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- W3126785561 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." @default.
- W3126785561 created "2021-02-15" @default.
- W3126785561 creator A5035270942 @default.
- W3126785561 date "2019-01-01" @default.
- W3126785561 modified "2023-09-27" @default.
- W3126785561 title "Deep learning calibration of option pricing models: some pitfalls and solutions" @default.
- W3126785561 hasPublicationYear "2019" @default.
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