Publications by Antoine Savine

AAD and Machine Learning in Finance

Part 1, Wilmott, November 2019

Part 2, Wilmott, January 2020

Machine Learning in Finance

These two articles with code running simplistic Jupyter-TensorFlow (1.x) models demonstrate how vanilla neural nets (deeply) learn pricing of European calls and high dimensional basket options – the notebooks also compare neural nets with conventional polynomial regression models (a la LSM) and offer a quick, simple introduction to the implementation of simple 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:


AAD and Parallel Simulations

Scripting for financial derivatives

Selected Articles

Financial cash-flow scripting: beyond valuation

Modern Computational Finance: AAD and Parallel Simulations (preview)

From model to market risks: The implicit function theorem (IFT), demystified

Theory of Volatility

LSM Reloaded: Differentiate xVA on your iPad mini


Deep Analytics

A brief history of scripting

Modern Computational Finance: AAD and Parallel Simulations (story)

Two questions to test your quant skills

Introduction to Interest Rate Models

Sobol sequences explained

Exporting C++ to Excel: a quick and painless tutorial

Slides on SlideShare

A brief history of discounting

Practical implementation of AAD

Fuzzy logic and financial risk

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