Algorithmic Differentiation in Finance Explained

Algorithmic Differentiation in Finance Explained
Author: Marc Henrard
Publisher: Springer
Total Pages: 112
Release: 2017-09-04
Genre: Business & Economics
ISBN: 3319539795

Download Algorithmic Differentiation in Finance Explained Book in PDF, Epub and Kindle

This book provides the first practical guide to the function and implementation of algorithmic differentiation in finance. Written in a highly accessible way, Algorithmic Differentiation Explained will take readers through all the major applications of AD in the derivatives setting with a focus on implementation. Algorithmic Differentiation (AD) has been popular in engineering and computer science, in areas such as fluid dynamics and data assimilation for many years. Over the last decade, it has been increasingly (and successfully) applied to financial risk management, where it provides an efficient way to obtain financial instrument price derivatives with respect to the data inputs. Calculating derivatives exposure across a portfolio is no simple task. It requires many complex calculations and a large amount of computer power, which in prohibitively expensive and can be time consuming. Algorithmic differentiation techniques can be very successfully in computing Greeks and sensitivities of a portfolio with machine precision. Written by a leading practitioner who works and programmes AD, it offers a practical analysis of all the major applications of AD in the derivatives setting and guides the reader towards implementation. Open source code of the examples is provided with the book, with which readers can experiment and perform their own test scenarios without writing the related code themselves.

Modern Computational Finance

Modern Computational Finance
Author: Antoine Savine
Publisher: John Wiley & Sons
Total Pages: 592
Release: 2018-11-20
Genre: Mathematics
ISBN: 1119539455

Download Modern Computational Finance Book in PDF, Epub and Kindle

Arguably the strongest addition to numerical finance of the past decade, Algorithmic Adjoint Differentiation (AAD) is the technology implemented in modern financial software to produce thousands of accurate risk sensitivities, within seconds, on light hardware. AAD recently became a centerpiece of modern financial systems and a key skill for all quantitative analysts, developers, risk professionals or anyone involved with derivatives. It is increasingly taught in Masters and PhD programs in finance. Danske Bank's wide scale implementation of AAD in its production and regulatory systems won the In-House System of the Year 2015 Risk award. The Modern Computational Finance books, written by three of the very people who designed Danske Bank's systems, offer a unique insight into the modern implementation of financial models. The volumes combine financial modelling, mathematics and programming to resolve real life financial problems and produce effective derivatives software. This volume is a complete, self-contained learning reference for AAD, and its application in finance. AAD is explained in deep detail throughout chapters that gently lead readers from the theoretical foundations to the most delicate areas of an efficient implementation, such as memory management, parallel implementation and acceleration with expression templates. The book comes with professional source code in C++, including an efficient, up to date implementation of AAD and a generic parallel simulation library. Modern C++, high performance parallel programming and interfacing C++ with Excel are also covered. The book builds the code step-by-step, while the code illustrates the concepts and notions developed in the book.

The Art of Differentiating Computer Programs

The Art of Differentiating Computer Programs
Author: Uwe Naumann
Publisher: SIAM
Total Pages: 358
Release: 2012-01-01
Genre: Mathematics
ISBN: 9781611972078

Download The Art of Differentiating Computer Programs Book in PDF, Epub and Kindle

This is the first entry-level book on algorithmic (also known as automatic) differentiation (AD), providing fundamental rules for the generation of first- and higher-order tangent-linear and adjoint code. The author covers the mathematical underpinnings as well as how to apply these observations to real-world numerical simulation programs. Readers will find: examples and exercises, including hints to solutions; the prototype AD tools dco and dcc for use with the examples and exercises; first- and higher-order tangent-linear and adjoint modes for a limited subset of C/C++, provided by the derivative code compiler dcc; a supplementary website containing sources of all software discussed in the book, additional exercises and comments on their solutions (growing over the coming years), links to other sites on AD, and errata.

Evaluating Derivatives

Evaluating Derivatives
Author: Andreas Griewank
Publisher: SIAM
Total Pages: 438
Release: 2008-01-01
Genre: Mathematics
ISBN: 0898717760

Download Evaluating Derivatives Book in PDF, Epub and Kindle

This title is a comprehensive treatment of algorithmic, or automatic, differentiation. The second edition covers recent developments in applications and theory, including an elegant NP completeness argument and an introduction to scarcity.

Algorithmic Differentiation in Finance

Algorithmic Differentiation in Finance
Author: Marc P. A. Henrard
Publisher:
Total Pages: 20
Release: 2013
Genre:
ISBN:

Download Algorithmic Differentiation in Finance Book in PDF, Epub and Kindle

Algorithmic Differentiation (AD) is an efficient way to compute derivatives of a value with respect to the data inputs. In finance the model calibration to market data can be an important part of the valuation process. In presence of calibration, when obtained through exact equation solving or optimisation, very efficient implementation can be done using the implicit function theorem with the standard AD approach. Previous results discussed the exact case are here extended to the case of calibration obtained by a least-square approach.

Principles of Data Assimilation

Principles of Data Assimilation
Author: Seon Ki Park
Publisher: Cambridge University Press
Total Pages: 413
Release: 2022-09-29
Genre: Science
ISBN: 1108923895

Download Principles of Data Assimilation Book in PDF, Epub and Kindle

Data assimilation is theoretically founded on probability, statistics, control theory, information theory, linear algebra, and functional analysis. At the same time, data assimilation is a very practical subject, given its goal of estimating the posterior probability density function in realistic high-dimensional applications. This puts data assimilation at the intersection between the contrasting requirements of theory and practice. Based on over twenty years of teaching courses in data assimilation, Principles of Data Assimilation introduces a unique perspective that is firmly based on mathematical theories, but also acknowledges practical limitations of the theory. With the inclusion of numerous examples and practical case studies throughout, this new perspective will help students and researchers to competently interpret data assimilation results and to identify critical challenges of developing data assimilation algorithms. The benefit of information theory also introduces new pathways for further development, understanding, and improvement of data assimilation methods.

Quantitative Finance For Dummies

Quantitative Finance For Dummies
Author: Steve Bell
Publisher: John Wiley & Sons
Total Pages: 408
Release: 2016-06-07
Genre: Business & Economics
ISBN: 1118769430

Download Quantitative Finance For Dummies Book in PDF, Epub and Kindle

An accessible, thorough introduction to quantitative finance Does the complex world of quantitative finance make you quiver?You're not alone! It's a tough subject for even high-levelfinancial gurus to grasp, but Quantitative Finance ForDummies offers plain-English guidance on making sense ofapplying mathematics to investing decisions. With this completeguide, you'll gain a solid understanding of futures, options andrisk, and get up-to-speed on the most popular equations, methods,formulas and models (such as the Black-Scholes model) that areapplied in quantitative finance. Also known as mathematical finance, quantitative finance is thefield of mathematics applied to financial markets. It's a highlytechnical discipline—but almost all investment companies andhedge funds use quantitative methods. This fun and friendly guidebreaks the subject of quantitative finance down to easilydigestible parts, making it approachable for personal investors andfinance students alike. With the help of Quantitative FinanceFor Dummies, you'll learn the mathematical skills necessary forsuccess with quantitative finance, the most up-to-date portfolioand risk management applications and everything you need to knowabout basic derivatives pricing. Covers the core models, formulas and methods used inquantitative finance Includes examples and brief exercises to help augment yourunderstanding of QF Provides an easy-to-follow introduction to the complex world ofquantitative finance Explains how QF methods are used to define the current marketvalue of a derivative security Whether you're an aspiring quant or a top-tier personalinvestor, Quantitative Finance For Dummies is your go-toguide for coming to grips with QF/risk management.

Modern Computational Finance

Modern Computational Finance
Author: Antoine Savine
Publisher: John Wiley & Sons
Total Pages: 295
Release: 2021-11-02
Genre: Mathematics
ISBN: 111954078X

Download Modern Computational Finance Book in PDF, Epub and Kindle

An incisive and essential guide to building a complete system for derivative scripting In Volume 2 of Modern Computational Finance Scripting for Derivatives and xVA, quantitative finance experts and practitioners Drs. Antoine Savine and Jesper Andreasen deliver an indispensable and insightful roadmap to the interrogation, aggregation, and manipulation of cash-flows in a variety of ways. The book demonstrates how to facilitate portfolio-wide risk assessment and regulatory calculations (like xVA). Complete with a professional scripting library written in modern C++, this stand-alone volume walks readers through the construction of a comprehensive risk and valuation tool. This essential book also offers: Effective strategies for improving scripting libraries, from basic examples—like support for dates and vectors—to advanced improvements, including American Monte Carlo techniques Exploration of the concepts of fuzzy logic and risk sensitivities, including support for smoothing and condition domains Discussion of the application of scripting to xVA, complete with a full treatment of branching Perfect for quantitative analysts, risk professionals, system developers, derivatives traders, and financial analysts, Modern Computational Finance Scripting for Derivatives and xVA: Volume 2 is also a must-read resource for students and teachers in master’s and PhD finance programs.

Numerical Algorithms

Numerical Algorithms
Author: Justin Solomon
Publisher: CRC Press
Total Pages: 400
Release: 2015-06-24
Genre: Computers
ISBN: 1482251892

Download Numerical Algorithms Book in PDF, Epub and Kindle

Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics presents a new approach to numerical analysis for modern computer scientists. Using examples from a broad base of computational tasks, including data processing, computational photography, and animation, the textbook introduces numerical modeling and algorithmic desig