Pricing of Energy Contracts: From Replication Pricing to Swing Options

Chapter
Part of the International Series in Operations Research & Management Science book series (ISOR, volume 199)

Abstract

The principle of replication or superhedging is widely used for valuating financial contracts, in particular, derivatives. In the special situation of energy markets, this principle is not quite appropriate and might lead to unrealistic high prices, when complete hedging is not possible, or to unrealistic low prices, when own production is involved. Therefore we compare it to further valuation strategies: acceptability pricing weakens the requirement of almost sure replication and indifference pricing accounts for the opportunity costs of producing for a considered contract. Finally, we describe a game-theoretic approach for valuating flexible contracts (swing options), which is based on bi-level optimization.

Keywords

Filtration Hedging 

References

  1. 1.
    Artzner P, Delbaen F, Eber JM, Heath D (1999) Coherent measures of risk. Math Financ 9:203–228CrossRefGoogle Scholar
  2. 2.
    Barbieri A, Garman M (2002) Understanding the valuation of swing contracts. Energy Power Risk Manag, FEA, Tech. Rep.Google Scholar
  3. 3.
    Broussev N, Pflug G (2009) Electricity swing options: behavioral models and pricing. Eur J Oper Res 197(39):1041–1050Google Scholar
  4. 4.
    Bühlmann H (1972) Mathematical risk theory. Die Grundlehren der mathematischen Wissenschaften, Band 172. Springer, New YorkGoogle Scholar
  5. 5.
    Carmona R (2009) Indifference pricing: theory and applications. Princeton series in financial engineering. Princeton University Press, PrincetonGoogle Scholar
  6. 6.
    Dempe S (1992) A necessary and a sufficient optimality condition for bilevel programming problems. Optimization 25:341–354CrossRefGoogle Scholar
  7. 7.
    Dempe S (2002) Foundations of bilevel programming. Kluwer Academic Publishers, DordrechtGoogle Scholar
  8. 8.
    Föllmer H, Leukert P (1999) Quantile hedging. Financ Stoch 3:251–273CrossRefGoogle Scholar
  9. 9.
    Frauendorfer K, Güssow J, Haarbrücker G, Kuhn D (2005) Stochastische optimierung im energiehandel: entscheidungsunterstützung und bewertung für das portfoliomanagement. Zeitschrift für Energie, Markt, Wettbewerb 1:59–66Google Scholar
  10. 10.
    Haarbrücker G, Kuhn D (2009) Valuation of electricity swing options by multistage stochastic programming. Automatica 45:889–899CrossRefGoogle Scholar
  11. 11.
    Kaminski V, Gibner S (1995) Exotic options. In: Kaminski V (ed) Managing energy price risk. Risk Publications, London, pp 117–148Google Scholar
  12. 12.
    Kovacevic R, Paraschiv F (2012) Medium-term planning for thermal electricity production, OR Spectrum,  Doi:10.1007/500291-013-0340-9 Google Scholar
  13. 13.
    Kovacevic RM, Pflug GC (2013) Electricity swing option pricing by stochastic bilevel optimization: a survey and new approaches, available ref www.speps.org
  14. 14.
    McNeil AJ, Frey R, Embrechts P (2005) Quantitative risk management - concepts, techniques and tools. Princeton series in finance. Princeton University Press, PrincetonGoogle Scholar
  15. 15.
    Outrata J (1993) Necessary optimality conditions for Stackelberg problems. J Optim 76:305–320Google Scholar
  16. 16.
    Pennanen T (2012) Introduction to convex optimization in financial markets. Math Program 134:157–186CrossRefGoogle Scholar
  17. 17.
    Pflug G, Römisch W (2007) Modeling, measuring and managing risk. World Scientific, SingaporeCrossRefGoogle Scholar
  18. 18.
    Pilipović D (2007) Energy risk: valuing and managing energy derivatives, 2nd edn. McGraw-Hill Professionalition, New YorkGoogle Scholar
  19. 19.
    Pilipović D, Wengler J (1998) Getting into the swing. Energy Power Risk Manag 2:22–24Google Scholar
  20. 20.
    Rockafellar R (1974) Conjugate duality and optimization. In: CBMS-NSF regional conference series in applied mathematics, vol 16. SIAM, PhiladelphiaGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Department of Statistics and Operations Research (ISOR)University of ViennaViennaAustria
  2. 2.ISOR and IIASALaxenburgAustria

Personalised recommendations