Test of recent advances in extracting information from option prices
Gregoriou, A.; Healy, J. V.; Hudson, R.
J. V. Healy
Professor Robert Hudson Robert.Hudson@hull.ac.uk
Professor of Finance
© 2017 Elsevier Inc. A large literature exists on techniques for extracting probability distributions for future asset prices from option prices. No definitive method has been developed however. The parametric 'mixture of normals', and non-parametric 'smoothed implied volatility' methods remain the most widespread approaches. These though are subject to estimation errors due to discretization, truncation, and noise. Recently, several authors have derived 'model free' formulae for computing the moments of the risk neutral density (RND) directly from option prices, without first estimating the full density. The accuracy of these formulae is studied here for the first time. The Black-Scholes formula is used to generate option prices, and error curves for the first 4 moments of the RND are computed using the 'model-free' formulae. It is found that, in practice, the formulae are prone to large and economically significant errors, because they contain definite integrals that can only be solved numerically. We show that without mathematically equivalent expressions with analytical solutions the formulae are difficult to deploy effectively in practice.
Healy, J. V., Gregoriou, A., & Hudson, R. (2018). Test of recent advances in extracting information from option prices. International review of financial analysis, 56, 292-302. https://doi.org/10.1016/j.irfa.2017.09.011
|Journal Article Type||Article|
|Acceptance Date||Sep 24, 2017|
|Online Publication Date||Sep 27, 2017|
|Deposit Date||Sep 26, 2017|
|Publicly Available Date||Mar 28, 2019|
|Journal||International review of financial analysis|
|Peer Reviewed||Peer Reviewed|
|Keywords||Economics and Econometrics; Finance|
|Additional Information||This is the accepted manuscript of an article published in International review of financial analysis, 2017. The version of record is available at the DOI link in this record.|
Publisher Licence URL
©2019, Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
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