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An evolutionary computing approach to minimize dynamic hedging error

Nahavandi, Saeid and Khoshnevisan, Mohammad 2003, An evolutionary computing approach to minimize dynamic hedging error, in Papers : BISC FLINT-CIBI International Joint Workshop on Soft Computing for Internet and Bioinformatics, University of California, Department of Electrical Engineering and Computer Sciences, Berkeley, Calif..

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Title An evolutionary computing approach to minimize dynamic hedging error
Author(s) Nahavandi, Saeid
Khoshnevisan, Mohammad
Conference name BISC FLINT-CIBI International Joint Workshop on Soft Computing for Internet and Bioinformatics (2003 : Berkeley, Calif.)
Conference location Berkeley, Calif.
Conference dates 15-19 Dec. 2003
Title of proceedings Papers : BISC FLINT-CIBI International Joint Workshop on Soft Computing for Internet and Bioinformatics
Editor(s) Masoud, Nikravesh
Publication date 2003
Publisher University of California, Department of Electrical Engineering and Computer Sciences
Place of publication Berkeley, Calif.
Summary The objective of our present paper is to derive a computationally efficient genetic pattern learning algorithm to evolutionarily derive the optimal rebalancing weights (i.e. dynamic hedge ratios) to engineer a structured financial product out of a multiasset, best-of option. The stochastic target function is formulated as an expected squared cost of hedging (tracking) error which is assumed to be partly dependent on the governing Markovian process underlying the individual asset returns and partly on
randomness i.e. pure white noise. A simple haploid genetic algorithm is advanced as an alternative numerical scheme, which is deemed to be
computationally more efficient than numerically deriving an explicit solution to the formulated optimization model. An extension to our proposed scheme is suggested by means of adapting the Genetic Algorithm parameters based on fuzzy logic controllers.
Language eng
Field of Research 080399 Computer Software not elsewhere classified
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category E2 Full written paper - non-refereed / Abstract reviewed
Copyright notice ©2003, University of California, Department of Electrical Engineering and Computer Sciences
Persistent URL http://hdl.handle.net/10536/DRO/DU:30014103

Document type: Conference Paper
Collection: School of Engineering and Technology
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