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1st Edition PDF ebook by author Donald R. Chambers; Qin Lu – published by Chapman & Hall in 2021 and save up to 80% compared to the print version of this textbook. With PDF version of this textbook, not only save you money, you can also highlight, add text, underline add post-it notes, bookmarks to pages, instantly search for the major terms or chapter titles, etc.
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eBook Details:
Full title: Introduction to Financial Mathematics: With Computer Applications
1st Edition
Edition: 1st
Copyright year: 2021
Publisher: Chapman & Hall
Author: Donald R. Chambers; Qin Lu
ISBN: 9780367752781, 9781000370140
Format: PDF
Description of Introduction to Financial Mathematics: With Computer Applications
1st Edition:
This book’s primary objective is to educate aspiring finance professionals about mathematics and computation in the context of financial derivatives. The authors offer a balance of traditional coverage and technology to fill the void between highly mathematical books and broad finance books. The focus of this book is twofold: To partner mathematics with corresponding intuition rather than diving so deeply into the mathematics that the material is inaccessible to many readers. To build reader intuition, understanding and confidence through three types of computer applications that help the reader understand the mathematics of the models. Unlike many books on financial derivatives requiring stochastic calculus, this book presents the fundamental theories based on only undergraduate probability knowledge. A key feature of this book is its focus on applying models in three programming languages -R, Mathematica and EXCEL. Each of the three approaches offers unique advantages. The computer applications are carefully introduced and require little prior programming background. The financial derivative models that are included in this book are virtually identical to those covered in the top financial professional certificate programs in finance. The overlap of financial models between these programs and this book is broad and deep.
