Publications by Antoine Savine

AAD and Machine Learning in Finance

Part 1, Wilmott, November 2019

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Part 2, Wilmott, January 2020

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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: github.com/asavine/CompFinLecture

Books

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

Stories

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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