Option implied volatility estimation: A computational intelligent approach

Stanley Cho; Gang Dong; Kin Keung La

doi:10.1109/CSO.2011.197

Option implied volatility estimation: A computational intelligent approach

Stanley Cho
, Gang Dong
, Kin Keung La

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

1 Citation (Scopus)

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. © 2011 IEEE.

Original language	English
Title of host publication	Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011
Pages	545-547
DOIs	https://doi.org/10.1109/CSO.2011.197
Publication status	Published - 2011
Event	4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 - Kunming, Lijiang, Yunnan, China Duration: 15 Apr 2011 → 19 Apr 2011

Conference

Conference	4th International Joint Conference on Computational Sciences and Optimization, CSO 2011
Place	China
City	Kunming, Lijiang, Yunnan
Period	15/04/11 → 19/04/11

Research Keywords

Black-scholes model
Hang seng index option
Implied volatility
Least square support vector machine

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10.1109/CSO.2011.197

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@inproceedings{137f30052da44f1fa60d4e8e04958980,

title = "Option implied volatility estimation: A computational intelligent approach",

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. {\textcopyright} 2011 IEEE.",

keywords = "Black-scholes model, Hang seng index option, Implied volatility, Least square support vector machine",

author = "Stanley Cho and Gang Dong and La, \{Kin Keung\}",

year = "2011",

doi = "10.1109/CSO.2011.197",

language = "English",

isbn = "9780769543352",

pages = "545--547",

booktitle = "Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011",

note = "4th International Joint Conference on Computational Sciences and Optimization, CSO 2011 ; Conference date: 15-04-2011 Through 19-04-2011",

}

Cho, S, Dong, G & La, KK 2011, Option implied volatility estimation: A computational intelligent approach. in Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011., 5957721, pp. 545-547, 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011, Kunming, Lijiang, Yunnan, China, 15/04/11. https://doi.org/10.1109/CSO.2011.197

Option implied volatility estimation: A computational intelligent approach. / Cho, Stanley; Dong, Gang; La, Kin Keung.
Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011. 2011. p. 545-547 5957721.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - Option implied volatility estimation

T2 - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011

AU - Cho, Stanley

AU - Dong, Gang

AU - La, Kin Keung

PY - 2011

Y1 - 2011

N2 - 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. © 2011 IEEE.

AB - 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. © 2011 IEEE.

KW - Black-scholes model

KW - Hang seng index option

KW - Implied volatility

KW - Least square support vector machine

UR - https://www.scopus.com/pages/publications/80051987685

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-80051987685&origin=recordpage

U2 - 10.1109/CSO.2011.197

DO - 10.1109/CSO.2011.197

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769543352

SP - 545

EP - 547

BT - Proceedings - 4th International Joint Conference on Computational Sciences and Optimization, CSO 2011

Y2 - 15 April 2011 through 19 April 2011

ER -

Option implied volatility estimation: A computational intelligent approach

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