“The Global Financial Crisis resulted in profound changes in quants’ Modus Operandi. This timely three-volume set describes some of the tools necessary to deal with these changes. Individual volumes cover in detail several important topics of interest to anyone who wants to stay au courant with modern developments in financial engineering. While the books are predominantly practically oriented, they strike a fine balance between theoretical and applied considerations. The authors are prominent practitioners and indisputable thought-leaders in the field. I recommend this set enthusiastically to anyone who wishes to understand the current and emerging trends in financial engineering.”
– Prof. Alexander Lipton, Founder and CEO, Stronghold Labs; Fellow, Connection Science and Engineering, Massachusetts Institute of Technology
The scripting of derivatives transactions has been a central feature of finance software since the 1990s. Most derivatives valuation and risk systems, both in-house and externally provided, include some form of feature scripting technology. Despite this, a significant gap exists in the existing literature regarding the application of scripting to derivatives and risk.
In Modern Computational Finance: Scripting for Derivatives and xVA, a team of distinguished finance professionals addresses this gap and delivers an extraordinary exposition of scripting for derivatives valuation. With a complete, professional scripting library written in modern C++, this volume demonstrates that scripting technology has much wider applications than what is typically assumed. It offers the strategies, concepts, and information required to construct a comprehensive risk and valuation tool.
In this stand-alone volume, the authors show how scripting offers a unique representation of financial transactions that enable finance practitioners to interrogate, aggregate, and manipulate cash-flows in several ways. This facilitates portfolio-wide risk assessment and regulatory calculations.
The book provides effective strategies for improving scripting libraries, from basic examples, like support for dates and vectors, to advanced concepts, like American Monte Carlo techniques. It also explores the concepts of fuzzy logic and risk sensitivities with support for smoothing and condition domains, as well as a complete and fulsome discussion of the application of scripting to xVA.
Ideal for quantitative analysts, risk professionals, system developers, derivatives traders, and financial analysts, Modern Computational Finance: Scripting for Derivatives and xVA, is also required reading for students in any of these fields seeking a definitive resource on derivative scripting.
From the Back Cover
PRAISE FOR MODERN COMPUTATIONAL FINANCE
“This book is an indispensable resource for any quant. Written by experts in the field and filled with practical examples and industry insights that are hard to find elsewhere, the book sets a new standard for computational finance.”
―Paul Glasserman, Jack R. Anderson Professor of Business, Columbia University
“The global financial crisis resulted in profound changes to quants’ Modus Operandi. Modern Computational Finance describes some of the tools necessary to deal with these changes. This book covers in detail several important topics of interest to anyone who wants to stay au courant with modern developments in financial engineering. While the book is predominantly practically oriented, it strikes a fine balance between theoretical and applied considerations. The authors are prominent practitioners and undisputed thought-leaders in the field. I recommend this book enthusiastically to anyone who wishes to understand the current and emerging trends in financial engineering.”
―Professor Alexander Lipton, Fellow, Connection Science and Engineering, Massachusetts Institute of Technology; Founder and CIO, Sila
“This is a new era that expects a new, expanded skill set from a new generation of quants. This is a new type of publication that combines words, mathematics, and code to offer a full picture for the generic, effective, practical development of modern financial libraries. The authors provide the unique perspective of long-time leading derivatives practitioners. Brilliant.”
―Rolf Poulsen, Professor of Mathematical Finance, University of Copenhagen
About the Author
ANTOINE SAVINE is a mathematician and derivatives practitioner with 25 years of leadership experience with global investment banks. He wrote the book on automatic adjoint differentiation (AAD) and co-developed Differential Machine Learning. He was also influential in volatility modeling and many areas of numerical and computational finance. Antoine works with Superfly Analytics at Danske Bank, winner of the 2019 Excellence in Risk Management and Modelling RiskMinds award. He holds a PhD in Mathematical Finance from Copenhagen University, where he teaches quantitative and computational finance.
Jesper Andreasen heads the Quantitative Research department at Saxo Bank. Over a 25 year long career he has held senior roles in quant departments of Bank of America, Nordea and General Re Financial Products, and he founded and headed the Superfly Analytics department at Danske Bank. Jesper co-received Risk magazine’s 2001 and 2012 Quant of the year awards and their In-House Risk System of the year award in 2015. He is an honorary professor of Mathematical Finance at Copenhagen University and completed his PhD in the same subject at Aarhus University in 1997.
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