Deep learning calibration of option pricing models: some pitfalls and solutions

Addressing model calibration and the issue of no-arbitrage in a deep learning approach


Andrey Itkin considers a classical problem of mathematical finance: the calibration of option pricing models to market data. He highlights some pitfalls in the existing approaches and proposes resolutions that improve both performance and the accuracy of calibration. Itkin also addresses the problem of no-arbitrage pricing when using a trained neural net, which is currently ignored in the literature

Recently, a classical problem of mathematical finance – the

Only users who have a paid subscription or are part of a corporate subscription are able to print or copy content.

To access these options, along with all other subscription benefits, please contact [email protected] or view our subscription options here:

You are currently unable to copy this content. Please contact [email protected] to find out more.

To continue reading...

You need to sign in to use this feature. If you don’t have a account, please register for a trial.

Sign in
You are currently on corporate access.

To use this feature you will need an individual account. If you have one already please sign in.

Sign in.

Alternatively you can request an individual account here: