Determining and benchmarking risk neutral distributions implied from option prices

Oliver Salazar Celis, Lingzhi Liang, Damiaan Lemmens, Jacques Tempère and Annie Cuyt

Applied Mathematics and Computation, 2015, vol. 258, issue C, 372-387

Abstract: Risk neutral probability density functions (RNDs) play a central role in assessing models for stock market behavior. However, it remains challenging to distill a realistic estimate for the RND from empirical data. In this work we introduce a novel method to infer a RND estimate from observed option prices. Our method efficiently yields a realistic rational function approximation to the RND, it is flexible w.r.t. the shape of the underlying distribution and robust in the presence of noise. To show this, we first investigate how well a method can actually retrieve a known distribution from noisy option prices. Then we consider real market data and show how our method can be used to derive a single continuously differentiable RND estimate from empirical call and put option price data.

Keywords: Implied probability density function; Benchmarking; S&P 500 Index options; Rational approximations; Option pricing; Bid-ask interval (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:apmaco:v:258:y:2015:i:c:p:372-387

DOI: 10.1016/j.amc.2015.02.011

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