Modern Computational Finance
Modern Computational Finance: AAD and Parallel Simulations, published with Wiley, is out November 13, 2018 (ebook) and November 20 (hardcover) on Amazon, Apple Books and many other places including your favorite book store.
It covers the principles, professional implementation and interaction of three of the key technologies powering modern derivatives systems: parallel computing, Monte-Carlo simulations and automatic adjoint differentiation (AAD), a powerful algorithm that computes thousands of differentials with outstanding speed and accuracy. It is the combination of these technologies, among others, that earned Danske Bank the In-House System of the Year 2015 Risk award.
“It would not be much of an exaggeration to say that Antoine Savine’s book ranks as the 21st century peer to Merton’s ‘Continuous-Time Finance’:
It makes modern computational techniques such as multi-threaded parallel AAD as accessible to finance professionals as Merton’s introduction of stochastic calculus into finance. A first in a three book series authored by Danske Bank’s powerhouse quant team makes intricate concepts inherent to production-quality implementation of AAD easy to understand and follow through.
No other quant finance focused book has gone so deeply into parallel C++ and AAD with such clarity, level of detail and thoroughness. I can hardly wait for the remaining two volumes to see what else the wizards of AAD have up their sleeves.”
Vladimir V. Piterbarg,
Head of Quantitative Analytics and Development at NatWest Markets,
Co-author of the three-volume set “Interest Rate Modeling”
“A passion to instruct
A knack for clarity
An obsession with detail
A luminous writer
An instant classic.”
Head of Quantitative Research, Bloomberg L.P.
TOC and Leif Andersen’s preface
Sobol sequences explained
Section 5.4 provides a self-contained introduction to Sobol sampling, a sharp imoprovement over random sampling in the context of pricing by Monte-Carlo. Sobol sampling is considered a best practice in finance since the pioneering work of Jaeckel and Joe and Kuo in the early 2000s, yet it remains widely misunderstood.
Chapter 13 discusses the check-pointing technique, critical for the practical implermentation of AAD. This is not self contained and probably hard to read in isolation from the rest of part III. It is only shown here to give a taste of the general style of the book.
The book comes with complete, professional C++ code for generic, parallel Monte-Carlo simulations and AAD. The code is freely available on GitHub. It is advised to watch the repo and follow the author to be notified of updates and improvement.
Please leave your own reviews, along questions, comments and suggestions at the bottom of this page, or on the book’s page on GoodReads.
Exercises and assignments
Exercises and assignments are being produced and will be posted separately. In the meantime, interested readers will find below the final hand-in for the computational finance lecture of autumn 2018 at Copenhagen University, where the book is used as curriculum: