Title :
Option Implied Volatility Estimation: A Computational Intelligent Approach
Author :
Choi, Stanley ; Dong, Gang ; Lai, Kin Keung
Author_Institution :
Head & Shoulders Securities Ltd., Hong Kong, China
Abstract :
Generally, option implied volatility is estimated by the inverse function of the Black-Scholes formula. The structure of Black-Scholes formula is fixed and it can not updated with new information. Therefore, in this paper, the Least Square Support Vector Machine (LSSVM) model, a novel version of Neural Networks, is proposed to estimate options´ implied volatility. It has excellent performance in approximation of complex functions. In the end, Hang Seng Index options are used to verify the performance of the LSSVM.
Keywords :
least squares approximations; stock markets; support vector machines; Black-Scholes formula; Hang Seng index options; approximation; complex functions; computational intelligent approach; inverse function; least square support vector machine model; neural networks; option implied volatility estimation; Computational modeling; Function approximation; Indexes; Least squares approximation; Mathematical model; Pricing; Support vector machines; Black-Scholes model; Hang Seng Index option; Least Square Support Vector Machine; implied volatility;
Conference_Titel :
Computational Sciences and Optimization (CSO), 2011 Fourth International Joint Conference on
Conference_Location :
Yunnan
Print_ISBN :
978-1-4244-9712-6
Electronic_ISBN :
978-0-7695-4335-2
DOI :
10.1109/CSO.2011.197
