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SANOS: smooth strictly arbitrage-free non-parametric option surfaces

Abstract:
We present a simple, numerically efficient but highly flexible non-parametric method to construct representations of option price surfaces which are both smooth and strictly arbitrage-free across time and strike. The method can be viewed as a smooth generalization of the widely-known linear interpolation scheme, and retains the simplicity and transparency of that baseline. Calibration of the model to observed market quotes is formulated as a linear program, allowing bid-ask spreads to be incorporated directly via linear penalties or inequalities, and delivering materially lower computational cost than most of the currently available implied-volatility surface fitting routines. As a further contribution, we derive an equivalent parameterization of the proposed surface in terms of strictly positive "discrete local volatility" variables. This yields, to our knowledge, the first construction of smooth, strictly arbitrage-free option price surfaces while requiring only trivial parameter constraints (positivity). We illustrate the approach using S&P 500 index options.
Publication status:
Published
Peer review status:
Not peer reviewed

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Preprint server copy:
10.48550/arXiv.2601.11209

Authors

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Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0002-8418-7284
More by this author
Institution:
University of Oxford
Division:
MPLS
Department:
Mathematical Institute
Role:
Author
ORCID:
0000-0002-6330-5480


More from this funder
Funder identifier:
https://ror.org/01h531d29
Funding agency for:
Kratsios, A
Grant:
DGECR-2023-00230
RGPIN-2023-04482
More from this funder
Funder identifier:
https://ror.org/00k4n6c32
Funding agency for:
Kratsios, A
Programme:
Bando PRIN 2022
More from this funder
Funder identifier:
https://ror.org/03kqdja62
Funding agency for:
Kratsios, A
Saqur, R


Preprint server:
arXiv
Publication date:
2026-01-16
DOI:


Language:
English
Pubs id:
2363634
Local pid:
pubs:2363634
Deposit date:
2026-01-23
ARK identifier:

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