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Deep calibration of the quadratic rough Heston model
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The quadratic rough Heston model provides a natural way to encode Zumbach effect in the rough volatility paradigm. We apply multi-factor approximation and use deep learning methods to build an efficient calibration procedure for this model. We show that the model is able to reproduce very well both SPX and VIX implied volatilities. We typically obtain VIX option prices within the bid-ask spread and an excellent fit of the SPX at-the-money skew. Moreover, we also explain how to use the trained neural networks for hedging with instantaneous computation of hedging quantities.
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Cited by 1 Pith paper
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Joint deep calibration of the 4-factor PDV model
Neural networks trained on least-squares Monte Carlo data calibrate the 4-factor path-dependent volatility model to SPX and VIX markets in about five seconds per date.
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