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2019 | 29 | 1 | 97-119

Article title

The valuation of real options in a hybrid environment

Authors

Content

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EN

Abstracts

EN
The aim of this paper is to present the possibilities and purposefulness of the application of fuzzy set theory to the valuation of real options. Owing to temporal fluctuations in the market, some input parameters in a model of a real option cannot always be expressed in a precise sense. Therefore, it is natural to consider them as a fuzzy numbers. Such an approach allows us to keep more information about the possible value of real options. A hybrid (fuzzy-stochastic) model for valuing a switch option is presented. Under these assumptions, the value of a switch option will be a fuzzy random set. This article assesses the incremental benefit of product switch options in steel plant projects. Such options are valued by Monte Carlo simulation and modelling the prices of and demand for steel products using fuzzy geometric Brownian motion. Finally, the value of a product switch option is defined by the upper and lower probability distribution function

Year

Volume

29

Issue

1

Pages

97-119

Physical description

Contributors

  • Faculty of Management, AGH University of Science and Technology, ul. Mickiewicza 30, 30-059 Kraków, Poland

References

  • ALLENOTOR D., THULASIRAM R.K., A grid resources valuation model using fuzzy real option, Lecture Notesin Comp. Sci., Parallel and Distributed Processing and Applications, Springer, 2007, 4742, 622–632.
  • APPADOO S.S., THAVANESWARAN A., Recent developments in fuzzy sets approach in option pricing, J. Math. Fin., 2013, 3 (2), 312–322.
  • BASTIAN-PINTO C., BRANDÃO L., HAHN W.J., Flexibility as a source of value in the production of alternative fuels. The ethanol case, Energy Econ., 2009, 31 (3), 411–422.
  • BELLMAN R.E., ZADEH L.A., Decision-making in a fuzzy environment, Management Science, 1970, 17 (4), 141–164.
  • BLANK F.F., BAYDIA T.K.N., DIAS M.A., Private infrastructure investment through public private partnership. An application to a toll road highway concession in Brazil, Proc. 13th Annual International Conference on Real Options, Braga, Portugal, Santiago, Spain, 2009, 1–21.
  • BRENNAN M.J., SCHWARTZ E.S., Evaluating natural resource investments, J. Bus., 1985, 58 (2), 135–157.
  • CARLSSON C., FULLER R., A fuzzy approach to real option valuation, Fuzzy Sets Syst., 2003, 139 (2), 297–312.
  • CARLSSON C., FULLER R., HEIKKILA M., MAJLENDER P., A fuzzy approach to R&D project portfolio selection, Int. J. Appr. Reas., 2007, 44 (2), 93–105.
  • GARCIA F.A.A., Fuzzy real option valuation in a power station reengineering project. Soft computing with industrial applications, Proc. Sixth Biannual World Automation Congress, Spain, 2004, 281– 287.
  • GUERRA M.L., SORINI L., STEFANINI L., Parametrized fuzzy numbers for option pricing, IEEE International Conference on Fuzzy Systems, London 2007, 727–732.
  • HLADÍK M., ČERNY M., Interval regression by tolerance analysis approach, Fuzzy Sets Syst., 2012, 193 (4), 85–107.
  • HUANG M.-G., Real options approach-based demand forecasting method for a range of products with highly volatile and correlated demand, Eur. J. Oper. Res., 2009, 198 (3), 867–877.
  • HULL J.C., Options, futures and other derivatives securities, 6th Ed., Prentice Hall, Englewood Cliffs 2006.
  • KAHRAMAN C., UCAL İ., Fuzzy real options valuation for oil investments, Proc. 8th International FLINS Conference, World Scientific Publishing Co. PTE Ltd., Singapore 2008, 1027–1032.
  • LEE C.F., TZENG G.H., WANG S.Y., A new application of fuzzy set theory to the Black–Scholes option pricing model, Expert Syst. Appl., 2005, 29 (2), 330–342.
  • MARATHE R., RYAN S.M., On the validity of the geometric Brownian motion assumption, Eng. Econ., 2005, 50 (2), 1–40.
  • OZORIO L.M., BASTIAN-PINTO C.L., BAIDYA T.K.N., BRANDÃO L.E.T., Investment decision in integrated steel plants under uncertainty, Int. Rev. Fin. Anal., 2013, 27 (4), 55–64.
  • RĘBIASZ B., GAWEŁ B., SKALNA I., Valuing managerial flexibility. An application of real-option theory to steel industry investments, Oper. Res. Dec., 2017, 27 (2), 91–111.
  • RĘBIASZ B., New method of selection of efficient project portfolios in the presence of hybrid uncertainty, Oper. Res. Dec., 2016, 26 (4), 65–90.
  • RUEY S.T., Analysis of Financial Time Series, Wiley, New York 2002.
  • SHIU H.H., SHU H.L., A fuzzy real option approach for investment project valuation, Exp. Syst. Appl., 2011, 38 (12), 15296–15302.
  • TAO C., JINLONG Z., SHAN L., BENHAI Y., Fuzzy real option analysis for IT investment in nuclear power station, Lecture Notes in Comp. Sci., ICCS 2007, Springer, 2007, 4489, 953–959.
  • THAVANESWARAN A., APPADOO S.S., FRANK J., Binary option pricing using fuzzy numbers, Appl. Math. Lett., 2013, 26 (1), 65–72.
  • THIAGARAJACH K., THAVANESWARAN A., Fuzzy coefficient volatility models with financial applications, J. Risk Fin., 2006, 7 (5), 503–524.
  • WATTANARAT V., PHIMPHAVONG P., MATSUMARU M., Demand and price forecasting models for strategic and planning decisions in a supply chain, Proc. Schl. ITE Tokai Univ., 2010, 3 (2), 37–42.
  • WEIDONG X., CHONGFENG W., XU W.J., LI H.Y., A jump-diffusion model for option pricing under fuzzy environments, Ins.: Math. Econ., 2004, 44 (3), 337–344.
  • WU H.-C., Using fuzzy sets theory and Black–Scholes formula to generate pricing boundaries of European options, Appl. Math. Comp., 2007, 185 (1), 136–146.
  • WU H.-C., Pricing European options based on the fuzzy pattern of Black–Scholes formula, Comp. Oper. Res., 2004, 31 (7), 1069–1081.
  • XU W., PENG X., XIAO W., The fuzzy jump-diffusion model to pricing European vulnerable options, Int. J. Fuzzy Syst., 2013, 15 (3), 317–325.
  • YANG T.I., Simulation based estimation for correlated cost elements, Int. J. Project Manage., 2005, 23 (4), 275–282.
  • YU S.-E., LI M.-Y.L., HUARNG K.-H., CHEN T.-H., CHEN C.-Y., Model construction of option pricing based on fuzzy theory, J. Marine Sci. Technol., 2011, 19 (5), 460–469.
  • ZADEH L.A., Fuzzy sets, Inf. Control, 1965, 8, 338–358.
  • ZDENEK Z., Modelling the sequential real options under uncertainty and vagueness (fuzzy-stochastic approach), [In:] J. Ramik, D.K. Stavarek (Eds.), Proc. 30th International Conference Mathematical Methods in Economics, Karvina, Czech Republic, 2012, 1027–1032.
  • ZENG M., WANG H., ZHANG T., LI B., HUANG S., Research and application of power network investment decision-making model based on fuzzy real options, International Conference on Service Systems and Service Management, IEEE, Chengdu, China, 2007, 397−402.
  • ZHANG L.-H., ZHANG W.-G., XU W.-J., XIAO W.-L., The double exponential jump diffusion model for pricing European options under fuzzy environments, Econ. Model., 2012, 29 (3), 780–786.
  • ZMESKAL Z., Generalised soft binominal American real option pricing model. Fuzzy stochastic approach, Eur. J. Oper. Res., 2010, 207 (2), 1096–1103.

Document Type

Publication order reference

Identifiers

YADDA identifier

bwmeta1.element.desklight-66c6507f-dd21-4ccc-810c-9258701ec68e
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