| dc.creator | Maciel L.S. | |
| dc.date | 2011 | |
| dc.date | 2015-06-30T20:43:30Z | |
| dc.date | 2015-11-26T14:54:13Z | |
| dc.date | 2015-06-30T20:43:30Z | |
| dc.date | 2015-11-26T14:54:13Z | |
| dc.date.accessioned | 2018-03-28T22:06:05Z | |
| dc.date.available | 2018-03-28T22:06:05Z | |
| dc.identifier | | |
| dc.identifier | Fuzzy Economic Review. , v. 16, n. 2, p. 59 - 73, 2011. | |
| dc.identifier | 11360593 | |
| dc.identifier | | |
| dc.identifier | http://www.scopus.com/inward/record.url?eid=2-s2.0-84858048676&partnerID=40&md5=7cacdda9d013bab3b3d52fd3b05c6101 | |
| dc.identifier | http://www.repositorio.unicamp.br/handle/REPOSIP/108998 | |
| dc.identifier | http://repositorio.unicamp.br/jspui/handle/REPOSIP/108998 | |
| dc.identifier | 2-s2.0-84858048676 | |
| dc.identifier.uri | http://repositorioslatinoamericanos.uchile.cl/handle/2250/1255094 | |
| dc.description | Recently, option pricing has become the focus of risk managers, policymakers, traders and more generally all market participants, since they find valuable information in these contracts. This paper suggests the pricing performance evaluation on Brazilian exchange rate R$ (Reais) per US$ (U.S. Dollar) option contracts, traded at the Brazilian derivatives market, using an adaptive networkbased fuzzy inference system, for the period from April 1999 to April 2009. A fuzzy rule-based system was built with a family of conditional if-then statements whose consequent are functions of the antecedents, and then composed with the aid of fuzzy neurons. The ANFIS model was compared against the Black closedform formula and some neural networks topologies, considering traditional error measures and statistical tests. The results showed that the ANFIS model outperforms closed-form formula methodology in pricing Brazilian currency options, mainly for out-of-the money contracts. | |
| dc.description | 16 | |
| dc.description | 2 | |
| dc.description | 59 | |
| dc.description | 73 | |
| dc.description | Abbasi, E., Abouec, A., Stock price forecasting by using neuro-fuzzy inference system (2008) Proceedings of the World Academy of Science, Engineering and Technology, 36, pp. 320-323 | |
| dc.description | Afolab, M., Olatoyosi, O., Predicting stock prices using a hybrid Kohonen self-organizing map (SOM) (2007) 40 th Annual Hawaii International Conference on System Sciences, pp. 1-8 | |
| dc.description | Andreou, P.C., Charalambous, C., Martzoukos, S.H., Pricing and trading European options by combining artificial neural networks and parametric models with implied parameters (2008) European Journal of Operational Research, 185 (3), pp. 1415-1433. , DOI 10.1016/j.ejor.2005.03.081, PII S0377221706006230 | |
| dc.description | Bennel, J., Sutcliffe, C., Black-Scholes versus artificial neural networks in pricing FTSE-100 options (2004) Intelligent Systems in Account, Finance & Management, 12 (4), pp. 243-260 | |
| dc.description | Black, F., The pricing of commodity contracts (1976) Journal of Financial Economics, 3, pp. 167-179 | |
| dc.description | Black, F., Scholes, M., The pricing of options and corporate liabilities (1973) Journal of Political Economy, 12 (3), pp. 637-654 | |
| dc.description | Bollerslev, T., Generalized autoregressive conditional heteroskedasticity (1986) Journal of Econometrics, 31, pp. 303-327 | |
| dc.description | Boyacioglu, M.A., Avci, D., An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange (2010) Expert Systems with Applications, 37 (12), pp. 7908-7912 | |
| dc.description | Chang, P.C., Liu, C.H., A TKS type fuzzy rule based system for stock price prediction (2008) Expert Systems with Applications, 34 (1), pp. 135-144 | |
| dc.description | Figa-Talamanca, G., Guerra, M.L., Fuzzy option value with stochastic volatility models (2009) 9 th International Conference on Intelligent Systems Design and Applications, , Pisa, Italy | |
| dc.description | Gencay, R., Qi, M., Pricing and hedging derivative securities with neural networks: Bayesian regularization, early stopping, and bagging (2001) IEEE Transactions on Neural Networks, 12 (4), pp. 726-734. , DOI 10.1109/72.935086, PII S1045922701050202 | |
| dc.description | Granger, J.W.C., Newbold, P., (1977) Forecasting Economic Time Series, , New York, Academic Press | |
| dc.description | Healy, J., Dixon, M., Read, B., Cai, F.F., A data-centric approach to understanding the pricing of financial options (2002) European Physical Journal B, 27 (2), pp. 219-227 | |
| dc.description | Jang, J.R., ANFIS: Adaptive-network-based fuzzy inference system (1993) Transactions on Systems, Man and Cybernetics, 23 (3), pp. 665-685 | |
| dc.description | Lehmann, E.L., (1988) Nonparametric Statistical Methods Based on Ranks, , New Jersey, Prentice Hall | |
| dc.description | Leu, Y., A fuzzy time series-based neural network approach to option pricing forecasting (2010) Lecture Notes in Computer Science, 5990, pp. 360-369 | |
| dc.description | Liang, X., Zhang, H., Xiao, J., Chen, Y., Improving option pricing forecasts with neural networks and support vector regressions (2009) Neurocomputing, 72, pp. 3055-3065 | |
| dc.description | Liu, F., Pricing currency options based on fuzzy techniques (2009) European Journal of Operational Research, 193 (2), pp. 530-540 | |
| dc.description | Nelson, D.B., Conditional heteroskedasticity in asset returns: A new approach (1991) Econometrica, 59, pp. 347-370 | |
| dc.description | Nelson, D.B., Cao, C.Q., Inequality constraints in the univariate GARCH model (1992) Journal of Business & Economic Statistics, 10, pp. 229-235 | |
| dc.description | Thiagarajah, K., Appadoo, S.S., Thavaneswaran, A., Option valuation model with adaptive fuzzy numbers (2007) Computers and Mathematics with Applications, 53 (5), pp. 831-841. , DOI 10.1016/j.camwa.2007.01.011, PII S0898122107000375 | |
| dc.description | Trikle, B.S., Forecasting annual excess stock returns via an adaptive network-based fuzzy inference system (2006) Intelligent Systems in Accounting, Finance & Management, 13 (3), pp. 165-177 | |
| dc.description | Schwarz, G., Estimating the dimension of a model (1978) Annals of Statistics, 6 (2), pp. 461-468 | |
| dc.description | Shea, G.S., Interest rate term structure estimation with exponential splines: A note (1985) The Journal of Finance, 40 (1), pp. 319-325 | |
| dc.description | Wang, Y., Using neural network to forecast stock index option price: A new hybrid GARCH approach (2009) Quality & Quantity, 43 (5), pp. 833-843 | |
| dc.description | Wu, H.-C., Pricing European options based on the fuzzy pattern of Black-Scholes formula (2004) Computers and Operations Research, 31 (7), pp. 1069-1081. , DOI 10.1016/S0305-0548(03)00065-0 | |
| dc.description | Wu, H.-C., European option pricing under fuzzy environments (2005) International Journal of Intelligent Systems, 20 (1), pp. 89-102. , DOI 10.1002/int.20055 | |
| dc.description | Yang, Z.R., Platt, M.B., Platt, H.D., Probabilistic neural network in bankruptcy predictions (1999) Journal of Business Research, 44, pp. 67-74 | |
| dc.description | Zhan, H., Pricing asian options using fuzzy theory (2010) International Conference on Artificial Intelligence and Education (ICAIE), pp. 226-230. , Hangzhou, China | |
| dc.description | Zurada, J.M., Foster, B.P., Ward, T.J., Barker, P.M., Neural networks versus logit regression models for financial distress response variables (1999) Journal of Applied Business Research, 15, pp. 21-29 | |
| dc.language | en | |
| dc.publisher | | |
| dc.relation | Fuzzy Economic Review | |
| dc.rights | fechado | |
| dc.source | Scopus | |
| dc.title | Pricing Brazilian Exchange Rate Options Using An Adaptive Network-based Fuzzy Inference System | |
| dc.type | Artículos de revistas | |
