Evolving fuzzy systems for pricing fixed income options

Evolving Systems

Volumn 3, Issue 1, 2012, Pages 5-18

  Maciel, Leandro ^a   Lemos, Andre ^b   Gomide, Fernando ^a   Ballini, Rosangela ^a  

^a UNIVERSITY OF CAMPINAS   (Brazil)

^b FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

Author keywords

Derivatives; Evolving fuzzy systems; Interest rate; Neural networks; Option pricing

Indexed keywords

CLOSED-FORM FORMULAE; COMPUTATIONAL FINANCE; EVOLVING FUZZY SYSTEMS; EVOLVING MODELS; FORECAST MODEL; FUZZY MODELS; INTEREST RATES; OPTION PRICE; OPTION PRICING; PARTICIPATORY LEARNING; PRICE MOVEMENT; SYSTEM ADAPTATION; SYSTEM DYNAMICS;

COSTS; DERIVATIVES; FUZZY SYSTEMS; NEURAL NETWORKS; STATISTICAL TESTS;

ECONOMICS;

EID: 84857869648     PISSN: 18686478     EISSN: 18686486     Source Type: Journal    
DOI: 10.1007/s12530-011-9042-1     Document Type: Article

References (41)

1. Abonyi J, Babuska R, Szeifert F (2002) Modified Gath-Geva fuzzy clustering for identification of Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B 32(5): 612-621.
2. Almeida CIR, Vicente JV (2006) Pricing and hedging brazilian fixed income options. In: Proceedings of the 6th Brazilian financial meeting, Vitória, Brazil.
3. Angelov P (2002) Evolving rule-based models: a tool for design of flexible adaptive systems. Springer, Heidelberg, ISBN: 3-7908-1457-1.
4. Angelov P, Filev D (2004) An approach to on-line identification of evolving Takagi-Sugeno fuzzy models. IEEE Trans Syst Man Cybern Part B 34(1): 484-498.
5. Angelov P, Filev D (2005) Simpl_eTS: a simplified method for learning evolving Takagi-Sugeno fuzzy models. In: Proceedings of the 2005 international conference on fuzzy systems, FUZZ-IEEE, Reno, USA, pp 1068-1073.
6. Angelov P, Zhou X (2006a) Evolving fuzzy systems from data streams in real-time. In: Proceedings of the international symposium on evolving fuzzy systems, pp 29-35.
7. Angelov P, Zhou X (2006b) Online identification of Takagi-Sugeno fuzzy models DEMO. http://194803445/eTS/.
8. Astrom KJ, Wittenmark B (1994) Adaptive control, 2nd edn. Addison-Wesley Longman Publishing Co., Inc., Boston.
9. Barbedo CHS, Vicente JVM, Lion OMB (2009) Pricing asian interest rate options with a Three-Factor HJM model. Working Paper Series 188. http://www. bcb. gov. br/pec/wps/ingl/wps188. pdf.
10. Black F (1976) The pricing of commodity contracts. J Financ Econ 3: 167-179.
11. Black F, Scholes M (1973) The pricing of options and corporate liabilities. J Political Econ 81(3): 637-654.
12. Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2: 267-278.
13. Diebold FX, Mariano RS (1995) Comparing predictive accuracy. J Bus Econ Stat 13(3): 253-263.
14. Figa-Talamanca G, Guerra ML (2009) Fuzzy option value with stochastic volatility models. In: Proceedings of the 8th international conference on intelligent systems design and applications, Pisa, Italy.
15. Gençay R, Qi M (2001) Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping and bagging. IEEE Trans Neural Netw 12(4): 726-734.
16. Hastie T, Tibshirani R, Friedman JH (2001) The element of statistical learning: data mining, inference, and prediction, 2nd edn. Springer, New York.
17. Johnson CR (1988) Lectures & adaptive parameter estimation, 1st edn. Prentice-Hall, Upper Saddle River.
18. Junior AF, Greco F, Lauro C, Francisco G, Rosenfeld R, Oliveira R (2003) Application of Hull-White model to Brazilian IDI options. In: Proceedings of the 3th Brazilian financial meeting, Florianópolis, Brazil.
19. Kasabov N, Song Q (2002) DENFIS: dynamic evolving neural-fuzzy inference system and its application for time series prediction. IEEE Trans Fuzzy Syst 10(2): 144-154.
20. Kim E, Park M, Kim S, Park M (1998) A transformed input-domain approach to fuzzy modeling. IEEE Trans Fuzzy Syst 6(4): 596-604.
21. Lehmann EL (1988) Nonparametric statistical methods based on ranks. Prentice Hall, Englewood Cliffs.
22. Lemos A, Gomide F, Caminhas W (2011) Multivariable gaussian evolving fuzzy modeling system. IEEE Trans Fuzzy Syst 19(1): 91-104.
23. Leu Y (2010) A fuzzy time series-based neural network approach to option price forecasting. Lect Notes Comput Sci 5990: 360-369. doi: 10. 1007/978-3-642-12145-6_37.
24. Lima E (2008) Modelo Takagi-Sugeno evolutivo participativo. Master's thesis, School of Electrical and Computer Engineering, University of Campinas.
25. Lima E, Ballini R, Gomide F (2010a) Dynamic analysis of the participatory learning algorithm. In: Proceedings of the 36th annual convention of the Society for the Study of Artificial Intelligence and Simulation of Behaviour (AISB), Leicester, UK.
26. Lima E, Hell M, Ballini R, Gomide F (2010b) Evolving fuzzy modeling using participatory learning. In: Angelov P, Filev D, Kasabov N (eds) Evolving intelligent systems, chap 4. Wiley-IEEE-Press, NJ, pp 67-86.
27. Liu F (2009) Pricing currency options based on fuzzy techniques. Eur J Oper Res 193(2): 530-5402.
28. Ljung L (1999) System identification-theory for the user, 2nd edn. Prentice-Hall, Englewood Cliffs.
29. Ljung L (1999b) System identification: theory for the user. Prentice-Hall, Heidelberg, ISBN: 0-13-656695-2.
30. Lughofer ED (2008) FLEXFIS: a robust incremental learning approach for evolving Takagi-Sugeno fuzzy models. IEEE Trans Fuzzy Syst 16(6): 1393-1410.
31. Maciel LS, Ballini R (2010) Option pricing using fuzzy and neuro-fuzzy inference systems. In: Proceedings of the 4th CSDA international conference on computational and financial economics, London.
32. Pedrycz W, Gomide F (2007) Fuzzy systems engineering: toward human-centric computing. Wiley Interscience, NJ.
33. Schwarz G (1978) Estimating the dimension of a model. Ann Stat 6(2): 461-468.
34. Silva L, Gomide F, Yager RR (2005) Participatory learning in fuzzy clustering. In: The 2005 IEEE international conference on fuzzy systems FUZZ-IEEE, pp 857-861.
35. Wellstead PE, Zarrop MB (1995) Self-tuning systems: control and signal processing, 1st edn. Wiley, New York.
36. Wu H (2005) European option pricing under fuzzy environments. Int J Intell Syst 20(1): 89-102.
37. Yager RR (1990) A model of participatory learning. IEEE Trans Syst Man Cybern 20: 1229-1234.
38. Yager R, Filev D (1994) Approximate clustering via the mountain method. IEEE Trans Syst Man Cybern 24(8): 1279-1284.
39. Yang ZR, Platt MB, Platt HD (1999) Probabilistic neural network in bankruptcy predictions. J Bus Res 44(2): 67-74.
40. Young PC (1984) Recursive estimation and time-series analysis. Springer, Berlin.
41. Zurada JM, Foster BP, Ward TJ, Barker PM (1999) Neural networks versus logit regression models for financial distress response variables. J Appl Bus Res 15: 21-29.