Efficient option pricing via a globally regularized neural network
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SCOPUS
- Title
- Efficient option pricing via a globally regularized neural network
- Authors
- Choi, HJ; Lee, HS; Han, GS; Lee, J
- Date Issued
- 2004-01
- Publisher
- SPRINGER-VERLAG BERLIN
- 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.
- Keywords
- HEDGING DERIVATIVE SECURITIES; ALGORITHM
- URI
- https://oasis.postech.ac.kr/handle/2014.oak/17743
- DOI
- 10.1007/978-3-540-28648-6_157
- ISSN
- 0302-9743
- Article Type
- Article
- Citation
- LECTURE NOTES IN COMPUTER SCIENCE, vol. 3174, page. 988 - 993, 2004-01
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