Comparison of methods to estimate option implied risk-neutral densities
Quantitative Finance, 2014, vol. 14, issue 10, 1839-1855
This paper is a comparison study of non-parametric techniques used to estimate risk-neutral densities from option prices. Cross-sectional option prices are first generated using Monte Carlo simulation. Using these simulated options data, risk-neutral densities of the underlying asset are estimated using three different non-parametric methods. The performances of these non-parametric estimation methods are then evaluated by comparing the estimated densities with the theoretical density. Unlike previous comparison studies that use traded options data without knowing the true risk-neutral densities, this study uses simulated option data with known data-generating processes and their corresponding risk-neutral densities, hence giving a real evaluation of the non-parametric estimation methods. This study finds that the kernel regression method yields the best performance, followed by the spline interpolation method and the neural network models.
References: View references in EconPapers View complete reference list from CitEc
Citations View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Access to full text is restricted to subscribers.
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:taf:quantf:v:14:y:2014:i:10:p:1839-1855
Ordering information: This journal article can be ordered from
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
Bibliographic data for series maintained by Chris Longhurst ().