Abstract
The aim of this paper is contributing from a practical and empirical perspective to option pricing under fuzziness. When we evaluate Black–Scholes option pricing formula with triangular fuzzy numbers, we obtain a non-triangular price that may be slightly difficult to use in practical applications. We improve the applicability of the fuzzy version of that formula by proposing and testing three triangular approximations when the subjacent asset price, its volatility and free interest rate are triangular fuzzy numbers. To check the goodness of these approximations, we firstly evaluate their closeness to the actual values of fuzzy Black and Scholes model. We find that the quality of those approximations depends on options maturity and moneyness grade and if we are pricing call or put options. We also assess the capability of those approximating methods to reflect satisfactorily real market prices and obtain good results. To develop all empirical applications, we use a sample of options on IBEX35 traded in the Spanish derivatives market on 3/1/2017.
Similar content being viewed by others
Notes
Notice that since 2016, in Eurozone, interest rates have been negative in all short-term monetary markets due to the expansive monetary policy of the European Central Bank.
In [22], it is developed a non-triangular approximating method for fuzzy option prices that also uses the Greeks. This paper proposes a flexible polynomial approximation to the fuzzy price of options, which is built up with the value of the extremes of the α-cuts in certain predefined knots and the value of the Greeks in these knots.
References
Abbasbandy, S., Ahmady, E., Ahmady, N.: Triangular approximations of fuzzy numbers using α-weighted valuations. Soft. Comput. 14(1), 71–79 (2010)
Anzilli, L., Facchinetti, G.: New definitions of mean value and variance of fuzzy numbers: an application to the pricing of life insurance policies and real options. Int. J. Approx. Reason. 91, 96–113 (2017)
Ban, A.: Approximation of fuzzy numbers by trapezoidal fuzzy numbers preserving the expected interval. Fuzzy Sets Syst. 159(11), 1327–1344 (2008)
Ban, A.I., Coroianu, L.: Nearest interval, triangular and trapezoidal approximation of a fuzzy number preserving ambiguity. Int. J. Approx. Reason. 53(5), 805–836 (2012)
Black, F., Cox, J.C.: Valuing corporate securities: some effects of bond indenture provisions. J. Financ. 31(2), 351–367 (1976)
Black, F., Scholes, M.: The pricing of options and corporate liabilities. J. Polit. Econ. 81(3), 637–654 (1973)
Brennan, M.J., Schwartz, E.S.: The pricing of equity-linked life insurance policies with an asset value guarantee. J. Financ. Econ. 3(3), 195–213 (1976)
Buckley, J.J., Eslami, E.: Pricing options, forwards and futures using fuzzy set theory. In: Kahraman, C. (ed.) Fuzzy engineering economics with applications, pp. 339–357. Springer, Berlin, Heidelberg (2008)
Buckley, J.J., Qu, Y.: On using α-cuts to evaluate fuzzy equations. Fuzzy Sets Syst. 38(3), 309–312 (1990)
Buckley, J.J.: Fuzzy statistics: hypothesis testing. Soft. Comput. 9(7), 512–518 (2005)
Capotorti, A., Figà-Talamanca, G.: On an implicit assessment of fuzzy volatility in the Black and Scholes environment. Fuzzy Sets Syst. 223, 59–71 (2013)
Carlsson, C., Fullér, R.: A fuzzy approach to real option valuation. Fuzzy Sets Syst. 139(2), 297–312 (2003)
Chrysafis, K.A., Papadopoulos, B.K.: On theoretical pricing of options with fuzzy estimators. J. Comput. Appl. Math. 223(2), 552–566 (2009)
de Andrés-Sánchez, J., González-Vila, L.: The valuation of life contingencies: a symmetrical triangular fuzzy approximation. Insur. Math. Econ. 72, 83–94 (2017)
de Andrés-Sánchez, J.: An empirical assestment of fuzzy Black and Scholes pricing option model in Spanish stock option market. J. Intell. Fuzzy Syst. 33(4), 2509–2521 (2017)
Dubois, D., Prade, H.: Fuzzy numbers: an overview. In: Dubois, D., Prade, H., Yager, R.R. (eds.) Readings on Fuzzy Sets for Intelligent Systems, pp. 113–148. Morgan Kaufmann Publishers, San Mateo (1993)
Dumas, B., Fleming, J., Whaley, R.E.: Implied volatility functions: empirical tests. J. Financ. 53(6), 2059–2106 (1998)
Figa-Talamanca, G., Guerra, M.L., Stefanini, L.: Market application of the fuzzy-stochastic approach in the heston option pricing model. Financ. a Uver 62(2), 162–179 (2012)
Grzegorzewski, P.: Fuzzy number approximation via shadowed sets. Inf. Sci. 225, 35–46 (2013)
Grzegorzewski, P., Mrówka, E.: Trapezoidal approximations of fuzzy numbers. Fuzzy Sets Syst. 153(1), 115–135 (2005)
Grzegorzewski, P., Mrówka, E.: Trapezoidal approximations of fuzzy numbers—revisited. Fuzzy Sets Syst. 158(7), 757–768 (2007)
Grzegorzewski, P., Pasternak-Winiarska, K.: Natural trapezoidal approximations of fuzzy numbers. Fuzzy Sets Syst. 250, 90–109 (2014)
Guerra, M.L., Sorini, L., Stefanini, L.: Option price sensitivities through fuzzy numbers. Comput. Math Appl. 61(3), 515–526 (2011)
Heberle, J., Thomas, A.: Combining chain-ladder reserving with fuzzy numbers. Insur. Math. Econ. 55, 96–104 (2014)
Heston, S.L.: A closed-form solution for options with stochastic volatility with applications to bond and currency options. Rev. Financ. Stud. 6(2), 327–343 (1993)
Jiménez, M., Rivas, J.A.: Fuzzy number approximation. Int. J. Uncertain. Fuzziness Knowl. Based Syst. 6(01), 69–78 (1998)
Kaufmann, A.: Fuzzy subsets applications in OR and management. In: Jones, A., Kaufmann, A., Zimmermann, H.J. (eds.) Fuzzy sets theory and applications, pp. 257–300. Springer, Netherlands. (1986)
Liu, Y., Chen, X., Ralescu, D.A.: Uncertain currency model and currency option pricing. Int. J. Intell. Syst. 30(1), 40–51 (2015)
MacBeth, J.D., Merville, L.J.: An empirical examination of the black–scholes call option pricing model. J. Financ. 34(5), 1173–1186 (1979)
Merton, R.C.: Applications of option-pricing theory: twenty-five years later (digest summary). Am. Econ. Rev. 88(3), 323–349 (1998)
Muzzioli, S., De Baets, B.: A comparative assessment of different fuzzy regression methods for volatility forecasting. Fuzzy Optim. Decis. Mak. 12(4), 433–450 (2013)
Muzzioli, S., De Baets, B.: Fuzzy approaches to option price modeling. IEEE Trans. Fuzzy Syst. 25(2), 392–401 (2017)
Muzzioli, S., Ruggieri, A., De Baets, B.: A comparison of fuzzy regression methods for the estimation of the implied volatility smile function. Fuzzy Sets Syst. 266, 131–143 (2015)
Nowak, P., Pawłowski, M.: Option pricing with application of levy processes and the minimal variance equivalent martingale measure under uncertainty. IEEE Trans. Fuzzy Syst. 25(2), 402–416 (2017)
Nowak, P., Romaniuk, M.: Computing option price for Levy process with fuzzy parameters. Eur. J. Oper. Res. 201(1), 206–210 (2010)
Nowak, P., Romaniuk, M.: A fuzzy approach to option pricing in a Levy process setting. Int. J. Appl. Math. Comput. Sci. 23(3), 613–622 (2013)
Nowak, P., Romaniuk, M.: Application of Levy processes and Esscher transformed martingale measures for option pricing in fuzzy framework. J. Comput. Appl. Math. 263, 129–151 (2014)
Savic, D., Pedrycz, W.: Fuzzy linear regression models: construction and evaluation. In: Kacprzyk, J., Fedrizzi, M. (eds.) Fuzzy regression analysis, pp. 91–101. Physica Verlag, Heidelberg (1992)
Sfiris, D.S., Papadopoulos, B.K.: Non-asymptotic fuzzy estimators based on confidence intervals. Inf. Sci. 279, 446–459 (2014)
Sun, Y., Kai, Y., Jichang, D.: Asian option pricing problems of uncertain mean-reverting stock model. Soft. Comput. (2017). https://doi.org/10.1007/s00500-017-2524-8
Terceño, A., De Andrés, J., Barberà, G., Lorenzana, T.: Using fuzzy set theory to analyze investments and select portfolios of tangible investments in uncertain environments. Int. J. Uncertain.Fuzziness Knowl. Based Syst. 11(03), 263–281 (2003)
Thavaneswaran, A., Appadoo, S.S., Frank, J.: Binary option pricing using fuzzy numbers. Appl. Math. Lett. 26(1), 65–72 (2013)
Trigeorgis, L.: Real options: managerial flexibility and strategy in resource allocation. MIT press, Boston (1996)
Wang, X., He, J.: A geometric Levy model for n-fold compound option pricing in a fuzzy framework. J. Comput. Appl. Math. 306, 248–264 (2016)
Wang, X., He, J., Li, S.: Compound option pricing under fuzzy environment. J. Appl. Math. (2014). https://doi.org/10.1155/2014/875319
Wu, H.C.: Pricing European options based on the fuzzy pattern of Black–Scholes formula. Comput. Oper. Res. 31(7), 1069–1081 (2004)
Wu, H.C.: Using fuzzy sets theory and Black–Scholes formula to generate pricing boundaries of European options. Appl. Math. Comput. 185(1), 136–146 (2007)
Xu, W., Xu, W., Li, H., Zhang, W.: A study of Greek letters of currency option under uncertainty environments. Math. Comput. Model. 51(5), 670–681 (2010)
Yoshida, Y.: The valuation of European options in uncertain environment. Eur. J. Oper. Res. 145(1), 221–229 (2003)
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. 29(3), 780–786 (2012)
Zmeškal, Z.: Application of the fuzzy–stochastic methodology to appraising the firm value as a European call option. Eur. J. Oper. Res. 135(2), 303–310 (2001)
Zmeškal, Z.: Generalised soft binomial American real option pricing model (fuzzy–stochastic approach). Eur. J. Oper. Res. 207(2), 1096–1103 (2010)
Acknowledgements
The author thanks anonymous reviewers for their constructive comments and suggestions.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
de Andrés-Sánchez, J. Pricing European Options with Triangular Fuzzy Parameters: Assessing Alternative Triangular Approximations in the Spanish Stock Option Market. Int. J. Fuzzy Syst. 20, 1624–1643 (2018). https://doi.org/10.1007/s40815-018-0468-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s40815-018-0468-5