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
Date Written: March 4, 2018
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)
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