Skip to main content

Advertisement

Log in

Pricing electric power options by maximizing the utility of investment wealth with fuzzy measures

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

Electric power options are hardly priced for the non-storable nature of electric energy. A pricing model for European call options on electric power is proposed, which is expected to give option premiums to investors by using optimization, expected utility as well as fuzzy measure theories. Firstly, a formula of the option premium based on maximizing the utility of investment wealth is given, where the utility function represents the investor’s preference for wealth and the Choquet expected integral with the λ-additive fuzzy measure is used to express the difference between investors in the estimation of option value. Then, combined with practical constraints a pricing model for the electric power option is obtained by maximizing the utility of investment wealth with the fuzzy measures. The integration of fuzzy measure and probability measure depicts the uncertainty of the option value, making the produced option premium worthwhile to refer to. Lastly, the impacts of utility functions, fuzzy parameters and practical constraints are tested by examples.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Scholesm BF (1973) The pricing of options corporate liabilities. J Polit Econ 81(3):637–659

    Article  Google Scholar 

  2. Hull JC (2005) Options, futures and other derivatives, 3rd edn. Prentice Hull, New York

    Google Scholar 

  3. Muzzioli S, Torricelli C (2002) Implied trees in illiquid markets: a Choquet pricing approach. Int J Intell Syst 17(6):577–594

    Article  MATH  Google Scholar 

  4. Appadoo SS et al (2008) Application of possibility theory to investment decisions. Fuzzy Optim Decis Making 7(1):35–57

  5. Ewald CO, Yang ZJ (2008) Utility based pricing and exercising of real options under geometric mean reversion and risk aversion toward idiosyncratic risk. Math Methods Oper Res 68(1):97–123

    Article  MATH  MathSciNet  Google Scholar 

  6. Haarbrucker G, Kuhn D (2009) Valuation of electricity swing options by multistage stochastic programming. Automatica 45(4):889–899

    Article  MathSciNet  Google Scholar 

  7. Cotter J, Hanly J (2012) Hedging effectiveness under conditions of asymmetry. Eur J Finance 18(2):135–147

  8. Wang X, He Y, Dong L et al (2011) Particle swarm optimization for determining fuzzy measures from data. Inf Sci 181(19):4230–4252

    Article  MATH  Google Scholar 

  9. Zhang Z (2011) Some discussions on uncertain measure. Fuzzy Optim Decis Making 10(1):31–43

    Article  MATH  Google Scholar 

  10. Yager Ronald R (2011) A measure based approach to the fusion of possibilistic and probabilistic uncertainty. Fuzzy Optim Decis Making 10(2):91–113

    Article  MATH  MathSciNet  Google Scholar 

  11. Xu XW, Hopp WJ (2009) Price trends in a dynamic pricing model with heterogeneous customers: a martingale perspective. Oper Res 57(5):1298–1302

    Article  MATH  MathSciNet  Google Scholar 

  12. Shioda R et al (2011) Maximum utility product pricing models and algorithms based on reservation price. Comput Optim Appl 48(2):157–198

  13. Elliott RJ, Siu TK (2011) Utility-based indifference pricing in regime-switching models. Nonlinear Anal-Theory Methods Appl 74(17):6302–6313

  14. Yang JQ, Yang ZJ (2012) Consumption utility-based pricing and timing of the option to invest with partial information. Comput Econ 39(2):195–217

    Article  MATH  Google Scholar 

  15. Sakawa M et al (2012) Fuzzy random bilevel linear programming through expectation optimization using possibility and necessity. Int J Mach Learn Cybernet 3(3):183–192

    Article  Google Scholar 

  16. Thavaneswaran A et al (2013) Binary option pricing using fuzzy numbers. Appl Math Lett 26(1):65–72

    Article  MATH  MathSciNet  Google Scholar 

  17. Kazemi SMR et al (2013) A hybrid intelligent approach for modeling brand choice and constructing a market response simulator. Knowl-Based Syst 40:101–110

    Article  Google Scholar 

  18. Li B, Wang T, Tian W (2013) Risk measures and asset pricing models with new versions of Wang’s transform. Adv Intell Syst Comput 200 AISC:155–167

  19. Gu P et al (2013) Modeling, simulation and design optimization of a hoisting rig active heave compensation system. Int J Mach Learn Cybernet 4(2):85–98

    Article  Google Scholar 

  20. Ramsay CM et al (2013) Pricing high-risk and low-risk insurance contracts with incomplete information and production costs. Insur Math Econ 52(3):606–614

    Article  MATH  MathSciNet  Google Scholar 

  21. Konstantinos A et al (2013) Papadopoulos. Hybrid (fuzzy-stochastic) modelling in construction operations management. Int J Mach Learn Cybernet 4(4):339–346

    Article  Google Scholar 

  22. Cui Y et al (2013) Probabilistic DEAR models. Int J Mach Learn Cybernet 4(4):373–389

    Article  Google Scholar 

  23. Wu JZ, Zhang QA (2010) 2-order additive fuzzy measure identification method based on diamond pairwise comparison and maximum entropy principle. Fuzzy Optim Decis Making 9(4):435–453

    Article  MATH  Google Scholar 

  24. Wang Z (1991) Fuzzy measure theory. Graduate school of the State University of New York, USA

  25. Wang X, Chen A, Feng H (2011) Upper integral network with extreme learning mechanism. Neurocomputing 74(16):2520–2525

    Article  Google Scholar 

  26. Yager RR, Alajlan N (2012) Measure based representation of uncertain information. Fuzzy Optim Decis Making 11(4):363–385

    Article  MATH  MathSciNet  Google Scholar 

  27. Shakya S et al (2012) Neural network demand models and evolutionary optimisers for dynamic pricing. Knowl-Based Syst 29:44–53

    Article  Google Scholar 

  28. Yechiel A, Shevah Y (2012) Optimization of energy costs for SWRO desalination plants. Desalination Water Treat 46(1–3):304–311

    Article  Google Scholar 

  29. Ksantini R, Boufama B (2012) Combining partially global and local characteristics for improved classification. Int J Mach Learn Cybernet 3(2):119–131

    Article  Google Scholar 

Download references

Acknowledgments

This paper is supported by the Fundamental Research Funds for the Central Universities (12MS84), the Co-construction Project of Beijing Municipal commission of Education, the National Natural Science Foundation of China (Grant No. 71171080).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yundong Gu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gu, Y., An, Y., Chen, D. et al. Pricing electric power options by maximizing the utility of investment wealth with fuzzy measures. Int. J. Mach. Learn. & Cyber. 6, 409–415 (2015). https://doi.org/10.1007/s13042-014-0270-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-014-0270-0

Keywords

Navigation