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Does the Black-Scholes formula work for electricity markets? A nonparametric approach

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  • Hjalmarsson, Erik

    () (Department of Economics, School of Economics and Commercial Law, Göteborg University)

Abstract

Despite the high volatilities recorded for electricity prices, there seems to be little demand for options on electricity. One reason for the disinterest in electricity options could arise from uncertainty about how to price these options. This study uses recent econometric advances to nonparametrically estimate correct prices for electricity options and compare these to the Black-Scholes prices. The main finding is that although the nonparametric estimates deviate significantly from the Black-Scholes prices, it would be diffcult to find an alternative parametric model that performs better. Thus, from a practical viewpoint, the Black-Scholes prices appear to be the best available.

Suggested Citation

  • Hjalmarsson, Erik, 2003. "Does the Black-Scholes formula work for electricity markets? A nonparametric approach," Working Papers in Economics 101, University of Gothenburg, Department of Economics.
  • Handle: RePEc:hhs:gunwpe:0101
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    File URL: http://hdl.handle.net/2077/2809
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    References listed on IDEAS

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    1. Ait-Sahalia, Yacine, 1996. "Testing Continuous-Time Models of the Spot Interest Rate," Review of Financial Studies, Society for Financial Studies, vol. 9(2), pages 385-426.
    2. A. Ronald Gallant & Chien-Te Hsu & George Tauchen, 1999. "Using Daily Range Data To Calibrate Volatility Diffusions And Extract The Forward Integrated Variance," The Review of Economics and Statistics, MIT Press, vol. 81(4), pages 617-631, November.
    3. Hardle, Wolfgang & Linton, Oliver, 1986. "Applied nonparametric methods," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 38, pages 2295-2339, Elsevier.
    4. Knittel, Christopher R. & Roberts, Michael R., 2005. "An empirical examination of restructured electricity prices," Energy Economics, Elsevier, vol. 27(5), pages 791-817, September.
    5. Sanjiv R. Das, 1998. "Poisson-Guassian Processes and the Bond Markets," NBER Working Papers 6631, National Bureau of Economic Research, Inc.
    6. Federico M. Bandi & Peter C. B. Phillips, 2003. "Fully Nonparametric Estimation of Scalar Diffusion Models," Econometrica, Econometric Society, vol. 71(1), pages 241-283, January.
    7. Bandi, Federico M. & Moloche, Guillermo, 2018. "On The Functional Estimation Of Multivariate Diffusion Processes," Econometric Theory, Cambridge University Press, vol. 34(4), pages 896-946, August.
    8. Hendrik Bessembinder & Michael L. Lemmon, 2002. "Equilibrium Pricing and Optimal Hedging in Electricity Forward Markets," Journal of Finance, American Finance Association, vol. 57(3), pages 1347-1382, June.
    9. Ball, Clifford A. & Torous, Walter N., 1983. "A Simplified Jump Process for Common Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 18(1), pages 53-65, March.
    10. Black, Fischer, 1976. "The pricing of commodity contracts," Journal of Financial Economics, Elsevier, vol. 3(1-2), pages 167-179.
    11. Malcolm P. Baker & E. Scott Mayfield & John E. Parsons, 1998. "Alternative Models of Uncertain Commodity Prices for Use with Modern Asset Pricing Methods," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 115-148.
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    Cited by:

    1. Fred Espen Benth & Jūratė Šaltytė Benth & Steen Koekebakker, 2008. "Stochastic Modeling of Electricity and Related Markets," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number 6811, February.
    2. Paul Twomey & Karsten Neuhoff, 2005. "Market Power and Technological Bias: The Case of Electricity Generation," Working Papers EPRG 0501, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    3. Twomey, Paul & Neuhoff, Karsten, 2010. "Wind power and market power in competitive markets," Energy Policy, Elsevier, vol. 38(7), pages 3198-3210, July.

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    More about this item

    Keywords

    Electricity markets; Nonparametric estimation; Option pricing;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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