Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario Based Particle Filter, Risk Factor Decomposition & Arbitrage Constraint Sampling

27 Pages Posted: 4 Mar 2018 Last revised: 26 Jan 2019

See all articles by Babak Mahdavi-Damghani

Babak Mahdavi-Damghani

University of Oxford - Oxford-Man Institute of Quantitative Finance

Stephen Roberts

University of Oxford - Oxford-Man Institute of Quantitative Finance

Date Written: March 4, 2018

Abstract

The change subsequent to the sub-prime crisis pushed pressure on decreased financial products complexity, going from exotics to vanilla options but increase in pricing efficiency. We introduce in this paper a more efficient methodology for vanilla option pricing using a scenario based particle filter in a hostile data environment. In doing so we capitalise on the risk factor decomposition of the the Implied Volatility surface Parameterization (IVP) recently introduced in order to define our likelihood function and therefore our sampling methodology taking into consideration arbitrage constraints.

Keywords: Implied Volatility Parametrization (IVP), Volatility Surface, SVI, gSVI, Arbitrage Free Volatility Surface, Fundamental Review of the Trading Book (FRTB)

Suggested Citation

Mahdavi-Damghani, Babak and Roberts, Stephen, Machine Learning Techniques for Deciphering Implied Volatility Surface Data in a Hostile Environment: Scenario Based Particle Filter, Risk Factor Decomposition & Arbitrage Constraint Sampling (March 4, 2018). Available at SSRN: https://ssrn.com/abstract=3133862 or http://dx.doi.org/10.2139/ssrn.3133862

Babak Mahdavi-Damghani (Contact Author)

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

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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