Power and gas retailers are exposed to a variety of risks when selling to domestic customers. Many of these risks arise from the fact that customers are offered a fixed price, while the retailer must purchase the gas and power to supply their customers from the wholesale markets. The risk associated with offering fixed price contracts is exacerbated by correlations between demand and market prices. For example, during a cold spell gas demand increases and wholesale prices tend to rise, whilst during milder weather demand falls and wholesale prices reduce.

How can a power and gas retailer estimate the magnitude of these risks?

  • $\begingroup$ nobody knows...? $\endgroup$ – Alex Jan 20 '18 at 1:31
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    $\begingroup$ The "risk that the price of Crude will be above K at time T" is the value of a call option with strike K and maturity T. $\endgroup$ – noob2 Jan 22 '18 at 22:19
  • $\begingroup$ True but only part of the picture i would say. Option prices represent the weighted average conditional loss under some dynamics. However, one might be more interested in tail risks, for which for example VaR like methodologies can be employed. $\endgroup$ – Bram Jan 31 '18 at 16:53

@Noob2’s comment above is “spot” on. Across the natural resource and energy value chains there are significant price risks that:

A. Market prices will fall below price takers’ unit costs; and, B. Market prices will exceed price setters’ unit prices.

In either case, if you assume that log price changes are a martingale, and that expected profit is the unconditional expectation that $P_t > K$ (I.e. units will be produced/sold at any price), then expected profit is simply $P_t - K$. I.e., market risk is priced into the spot and forward markets.

If, however, you add market forces to the mix in which quantities produced, bought and sold are dependent on price, then you can introduce more complex conditional expectations. In the simplest example, where producers halt production at no cost when prices fall below the cost of production, then the problem is tractable using some variation of the Black-Scholes options pricing framework. I.e., solve for $V_{t,P}$ given the condition that $V_{T,P} = Max[0,P_T-K]$.

If the market dynamics are not so simply expressed (they never are!), practitioners use approaches which fall under real options analysis (ROA).

ROA is inherently a broad term since it encompasses various discrete and continuous estimates for price processes, cost structures, decisions types, definite and indefinite time frames, optimal strike boundaries and other boundary conditions, and other determinants. Estimating value under simulated real market conditions is the unifying characteristic for all ROAs.

For more reading, I recommend:

  1. Upstream: Smith and McCardle. Options In the Real World
  2. Gas Storage: Thomas, Davisson, and Rasmussen. Natural gas storage valuation and optimization: A real options application

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