Efficient option pricing via a globally regularized neural network

Hyung Jun Choi; Hyo Seok Lee; Gyu Sik Han; Jaewook Lee

doi:10.1007/978-3-540-28648-6_157

Efficient option pricing via a globally regularized neural network

Hyung Jun Choi^*
, Hyo Seok Lee
, Gyu Sik Han
, Jaewook Lee

^*Corresponding author for this work

Department of Business Administration

Pohang University of Science and Technology

Research output: Contribution to conference › Chapter › peer-review

8 Scopus citations

Abstract

Nonparametric approaches of option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel neural network learning algorithm for option-pricing, which is a nonparametric approach. The proposed method is devised to improve generalization and computing time. Experimental results are conducted for the KOSPI200 index daily call options and demonstrate a significant performance improvement to reduce test error compared to other existing techniques.

Original language	English
Title of host publication	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Fuliang Yin, Chengan Guo, Jun Wang
Publisher	Springer Verlag
Pages	988-993
Number of pages	6
ISBN (Print)	3540228438, 9783540228431
DOIs	https://doi.org/10.1007/978-3-540-28648-6_157
State	Published - 2004

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3174
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

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10.1007/978-3-540-28648-6_157

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Choi, H. J., Lee, H. S., Han, G. S., & Lee, J. (2004). Efficient option pricing via a globally regularized neural network. In F. Yin, C. Guo, & J. Wang (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 988-993). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174). Springer Verlag. https://doi.org/10.1007/978-3-540-28648-6_157

Choi, Hyung Jun ; Lee, Hyo Seok ; Han, Gyu Sik et al. / Efficient option pricing via a globally regularized neural network. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Fuliang Yin ; Chengan Guo ; Jun Wang. Springer Verlag, 2004. pp. 988-993 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Nonparametric approaches of option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel neural network learning algorithm for option-pricing, which is a nonparametric approach. The proposed method is devised to improve generalization and computing time. Experimental results are conducted for the KOSPI200 index daily call options and demonstrate a significant performance improvement to reduce test error compared to other existing techniques.",

author = "Choi, \{Hyung Jun\} and Lee, \{Hyo Seok\} and Han, \{Gyu Sik\} and Jaewook Lee",

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Choi, HJ, Lee, HS, Han, GS & Lee, J 2004, Efficient option pricing via a globally regularized neural network. in F Yin, C Guo & J Wang (eds), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3174, Springer Verlag, pp. 988-993. https://doi.org/10.1007/978-3-540-28648-6_157

Efficient option pricing via a globally regularized neural network. / Choi, Hyung Jun; Lee, Hyo Seok; Han, Gyu Sik et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Fuliang Yin; Chengan Guo; Jun Wang. Springer Verlag, 2004. p. 988-993 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3174).

Research output: Contribution to conference › Chapter › peer-review

TY - CHAP

T1 - Efficient option pricing via a globally regularized neural network

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AU - Lee, Hyo Seok

AU - Han, Gyu Sik

AU - Lee, Jaewook

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N2 - Nonparametric approaches of option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel neural network learning algorithm for option-pricing, which is a nonparametric approach. The proposed method is devised to improve generalization and computing time. Experimental results are conducted for the KOSPI200 index daily call options and demonstrate a significant performance improvement to reduce test error compared to other existing techniques.

AB - Nonparametric approaches of option pricing have recently emerged as alternative approaches that complement traditional parametric approaches. In this paper, we propose a novel neural network learning algorithm for option-pricing, which is a nonparametric approach. The proposed method is devised to improve generalization and computing time. Experimental results are conducted for the KOSPI200 index daily call options and demonstrate a significant performance improvement to reduce test error compared to other existing techniques.

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BT - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Choi HJ, Lee HS, Han GS, Lee J. Efficient option pricing via a globally regularized neural network. In Yin F, Guo C, Wang J, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 988-993. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-28648-6_157

Efficient option pricing via a globally regularized neural network

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