Books

Announcement

The second volume of Modern Computational Finance is going to press with Wiley and will be available in November 2021. Coauthored with Jesper Andreasen, the book focuses on Derivatives scripting.

available for pre-order, shipping November 2021

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.

Reviews

“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.”

Bruno Dupire,
Head of Quantitative Research, Bloomberg L.P.

Preview

TOC and Leif Andersen’s preface

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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.

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Check-pointing differentials

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.

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Repository

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.

The related lecture material has its own GitHub repo with a more gentle introduction to AAD in Machine Learning and Finance. See Lectures by Antoine Savine.

Forum

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:

https://antoinesavine.com/wp-content/uploads/2019/01/compfinhandin.pdf

23 thoughts on “Books”

  1. Hi Antoine,
    On page 20 of your book, there is a small bug in the code at the bottom of the page. I’m not aware of any errata page on Wiley’s site so I’ll just report it in case you are not aware. Instead of

    size_t r = b.rows();

    it should be

    size_t r = b.cols();

    Also, some users of VS 2019 might encounter a build error with MatrixProduct.sln when OpenMP is turned on

    1>c1xx : error C2338: two-phase name lookup is not supported for C++/CLI, C++/CX, or OpenMP; use /Zc:twoPhase-
    1>c1xx : fatal error C1903: unable to recover from previous error(s); stopping compilation

    To fix this, one needs to add /Zc:twoPhase- to the C/C++, Command Line, Additional Options

    Best,

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    1. Thank you, indeed, it appears that the code snippet in the bottom of page 20 is flawed, thank you so much for pointing it out. This snippet is not part of the ‘official’ code in GitHub, and so the bug spent a year unnoticed.

      Commenting this page is a good way to post errata, or you can contact me directly on aadcentral@asavine.com.

      OpenMP conflicting with two-phase lookup seems to be a known flaw in VS2019. Thank you for giving the workaround.

      Kind regards,
      Antoine

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  2. You’re most welcome ! Perhaps the use of non square matrices if possible, in future example solutions will make such indexing bugs easier to spot.

    Best,

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  3. Hi,
    On page 34, in the code snippet at the top, do you mean to say

    vector inner;
    // …
    return inner;

    rather than

    vector inner;
    // …
    return result;

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      1. Much thanks! I am starting reading that book. Looking forward to the hard copy. Will definitely buy one when it comes out~

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  5. The first book is fantastic, and I’m super happy to hear that the second on scripting and XVA is coming out soon.

    I know that you’ve standardized on C++, and that this is the standard in the industry, but have you taken a look at Julia at all? I ask for two reasons:

    1) Julia has a significant amount of support for AD (ForwardDiff, ReverseDiff, Zygote, etc.), including forward-mode AD that seems to be somewhat useful in certain Jacobian scenarios.

    2) Julia has significant metaprogramming capabilities and is, like LISP, homoiconic, which would seem to be a natural benefit when it comes to scripting.

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    1. Hello, thank you for your kind words.

      The second book is on hold for a while because I have been working on differential machine learning, a really nice and novel manner to learn ‘analytic’ approximations on the fly with vast application to resolve the hottest problems in derivatives finance right now. I think you will like it, please have a look.

      Working paper: arxiv.org/abs/2005.02347

      Slides: http://www.deep-analytics.org

      GitHub: github.com/differential-machine-learning

      I don’t know anything about Julia.

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  6. I would not be surpised if your book becomes one of the classic reference texts for derivative pricing, to be widely read for a great many years into the future by both entry-level quants and experienced practitioners alike. Having the C++ code to work with out-of-the-box is tremendous also. Whilst I have no problem calling the code from 32bit Exel, I have having not been successful calling the functions from 64bit Excel. The code compiles under x64 and the XLL appears to register in Excel, however none of the functions are available/visible. Does the C++ code responsible for registering the functions, or for handling the Excel memory management, require modification for 64bit Excel?

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  7. Thank you for your kind words. Effectively the code in the export layer does not easily transfer to xl64, and I have not been able to find time to resolve this yet. I will have a look when I have a minute, in the meantime, if you do find a solution, may I ask you to kindly post it on the github repo?
    Kind regards,
    Antoine Savine

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  8. At page 265, code line 67 ( or equivalently, code line 267 in the repository https://github.com/asavine/CompFinance/blob/master/store.h ), there is a

    return it->second.first.get();

    The function this snippet belongs to, returns a “const Product*” pointer.
    Variable “it” is defined two lines above in the code as ” auto it = productStore.find(store); “, that is, an iterator of productStore, which in turn is an alias for “unordered_map<string, unique_ptr<Product>>”.

    In order to return a pointer to “Product”, the way I see it, it should be enough to type

    return it->second.get();

    where “it->second” is enough to dereference the “unique_ptr<Product>” from iterator “it”. The raw pointer ” Product* ” we want is then accessed from the unique_ptr wrapper by “get()”.

    Hence, is the added “first” at line 67 a typo, or am I missing something within my reasoning above?

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      4. Given the quality of the book, I am sure that in due time Wiley will issue a second edition, where this and the very few other typos present will get rectified.
        Keep up the good work.

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