Anonymous shelf assessment
Modern Computational Finance
Shelf score 8.0 / 10
On Modern Computational Finance · Antoine Savine · Leif B. G. Andersen · John Wiley & Sons
Published 18 April 2026
A comprehensive guide on algorithmic adjoint differentiation and parallel simulations in finance.
Overview
This institutional text provides an in-depth exploration of algorithmic adjoint differentiation (AAD) and parallel Monte Carlo simulations, tailored for practitioners in the finance sector. It is authored by industry professionals, ensuring a focus on practical applications and real-world implementation in large banking systems.
The book progresses from foundational concepts of adjoint differentiation to advanced application patterns in Monte Carlo simulations, addressing both pathwise and likelihood-ratio methodologies. It also delves into software architecture, emphasising the importance of maintaining clean separation of concerns to facilitate risk extraction without complicating pricing code.
With a focus on credit and rates simulation workloads, the text is designed for teams looking to enhance their pricing and risk management libraries. It equips readers with the necessary skills to evaluate and improve in-house pricing stacks, making it a valuable resource for those involved in quantitative finance and risk management.
By area & interest
Target Audience
This book is best suited for front-office and XVA quants, quantitative developers, and model-validation teams. It serves as a resource for those involved in implementing or refactoring risk engines and for senior MSc/PhD entrants in rates or credit analytics.
Practical Focus
The authors' practitioner background ensures that the content is grounded in deliverable systems rather than abstract theories. This practical approach is vital for readers looking to apply concepts directly to their work in finance.
Architectural Competency
Readers can expect to gain a solid understanding of architectural principles related to pricing stacks, enabling them to critique differentiability and make informed decisions regarding vendor versus build strategies for risk engines.
Basis of this assessment
This assessment is based on the catalogue description and additional contextual information provided.
Strengths
The book's strong practitioner authorship provides a realistic perspective on system implementation, making it relevant for modern banking contexts. Its explicit discussion of software structure aids in advocating for necessary refactors in risk libraries.
Limitations
The text is dense and implementation-heavy, which may pose challenges for readers without accompanying code or those new to the field. Additionally, some deployment details may become outdated due to rapid hardware evolution.
Ideal reader
Ideal readers include experienced quants and quantitative developers who are familiar with C++ and Monte Carlo pricing, as well as those involved in risk management and library development in financial institutions.