PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
2013 | nr 71 | 81--92
Tytuł artykułu

Ocena efektywności wybranych nieparametrycznych modeli wyceny opcji

Autorzy
Treść / Zawartość
Warianty tytułu
Assesment of the Effectiveness of Selected Nonparametric Option Pricing Models
Języki publikacji
PL
Abstrakty
Celem pracy jest analiza efektywności zastosowania sieci neuronowych i regresyjnych wektorów nośnych w wycenie opcji indeksowych (indeks WIG20) typu europejskiego notowanych na Warszawskiej Giełdzie Papierów Wartościowych (WGPW). (fragment tekstu)
EN
In the paper were presented researches on effectiveness of non-parametric option pricing models. In the article were used models based on neural networks optimized using a flexible back propagation algorithm (RPROP) and models based on support vectors. Graphically shows the changes of errors, depending on the moneyness and maturity of the option. In the paper were researched problems attached. (original abstract)
Rocznik
Numer
Strony
81--92
Opis fizyczny
Twórcy
  • Zachodniopomorski Uniwersytet Technologiczny w Szczecinie
Bibliografia
  • Amilon H. 2003. A neural network versus Black-Scholes: a comparison of pricing and hedging performances, J. Forecast., 22, 317-35.
  • Andreou P., Charalambous C., Martzoukos S. 2008. Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters, Eur. J. Oper. Res., vol. 185, Mar., 1415-1433.
  • Binner J.M., Bissoondeeal R.K., Elger T., Gazely A.M., Mullineux A.W. 2005. A comparison of linear forecasting models and neural networks: an application to Euro inflation and Euro Divisia, Appli. Econ., 37, 665-680.
  • Black F., Scholes M. 1973. The pricing of options and corporate liabilities. J. Politic. Econ. 81 (3), 637-654.
  • Black F., Scholes M. 1975. Fact and fantasy in the use of options. Financ. Anal. J. 31, 36-41 i 61-72.
  • Boser B.E., Guyon I., Vapnik V.N., 1992. A training algorithm for optimal margin classifiers, w: Proceedings of the Fifth Annual Workshop of Computational Learning Theory. 5, ACM. 144-152.
  • Cao L.J., Tay F.E.H. 2001. Financial forecasting using support vector machines, Neural Comput. Appl. 10, 184-192.
  • Chang C.-C., Lin C.-J. 2011. LIBSVM: A library for support vector machines. ACM Transactions on Intelligent Systems and Technology. vol. 2, no. 3, 1-27.
  • Cortes C., Vapnik V. 1995. Support-Vector Networks. Mach. Lear. vol. 20, no. 3, 273-297.
  • Gradojevic N., Gençay R., Kukolj D. 2009. Option pricing with modular neural networks. IEEE transactions on neural networks. vol. 20, no. 4, 626-637.
  • Huang S.-C., Wu T.-K. 2006. A hybrid unscented Kalman filter and support vector machine model in option price forecasting. Lect. Notes Comp. Sci., vol. 4221, 303-312.
  • Hull J., 1999. Kontrakty terminowe i opcje. Wprowadzenie. WIG-Press, Warszawa ISBN 83-8701-447-8.
  • Hutchinson J.M., Lo A.W., Poggio T. 1994. A nonparametric approach to pricing and hedging derivative securities via learning networks. J.Fin., 49, 851-859.
  • Jakubowski J., Palczewski A., Rutkowski M., Stettner Ł. 2003. Matematyka Finansowa, Instrumenty pochodne, WNT. ISBN 83-204-2807-6.
  • Korn R., Korn E. 2000. Option Pricing and Portfolio Optimization. Modern Methods of Financial Mathematics, Am. Math. Soc. Providence, Rhode Island. ISBN 0-8218-2123-7.
  • Liang X., Zhang H., Xiao J., Chen Y. 2009. Improving option price forecasts with neural networks and support vector regressions. Neurocomputing, vol. 72, no. 13-15, 3055-3065
  • Lin C.T., Yeh H.Y. 2005. The valuation of Taiwan stock index option prices-comparison of performances between Black-Scholes and neural network model, J. Stat. Manag. Syst., 8, 355-367.
  • Lin C.T., Yeh H.Y. 2009. Empirical of the Taiwan stock index option price forecasting model - applied artificial neural network, Appli. Econ., vol. 41, Jun. 2009, 1965-1972.
  • Liu M. 1996. Option pricing with neural networks w: Progress in Neural Information Processing, Red. S.I. Amari, L. Xu, L.W. Chan, I. King, i K.S. Leung. Springer-Verlag, vol. 2, 760-765.
  • Riedmiller M., Braun H. 1992. A fast adaptive learning algorithm. Technical Report, University Karslruhe, Germany.
  • Smola A.J., Scholkopf B. 2004. A tutorial on support vector regression. Stat. Comp., vol. 14, no. 3, 199-222.
  • Tseng C., Cheng S., Wang Y., Peng J. 2008. Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices, Physica A: Stat. Mech. Appli., vol. 387, May, 3192-3200.
  • Wang Y. 2009. Nonlinear neural network forecasting model for stock index option price: Hybrid GJR- GARCH approach. Exp. Syst. Appli., vol. 36, Jan, 564-570.
  • Vapnik V., Golowich S., Smola A. 1997. Support vector method for function approximation, regression estimation, and signal processing. in: Mozer M.C., Jordan M.I., and Petsche T. (Eds.) Advances in Neural Information Processing Systems 9, MA, MIT Press, Cambridge, 281-287.
  • Vapnik V., Chervonenkis A. 1964. A note on one class of perceptrons. Automation and Remote Control, 25.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171252935

Zgłoszenie zostało wysłane

Zgłoszenie zostało wysłane

Musisz być zalogowany aby pisać komentarze.
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.