Abstract
Comparing the pricing of electricity derivatives to the existing models for traditional commodities as presented in section 17.3 we need to encompass the unique characteristic of non-storability of power. This peculiarity of the electricity market has several crucial implications for derivatives pricing:
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(i)
The non-storability feature means that due to a lack of any power inventories electricity must be produced at exactly the same time as it is consumed. This creates a load-matching problem in that the utility industry needs to discover the value of lost load and decide on the optimum amount of reserve margin to provide.161
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(ii)
The non-storability problem could be diminished if there would be a physical possibility of economically transferring power from an overproduction region to a consumer region. However, the US power markets are geographically distinct with several regions serving as delivery points for electricity futures contracts. Currently, there are no satisfactory transmission policies but inter-regional price differences will decrease as deregulation proceeds and the utility industry is required to open up its transmission systems.162
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(iii)
If electricity is practically non-storable, no inventories can be hold and thereby it is impossible to specify the positive returns from owning the commodity for delivery if power is not storable. Thus, the convenience yield models can hardly be extended to price electricity derivatives. This incomplete market situation asks for different pricing models to value electricity derivatives.163
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References
See, for example, Woodley and Hunt (1997, p. 44 f.).
On the isssue of rationalising the US power transmission business see, for example, Barber (1997).
For suggestions of different pricing models see, for example, Eydeland and Geman (1998).
For the marked volatility in energy prices that is both high and variable over time see, for example, Duffie and Gray (1995) and Pokalsky and Robinson (1997).
See, for example, Pokalsky and Robinson (1997) and Leong (1997).
In applying these models for pricing purposes it needs to be ensured that power derivatives are valued risk neutrally. Leong (1997), for example, sees the need of properly distinguishing between power forward prices and forecasts of power prices.
See section 19.2 for a detailed description of the selected data sample and the estimation results of the empirical inferences.
Duffie and Gray (1995, p. 39).
Eydeland and Geman (1998, p. 73).
For an analysis on the impact of mean-reversion processes on, for example, interest rate contingent claims see Uhrig-Homburg (1999).
Other proposed stochastic volatility models from the literature include Hull and White (1987), Stein and Stein (1991), and Schöbel and Zhu (1999).
In Heston (1993) the spot price diffusion models the dynamics of a stock price which is a tradeable and therefore hedgeable asset.
On the relevance of incorporating risk premia in the valuation of power derivatives see, for example, Pirrong and Jermakyan (1999).
The fomula is named after the contributions of Feynman (1948) and Kac (1949).
For a description of the differential operator see, for example, Oksendal (1995, p. 128).
See, for example, Ingersoll (1987, p. 397) for an application with interest rate contingent claims.
This type of differential equation commonly arises with the squared Gaussian model of Cox, Ingersoll, and Ross (1985b); see, for example, Rogers (1995, p. 99 f.).
For a similar solution to equation (18.13) see, for example, Rogers (1995, p. 100) and Stein and Stein (1991, p. 730).
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© 2001 Springer-Verlag Berlin Heidelberg
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Kellerhals, B.P. (2001). Electricity Pricing Model. In: Financial Pricing Models in Continuous Time and Kalman Filtering. Lecture Notes in Economics and Mathematical Systems, vol 506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-21901-0_18
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DOI: https://doi.org/10.1007/978-3-662-21901-0_18
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