![]() " Evaluating the Performance of Machine Learning Algorithms in Financial Market Forecasting: A Comprehensive Survey," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. " Pricing And Hedging Short Sterling Options Using Neural Networks," " Neural networks for option pricing and hedging: a literature review," Christophe Chorro & Dominique Guegan & Florian Ielpo, 2010.Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) " Martingalized Historical approach for Option Pricing," Christophe Chorro & Dominique Guegan & Florian Ielpo, 2009.0(4), pages 129-140, December.įull references (including those not matched with items on IDEAS) Journal for Economic Forecasting, Institute for Economic Forecasting, vol. " Scenarios of the Romanian GDP Evolution With Neural Models," Journal of Political Economy, University of Chicago Press, vol. " The Pricing of Options and Corporate Liabilities," Black, Fischer & Scholes, Myron S, 1973.Physica A: Statistical Mechanics and its Applications, Elsevier, vol. " Artificial neural network model of the hybrid EGARCH volatility of the Taiwan stock index option prices," Tseng, Chih-Hsiung & Cheng, Sheng-Tzong & Wang, Yi-Hsien & Peng, Jin-Tang, 2008." Option pricing in a Garch model with tempered stable innovations,"įinance Research Letters, Elsevier, vol. " Pricing financial derivatives with neural networks," Morelli, Marco J & Montagna, Guido & Nicrosini, Oreste & Treccani, Michele & Farina, Marco & Amato, Paolo, 2004." Weighted fuzzy time series models for TAIEX forecasting," Monash Econometrics and Business Statistics Working Papersġ3/05, Monash University, Department of Econometrics and Business Statistics. " Another Look at Measures of Forecast Accuracy," The experiment results show that the proposed model outperforms several existing methods in terms of RMSE, MAE and the testing results of Diebold-Marioano test. Accordingly, this paper uses the bootstrap method to enhance the prediction accuracy of the proposed model. However, the sample size of option price data is small. In the proposed model, the dynamic n-order 2-factor fuzzy time series model can automatic choose the best n-order for searching similar data from historical data and, then, build a training dataset for the radial basis function neural network model to forecast the option price. The proposed model, termed as the dynamic weighted distance-based fuzzy time series neural network with bootstrap model, is composed of a dynamic n-order 2-factor fuzzy time series model, a radial basis function neural network model and a bootstrap method. To counter this problem, this paper proposes a novel hybrid model to forecast the option price. Accordingly, it is difficult to predict option price accurately. The option price forecasting is still a big challenging problem because the option pricing is determined by many factors. ![]()
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |