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Paper Information

Journal:   SCIENTIA IRANICA   2014 , Volume 21 , Number 3 (TRANSACTIONS D: COMPUTER SCIENCE AND ENGINEERING AND ELECTRICAL ENGINEERING); Page(s) 815 To 825.
 
Paper: 

A STRATEGY FOR FORECASTING OPTION PRICES USING FUZZY TIME SERIES AND LEAST SQUARE SUPPORT VECTOR REGRESSION WITH A BOOTSTRAP MODEL

 
 
Author(s):  LEE C.P.*, LIN W.C., YANG C.C.
 
* DEPARTMENT OF INFORMATION MANAGEMENT, DA-YEH UNIVERSITY, NO.168, UNIVERSITY RD., DACUN, CHANGHUA 515, TAIWAN
 
Abstract: 

Recently, the strategy for forecasting option price has become a popular nancial topic because options are important tools on risk management in nancial investments. The well-known Black-Scholes model (B-S model) is widely used for option pricing. In B-S model, the normal distribution assumption is important. However, the nancial data in real life may not follow the normal distribution assumption. For this reason, this paper proposes a novel hybrid model, which is a nonlinear prediction model without normal distribution assumptions to forecast the option prices. The proposed model is composed of a Fuzzy Time Series (FTS) model, a Least Square Support Vector Regression (LSSVR), and a bootstrap method. In the proposed model, FTS model is rstly used to fuzzify dataset and to build historical database. Subsequently, the proposed method uses the remainder contractual time to search similar historical trends as training samples. Finally, we use the bootstrap method on LSSVR to enhance the prediction accuracy. The experiment results show that the proposed model outperforms traditional time series models and several hybrid models in terms of the Root Mean Square Error (RMSE), the Mean Absolute Error (MAE) and the correlation coe cient (r) of actual and forecasted option price.

 
Keyword(s): OPTION PRICE, FUZZY TIME SERIES, LEAST SQUARE SUPPORT VECTOR REGRESSION, BOOTSTRAP, HYBRID MODEL
 
References: 
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