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dc.contributor.authorLu, Shan
dc.date.accessioned2019-07-31T14:51:05Z
dc.date.accessioned2019-08-09T13:50:16Z
dc.date.available2019-07-31T14:51:05Z
dc.date.available2019-08-09T13:50:16Z
dc.date.issued2019-12
dc.identifier.citationLu, S (2019) Monte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusions. Journal of Futures Markets. 39(12): 1587-1612.en_US
dc.identifier.urihttp://hdl.handle.net/10454/17201
dc.descriptionYesen_US
dc.description.abstractThis paper compares several widely-used and recently-developed methods to extract risk-neutral densities (RND) from option prices in terms of estimation accuracy. It shows that positive convolution approximation method consistently yields the most accurate RND estimates, and is insensitive to the discreteness of option prices. RND methods are less likely to produce accurate RND estimates when the underlying process incorporates jumps and when estimations are performed on sparse data, especially for short time-to-maturities, though sensitivity to the discreteness of the data differs across different methods.en_US
dc.language.isoenen_US
dc.relation.isreferencedbyhttps://doi.org/10.1002/fut.22049
dc.rights© 2019 Wiley This is the peer reviewed version of the following article: Lu, S (2019) Monte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusions. Journal of Futures Markets. 39(12): 1587-1612, which has been published in final form at https://doi.org/10.1002/fut.22049. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
dc.subjectRisk-neutral densityen_US
dc.subjectMonte Carlo simulationen_US
dc.subjectAffine jump diffusionsen_US
dc.titleMonte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusionsen_US
dc.status.refereedYesen_US
dc.date.Accepted2019-07-31
dc.date.application2019-09-08
dc.typeArticleen_US
dc.type.versionAccepted manuscripten_US
dc.date.updated2019-07-31T13:51:13Z
refterms.dateFOA2019-08-09T13:51:06Z


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