Derivative Algorithms; Volume 1: Bones,2nd Edition
by 作者: Thomas Hyer
Authors: Tom Hyer
ISBN-10 书号: 9814699519
ISBN-13 书号: 9789814699518
Pages: 332
Publisher Finelybook 出版日期: 2016
Book Description
Derivatives Algorithms — Volume 1: Bones (Second Edition) is for practicing quants who already have some expertise in risk-neutral pricing and in programming,and want to build a reusable and extensible library. Rather than specific models,this volume provides foundations common to all pricing,such as C++ code structure,interfaces,and several widely used mathematical methods. It also presents a set of protocols,by which models and trades can collaborate to support pricing and hedging tasks,and illustrates their use with several example trade types and models. Readers will learn to deploy the results of their research work with productivity-enhancing methods that are not taught elsewhere,including object serialization,code generation,and separation of concerns for continuous improvement. Of all the books on derivatives pricing,only Derivatives Algorithms shows the internals of a high-quality working library.
The new Second Edition is more accessible to readers who are not already familiar with the book’s concepts; there is an increased focus on explaining the motivation for each step,and on providing a high-level perspective on design choices. The chapters on Persistence and Protocols have been substantially rewritten,providing motivating examples and additional detail in the code. The treatment of yield curves and funding has been modernized,with the increased sophistication required by today’s markets. And a new final chapter,describing the next phase in the evolution of derivatives valuation and risk,has been added.
Contents
1.Introduction
2.Principles
3.Types and Interfaces
4.Vector and Matrix Computations
5.Persistence and Memory
6.Testing Framework
7.Further Maths
8.Schedules
9.Indices
10.Pricing Protocols
11.Standardized Trades
12.Curves
13.Models
14.Semianalytic Pricers
15.Risk
16.Appendi. The Age of Stochastic Calculus
Acknowledgements and Further Reading
Index