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Calibrating rough volatility models: a convolutional neural network approach

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arxiv 1812.05315 v3 pith:ISWYMMEI submitted 2018-12-13 q-fin.CP

Henry Stone This is my paper · ORCID
classification q-fin.CP
keywords convolutionalmodelneuralapplicationapproachcalibratingcalibrationcontextualise
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In this paper we use convolutional neural networks to find the H\"older exponent of simulated sample paths of the rBergomi model, a recently proposed stock price model used in mathematical finance. We contextualise this as a calibration problem, thereby providing a very practical and useful application.

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  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.

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