Deep learning derivatives pricing

I made two simplistic TensorFlow (1.x) notebooks for the benefit of my students at Copenhagen University, to demonstrate how vanilla neural nets (deeply) learn pricing of European calls and high dimensional basket options, together with a comparison with conventional polynomial regression models (a la LSM) and a quick, simple introduction to the implementation of deep learning models in TensorFlow.

Notebook 1: European call in Black and Scholes

Notebook 2: Basket option in Bachelier

The notebooks run directly on Google colab, in the cloud and on GPU, no installation required.

The lecture slides and material are all found here:

Some (much) more advanced considerations for efficiently learning prices of trading books, including “twin” neural nets who learn values and risks, and the super efficient differential regularization, are found here:

Recorded workshop from Kings College London: AAD, Backpropagation and Machine Learning in Finance

Back in March, I gave a series of lectures at Kings College London on automatic adjoint differentiation, backpropagation and machine learning, and how it all connects and applies to risk management of financial derivatives.

The lectures were recorded and made freely available online, either from Kings own page:

or, on YouTube:

It is my understanding that the audience at Kings found the talks useful, and I hope that they may be beneficial to a wider audience on the internet.