Modern Computational Finance: Parts I and II free on SSRN for a limited time

SSRN link now expired — download parts I and II here for a limited time:

part III to follow

parts i and ii free for a limited time on SSRN

The first two parts (out of three) of my book Modern Computational Finance (Wiley, 2018) are complimentarily available on SSRN only in December 2020.

Part I teaches necessary C++ foundations with a focus on parallel computing. Part II summarizes the theory of financial Derivatives and develops serial and parallel Monte-Carlo pricing and risk management engines.

Part III, not included in this preview, discusses and develops the critical adjoint differentiation (AD) technology, its professional implementation in C++ and its deployment in risk management platforms.

In recent years, AD revolutionized the field of quantitative finance. It brought us real-time risk reports and instantaneous calibration. It also enabled extremely promising new directions of research. For instance, the Risk article Differential Machine Learning by Brian Huge and I (also available on arXiv and SSRN) leverages pathwise gradients computed with AD to train a novel breed of deep learning models to effectively approximate pricing and risk functions of arbitrary financial products.

I implemented AD in production at Danske Bank with my colleagues of the Quantitative Research department. I also teach AD at Copenhagen University in the context of my graduate course on Computational Finance and Machine Learning in Finance. My book gives an exhaustive and pedagogical account of AD, its implementation in C++ and its deployment for the risk management of financial Derivatives.

Parts I and II are necessary pre-requisites to make the most of the critical part III.

Article with code, Wilmott Mar20

The third and final part of my paper  “Computation graphs for AAD and Machine Learning part III: Application to Derivatives Sensitivities” was just published in Wilmott, the third and final in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

The first parts were published in Wilmott November 2019 issue and  Wilmott January 2020 issue.

A. Savine, “Computation graphs for aad and machine learning part iii: application to derivatives sensitivities” Wilmott, vol. 2020, iss. 106, p. 24-39, 2020.

Article with code, Wilmott Jan20

The second part of my paper  “Computation graphs for AAD and Machine Learning part II: Adjoint differentiation and AAD” was just published in Wilmott, the second in a series of three articles with code dedicated to AAD and Computational Finance in general. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

The first part was published in Wilmott November 2019 issue.

A. Savine, “Computation graphs for aad and machine learning part ii: adjoint differentiation and AAD” Wilmott, vol. 2020, iss. 105, p. 32-45, 2020.

Article with code, Wilmott Nov19

My paper  “Computation graphs for AAD and Machine Learning part I: Introduction to computation graphs and automatic differentiation” was just published in Wilmott, the first in a series of articles with code dedicated to Computational Finance. It covers computation graphs, backpropagation, AAD and implementation in finance, taking inspiration in the recent achievements of the Superfly Analytics group of Danske Bank.

A. Savine, “Computation graphs for aad and machine learning part i: introduction to computation graphs and automatic differentiation,” Wilmott, vol. 2019, iss. 104, p. 36–61, 2019.