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.
To sigmoid-based functional description of the volatility smile
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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.
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On deep calibration of (rough) stochastic volatility models
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.