A neural network trained to invert rough Heston parameters from implied volatility surfaces relies most on short-maturity deep in-the-money prices, a pattern absent in the standard Heston model.
Deep learning calibration of option pricing models: some pitfalls and solutions
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abstract
Recent progress in the field of artificial intelligence, machine learning and also in computer industry resulted in the ongoing boom of using these techniques as applied to solving complex tasks in both science and industry. Same is, of course, true for the financial industry and mathematical finance. In this paper we consider a classical problem of mathematical finance - calibration of option pricing models to market data, as it was recently drawn some attention of the financial society in the context of deep learning and artificial neural networks. We highlight some pitfalls in the existing approaches and propose resolutions that improve both performance and accuracy of calibration. We also address a problem of no-arbitrage pricing when using a trained neural net, that is currently ignored in the literature.
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Deep learning interpretability for rough volatility
A neural network trained to invert rough Heston parameters from implied volatility surfaces relies most on short-maturity deep in-the-money prices, a pattern absent in the standard Heston model.