Introduction To C For Financial Engineers

This book introduces the reader to the C++ programming language and how to use it to write applications in quantitative finance (QF) and related areas. No previous knowledge of C or C++ is required -- experience with VBA, Matlab or other programming language is sufficient. The book adopts an incremental approach; starting from basic principles then moving on to advanced complex techniques and then to real-life applications in financial engineering. There are five major parts in the book: C++ fundamentals and object-oriented thinking in QF Advanced object-oriented features such as inheritance and polymorphism Template programming and the Standard Template Library (STL) An introduction to GOF design patterns and their applications in QF Applications The kinds of applications include binomial and trinomial methods, Monte Carlo simulation, advanced trees, partial differential equations and finite difference methods. This book includes a companion website with all source code and many useful C++ classes that you can use in your own applications. Examples, test cases and applications are directly relevant to QF. This book is the perfect companion to Daniel J. Duffy’s book Financial Instrument Pricing using C++ (Wiley 2004, 0470855096 / 9780470021620)

Apply modern C++ to applications in computational finance Introduction to C++ for Financial Engineers, Second Edition uses the new and improved language features and multi-paradigm programming styles to create robust and flexible code for a number of important areas in finance. Each chapter has been written to be as self-contained as possible, while taking account of the most recent developments in software design, programming styles and advances in desktop hardware. This resource is written for Quant developers versed in creating applications using C++98. It shows how to define, design and implement flexible applications using modern software design methods in C++. Developers will learn how to: Adopt a standardised design methodology (based on domain architectures) for applications Write clear and maintainable code in the ‘gold standard' C++ language Move from C++98 to next-generation C++11, C++ 14 and later Use C++ and Boost libraries instead of home-grown code Create multi-threaded and parallel applications Utilise applications to lattices, PDE and Monte Carlo models The chapters in this book begin with simple examples, transitioning to more extensive models and finance-related applications. Each chapter concludes with exercises and projects, allowing the reader to monitor progress by reviewing what has been discussed and writing code based on those concepts. New C++ syntax, language features and libraries Building flexible lattice models using the domain architecture approach Detailed discussion of PDE/Finite Difference Method for European and American option pricing C++ Concurrency, multithreading and parallel libraries for multi-core CPUs and GPUs Numerical solution of stochastic differential equations and Monte Carlo option pricing Optimal use of the combined object-oriented, template and functional programming styles Introduction to C++ for Financial Engineers, Second Edition assembles many of the design and language features to help you create flexible and maintainable applications.

One of the best languages for the development of financial engineering and instrument pricing applications is C++. This book has several features that allow developers to write robust, flexible and extensible software systems. The book is an ANSI/ISO standard, fully object-oriented and interfaces with many third-party applications. It has support for templates and generic programming, massive reusability using templates (?write once?) and support for legacy C applications. In this book, author Daniel J. Duffy brings C++ to the next level by applying it to the design and implementation of classes, libraries and applications for option and derivative pricing models. He employs modern software engineering techniques to produce industrial-strength applications: Using the Standard Template Library (STL) in finance Creating your own template classes and functions Reusable data structures for vectors, matrices and tensors Classes for numerical analysis (numerical linear algebra ?) Solving the Black Scholes equations, exact and approximate solutions Implementing the Finite Difference Method in C++ Integration with the ?Gang of Four? Design Patterns Interfacing with Excel (output and Add-Ins) Financial engineering and XML Cash flow and yield curves Included with the book is a CD containing the source code in the Datasim Financial Toolkit. You can use this to get up to speed with your C++ applications by reusing existing classes and libraries. 'Unique... Let's all give a warm welcome to modern pricing tools.' -- Paul Wilmott, mathematician, author and fund manager

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.

This textbook contains the fundamentals for an undergraduate course in mathematical finance aimed primarily at students of mathematics. Assuming only a basic knowledge of probability and calculus, the material is presented in a mathematically rigorous and complete way. The book covers the time value of money, including the time structure of interest rates, bonds and stock valuation; derivative securities (futures, options), modelling in discrete time, pricing and hedging, and many other core topics. With numerous examples, problems and exercises, this book is ideally suited for independent study.

Presents a fresh introduction to financial engineering. This book offers links between intuition and underlying mathematics and a mixture of market insights and mathematical materials. It also includes end-of-chapter exercises and case studies. Bestselling author Salih Neftci presents a fresh, original, informative, and up-to-date introduction to financial engineering. The book offers clear links between intuition and underlying mathematics and an outstanding mixture of market insights and mathematical materials. Also included are end-of-chapter exercises and case studies. In a market characterized by the existence of large pools of liquid funds willing to go anywhere, anytime in search of a few points of advantage, there are new risks. Lacking experience with these new risks, firms, governmental entities, and other investors have been surprised by unexpected and often disastrous financial losses. Managers and analysts seeking to employ these new instruments and strategies to make pricing, hedging, trading, and portfolio management decisions require a mature understanding of theoretical finance and sophisticated mathematical and computer modeling skills. Important and useful because it analyzes financial assets and derivatives from the financial engineering perspective, this book offers a different approach than the existing finance literature in financial asset and derivative analysis. Seeking not to introduce financial instruments but instead to describe the methods of synthetically creating assets in static and in dynamic environments and to show how to use them, his book complements all currently available textbooks. It emphasizes developing methods that can be used in order to solve risk management, taxation, regulation, and above all, pricing problems. This perspective forms the basis of practical risk management. It will be useful for anyone learning about practical elements of financial engineering. Exercises and case studies at end of each chapter and on-line Solutions Manual are provided. It explains issues involved in day-to-day life of traders, using language other than mathematics. It offers careful and concise analysis of the LIBOR market model and of volatility engineering problems.

“Practical Applications of Evolutionary Computation to Financial Engineering” presents the state of the art techniques in Financial Engineering using recent results in Machine Learning and Evolutionary Computation. This book bridges the gap between academics in computer science and traders and explains the basic ideas of the proposed systems and the financial problems in ways that can be understood by readers without previous knowledge on either of the fields. To cement the ideas discussed in the book, software packages are offered that implement the systems described within. The book is structured so that each chapter can be read independently from the others. Chapters 1 and 2 describe evolutionary computation. The third chapter is an introduction to financial engineering problems for readers who are unfamiliar with this area. The following chapters each deal, in turn, with a different problem in the financial engineering field describing each problem in detail and focusing on solutions based on evolutionary computation. Finally, the two appendixes describe software packages that implement the solutions discussed in this book, including installation manuals and parameter explanations.

The new edition of this influential textbook, geared towards graduate or advanced undergraduate students, teaches the statistics necessary for financial engineering. In doing so, it illustrates concepts using financial markets and economic data, R Labs with real-data exercises, and graphical and analytic methods for modeling and diagnosing modeling errors. These methods are critical because financial engineers now have access to enormous quantities of data. To make use of this data, the powerful methods in this book for working with quantitative information, particularly about volatility and risks, are essential. Strengths of this fully-revised edition include major additions to the R code and the advanced topics covered. Individual chapters cover, among other topics, multivariate distributions, copulas, Bayesian computations, risk management, and cointegration. Suggested prerequisites are basic knowledge of statistics and probability, matrices and linear algebra, and calculus. There is an appendix on probability, statistics and linear algebra. Practicing financial engineers will also find this book of interest.