Hedging derivative securities with genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{Chen:1999:ISAFM,
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author = "Shu-Heng Chen and Wo-Chiang Lee and Chia-Hsuan Yeh",
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title = "Hedging derivative securities with genetic
programming",
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journal = "Intelligent Systems in Accounting, Finance and
Management",
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year = "1999",
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volume = "8",
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number = "4",
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pages = "237--251",
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month = dec,
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note = "Special Issue: Machine Learning and Data Mining in
Finance",
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keywords = "genetic algorithms, genetic programming, option
pricing, Black-Scholes model, tracking error",
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ISSN = "1099-1174",
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DOI = "doi:10.1002/(SICI)1099-1174(199912)8:4%3C237::AID-ISAF174%3E3.0.CO%3B2-J",
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size = "15 pages",
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abstract = "One of the most recent applications of GP to finance
is to use genetic programming to derive option pricing
formulae. Earlier studies take the BlackScholes model
as the true model and use the artificial data generated
by it to train and to test GP. The aim of this paper is
to provide some initial evidence of the empirical
relevance of GP to option pricing. By using the real
data from S&P 500 index options, we train and test our
GP by distinguishing the case in-the-money from the
case out-of-the-money. Unlike most empirical studies,
we do not evaluate the performance of GP in terms of
its pricing accuracy. Instead, the derived GP tree is
compared with the Black-Scholes model in its capability
to hedge. To do so, a notion of tracking error is taken
as the performance measure. Based on the post-sample
performance, it is found that in approximately
20percent of the 97 test paths GP has a lower tracking
error than the Black--Scholes formula. We further
compare our result with the ones obtained by radial
basis functions and multilayer perceptrons and
one-stage GP",
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notes = "See also \cite{chen:1998:hdsGP}",
- }
Genetic Programming entries for
Shu-Heng Chen
Woh-Chiang Lee
Chia Hsuan Yeh
Citations