will represent a certain predefined state. In case of the energy or financial markets these state could be a specific set of time windows in which special events have occurred, whether these were unique price movements, or climatic conditions that resulted in market disruptions, or certain policy or regulation impacts etc that created huge window of risks and uncertainty
Now to model these scenarios we will have to take a structured set of stressed conditions on certain variables and depending on the type of scenarios we will have a combination of models, analytical, numerical or Simulation based calculation logic to derive the output of those conditional states
In case of gas trading. That's a high-stakes, fast-moving space, and risk can come from multiple angles: market volatility, supply disruptions, regulatory shifts, storage capacity, weather, and geopolitical events. In the case of both Gas and Power scenario modeling could employ either a numerical framework like a shift up and down for a full revaluation of the portfolio or a simulation model of the MC type to derive the results of the potential changes. These scenario can be modeled off the Spot price fluctuations or Basis risk such as differences between hub prices like TTF, Henry Hub, NBP or modified Forward curve models or change in assumptions on VaR.
Some of the various constructs for scenarios can be build upon:
Sudden supply cut (e.g., pipeline disruption, LNG terminal outage)
Weather extremes (polar vortex, hot summer)
Regulatory shifts (carbon tax, gas export limits)
or could be based on Historical shifts where we have had some extreme events.
Scenario framework helps us understand how our portfolio would behave under those same conditions now with set of assumptions that reflect the reality today
This is basically replaying extreme real-world data across your trading book.
There is however certain degree of complexity in modeling scenarios that make it difficult to execute. That complexity emanates from the fact that when we are dealing with the set of variables that need to change, it is imperative that the fundamental relationship of those variables or correlation component has to be taken into account as an incorrect assumption or a a break in that relationship will render the model output useless or inaccurate.
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