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    Monte Carlo analysis of methods for extracting risk-neutral densities with affine jump diffusions

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    Publication date
    2019-12
    Author
    Lu, Shan
    Keyword
    Risk-neutral density
    Monte Carlo simulation
    Affine jump diffusions
    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.
    Peer-Reviewed
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    Abstract
    This 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.
    URI
    http://hdl.handle.net/10454/17201
    Version
    Accepted manuscript
    Citation
    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.
    Link to publisher’s version
    https://doi.org/10.1002/fut.22049
    Type
    Article
    Collections
    Management and Law Publications

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