 A data and digital-contracts driven method for pricing complex derivativesJun Lu and Hiroshi Ohta Quantitative Finance, 2003, vol. 3, issue 3, 212-219 Abstract: In this paper, we propose a data and digital-contracts driven (DDCD) method for pricing various complex options. The DDCD method is a combination of nonparametric and parametric methods. In general, nonparametric data driven methods use observed data as training data of a learning network directly. Different from these, in the proposed DDCD method, some European-style digital contracts (DCs) of the underlying assets are added as auxiliary information to guide the learning process of the pricing formula. The DCs can be obtained by using the observed data according to parametric methods. Thus, the DCs are actually used as the hints of the pricing formula, and then the DDCD method has superior pricing accuracy to the common data driven method in practical applications. Some Monte Carlo simulation experiments are performed and the results demonstrate that the proposed method not only has the advantages of generalization and superior accuracy, as the non-parametric method has, but also has the property of robustness to financial data with noise, as the parametric method has. Date: 2003 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:3:y:2003:i:3:p:212-219 Ordering information: This journal article can be ordered from DOI: 10.1088/1469-7688/3/3/307 Access Statistics for this article Quantitative Finance is currently edited by Michael Dempster and Jim Gatheral More articles in Quantitative Finance from Taylor & Francis Journals |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.