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Modeling Energy Derivatives Through Supply, Demand, and System Data

Article Quant Q&A · Author: Brian B
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Summary

The document surveys broad approaches to modeling energy derivatives, with particular interest in electricity and also natural gas. One proposed path is to model the physical supply and demand relationships that drive prices. For electricity, the sudden loss of a generator or transmission corridor can shift available supply and cause a sharp price increase. This framing emphasizes gathering detailed market and system data, including prices, weather, wind, seasons, loads, generation, reserve capacity, and transmission constraints.

The discussion does not specify a mathematical model, calibration procedure, or pricing formula, and it does not compare model performance. It points readers toward books and a collection of commodity-price modeling research as starting points. The main lesson is that energy derivatives modeling can take multiple paths and that a structural approach depends heavily on relevant operational data; the brief answers do not provide enough detail to select a particular model or assess its suitability for a contract.

Key ideas

  • A structural approach models the supply and demand relationships that influence energy prices.
  • Generator outages and transmission disruptions can reduce available supply and cause electricity prices to spike.
  • Useful inputs can include weather, load, generation, reserves, and transmission conditions.
  • The discussion offers a broad starting point but gives no specific model equations or performance comparisons.

Tags

CommoditiesDerivatives pricingStatistics
Full text
# What are the major models for energy derivatives, particularly electricity derivatives?


# What are the major models for energy derivatives, particularly electricity derivatives?












Aside from Black-Scholes with crazy skews, what major models are used for energy derivatives? I'm thinking particularly of electricity derivatives, but I'm also interested in natural gas and other volatile contracts(*).

(*): pun intended

## Answer by bill_080 (score 11, accepted)

https://quant.stackexchange.com/a/514

That's a complicated question. There are many paths.

One path is to build a model of the underlying supply/demand relationships. For example, the sudden loss of a power supplier (or transmision corridor) shifts the supply curve to the left spiking the price. The key to the game is data, data, and more data (price, weather/wind, season, power loads, current power generation, stand-by generation, transmission line overload, etc).

There are several books written on the subject. If you dig around, you'll find everything from over-simplified books, to books that over-kill on a specific area of the business. Just a quick Google gives:

http://www.amazon.com/Managing-Energy-Price-Risk-Challenges/dp/1904339190

http://www.amazon.com/Understanding-Todays-Electricity-Business-Shively/dp/0974174416

My reputation level is too low to post more links.

## Answer by fabien (score 5)

https://quant.stackexchange.com/a/1098

This guy listed a list of key papers relative to commodities price modeling. That could perhaps help you get started.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

This summary was written by Stratmill's research agent from the original; it is not a copy of the source.