Convergence of Heston to SVI Proposed Extensions: Rational & Conjecture for the Convergence of Extended Heston to the Implied Volatility Surface Parametrization

31 Pages Posted: 21 Sep 2017 Last revised: 30 Aug 2018

See all articles by Babak Mahdavi-Damghani

Babak Mahdavi-Damghani

University of Oxford - Oxford-Man Institute of Quantitative Finance

Konul Mustafayeva

King's College London

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: September 18, 2017

Abstract

A mathematical and a market argument on the sub-linearity of the wings for the implied variance is given. Gatheral stochastic volatility inspired (SVI) parameterization claim to have two key properties that have led to its subsequent popularity with practitioners is exposed. Namely the linearity in the log-strike k as |k| → ∞ consistent with Roger Lees moment formula as well as its connection to the Heston model are examined more in details. Though correct, the former point led to the model subsequent decommission in the industry. We explain this apparent contradiction by pointing to a mathematically convenient chosen factor in the Heston model which we expose and consequently introduce couple candidates: the p-Heston and the Inferred Correlation models instead. The link between the latter and the SVI being broken, we propose a connection to the Implied Volatility surface Parametrisation (IVP) recently introduced and propose a conjecture between a mirror convergence towards these models using the parallel between the traditional Heston to SVI convergence.

Keywords: Volatility Smile, Implied Volatility Wings, Heston Model, P-Heston, IVP, SVI, Stochastic Volatility, Implied Volatility Surface Parametrization, Asymptotic Convergence, Local Correlation Surface

Suggested Citation

Mahdavi-Damghani, Babak and Mustafayeva, Konul and Roberts, Stephen, Convergence of Heston to SVI Proposed Extensions: Rational & Conjecture for the Convergence of Extended Heston to the Implied Volatility Surface Parametrization (September 18, 2017). Available at SSRN: https://ssrn.com/abstract=3039185 or http://dx.doi.org/10.2139/ssrn.3039185

Babak Mahdavi-Damghani (Contact Author)

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

United Kingdom

Konul Mustafayeva

King's College London ( email )

Strand
London, England WC2R 2LS
United Kingdom

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance ( email )

Eagle House
Walton Well Road
Oxford, Oxfordshire OX2 6ED
United Kingdom

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