Pith. sign in

REVIEW 1 cited by

To sigmoid-based functional description of the volatility smile

Not yet reviewed by Pith; the record is open.

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1407.0256 v3 pith:7A6D2DYP submitted 2014-07-01 q-fin.MF q-fin.CPq-fin.GN

classification q-fin.MFq-fin.CPq-fin.GN
keywords impliedvolatilitydemonstrateparameterizationwellarbitrage-freegridsmile
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
read the original abstract

We propose a new static parameterization of the implied volatility surface which is constructed by using polynomials of sigmoid functions combined with some other terms. This parameterization is flexible enough to fit market implied volatilities which demonstrate smile or skew. An arbitrage-free calibration algorithm is considered that constructs the implied volatility surface as a grid in the strike-expiration space and guarantees a lack of arbitrage at every node of this grid. We also demonstrate how to construct an arbitrage-free interpolation and extrapolation in time, as well as build a local volatility and implied pdf surfaces. Asymptotic behavior of this parameterization is discussed, as well as results on stability of the calibrated parameters are presented. Numerical examples show robustness of the proposed approach in building all these surfaces as well as demonstrate a better quality of the fit as compared with some known models.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. On deep calibration of (rough) stochastic volatility models

    q-fin.MF 2019-08 conditional novelty 6.0 of 10

    A two-step deep calibration method learns the rough Bergomi implied-volatility map with a small neural network and then calibrates with Levenberg-Marquardt, achieving millisecond calibration.

Pith tools