An Introduction to Quantitative Finance

An Introduction to Quantitative Finance

Author: Stephen Blyth

Publisher: Oxford University Press, USA

Published: 2014

Total Pages: 193

ISBN-13: 0199666598

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The quantitative nature of complex financial transactions makes them a fascinating subject area for mathematicians of all types. This book gives an insight into financial engineering while building on introductory probability courses by detailing one of the most fascinating applications of the subject.


Introduction to Quantitative Finance

Introduction to Quantitative Finance

Author: Robert R. Reitano

Publisher: MIT Press

Published: 2010-01-29

Total Pages: 747

ISBN-13: 026201369X

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An introduction to many mathematical topics applicable to quantitative finance that teaches how to “think in mathematics” rather than simply do mathematics by rote. This text offers an accessible yet rigorous development of many of the fields of mathematics necessary for success in investment and quantitative finance, covering topics applicable to portfolio theory, investment banking, option pricing, investment, and insurance risk management. The approach emphasizes the mathematical framework provided by each mathematical discipline, and the application of each framework to the solution of finance problems. It emphasizes the thought process and mathematical approach taken to develop each result instead of the memorization of formulas to be applied (or misapplied) automatically. The objective is to provide a deep level of understanding of the relevant mathematical theory and tools that can then be effectively used in practice, to teach students how to “think in mathematics” rather than simply to do mathematics by rote. Each chapter covers an area of mathematics such as mathematical logic, Euclidean and other spaces, set theory and topology, sequences and series, probability theory, and calculus, in each case presenting only material that is most important and relevant for quantitative finance. Each chapter includes finance applications that demonstrate the relevance of the material presented. Problem sets are offered on both the mathematical theory and the finance applications sections of each chapter. The logical organization of the book and the judicious selection of topics make the text customizable for a number of courses. The development is self-contained and carefully explained to support disciplined independent study as well. A solutions manual for students provides solutions to the book's Practice Exercises; an instructor's manual offers solutions to the Assignment Exercises as well as other materials.


Quantitative Finance

Quantitative Finance

Author: Matt Davison

Publisher: CRC Press

Published: 2014-05-08

Total Pages: 523

ISBN-13: 1439871698

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Teach Your Students How to Become Successful Working Quants Quantitative Finance: A Simulation-Based Introduction Using Excel provides an introduction to financial mathematics for students in applied mathematics, financial engineering, actuarial science, and business administration. The text not only enables students to practice with the basic techniques of financial mathematics, but it also helps them gain significant intuition about what the techniques mean, how they work, and what happens when they stop working. After introducing risk, return, decision making under uncertainty, and traditional discounted cash flow project analysis, the book covers mortgages, bonds, and annuities using a blend of Excel simulation and difference equation or algebraic formalism. It then looks at how interest rate markets work and how to model bond prices before addressing mean variance portfolio optimization, the capital asset pricing model, options, and value at risk (VaR). The author next focuses on binomial model tools for pricing options and the analysis of discrete random walks. He also introduces stochastic calculus in a nonrigorous way and explains how to simulate geometric Brownian motion. The text proceeds to thoroughly discuss options pricing, mostly in continuous time. It concludes with chapters on stochastic models of the yield curve and incomplete markets using simple discrete models. Accessible to students with a relatively modest level of mathematical background, this book will guide your students in becoming successful quants. It uses both hand calculations and Excel spreadsheets to analyze plenty of examples from simple bond portfolios. The spreadsheets are available on the book’s CRC Press web page.


A First Course in Quantitative Finance

A First Course in Quantitative Finance

Author: Thomas Mazzoni

Publisher: Cambridge University Press

Published: 2018-03-29

Total Pages: 599

ISBN-13: 1108419577

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Using stereoscopic images and other novel pedagogical features, this book offers a comprehensive introduction to quantitative finance.


Introduction to R for Quantitative Finance

Introduction to R for Quantitative Finance

Author: Gergely Daróczi

Publisher: Packt Publishing Ltd

Published: 2013-11-22

Total Pages: 253

ISBN-13: 1783280948

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This book is a tutorial guide for new users that aims to help you understand the basics of and become accomplished with the use of R for quantitative finance.If you are looking to use R to solve problems in quantitative finance, then this book is for you. A basic knowledge of financial theory is assumed, but familiarity with R is not required. With a focus on using R to solve a wide range of issues, this book provides useful content for both the R beginner and more experience users.


Introduction to Quantitative Methods for Financial Markets

Introduction to Quantitative Methods for Financial Markets

Author: Hansjoerg Albrecher

Publisher: Springer Science & Business Media

Published: 2013-06-28

Total Pages: 190

ISBN-13: 3034805195

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Swaps, futures, options, structured instruments - a wide range of derivative products is traded in today's financial markets. Analyzing, pricing and managing such products often requires fairly sophisticated quantitative tools and methods. This book serves as an introduction to financial mathematics with special emphasis on aspects relevant in practice. In addition to numerous illustrative examples, algorithmic implementations are demonstrated using "Mathematica" and the software package "UnRisk" (available for both students and teachers). The content is organized in 15 chapters that can be treated as independent modules. In particular, the exposition is tailored for classroom use in a Bachelor or Master program course, as well as for practitioners who wish to further strengthen their quantitative background.


An Introduction to Computational Finance

An Introduction to Computational Finance

Author: ™mr U?ur

Publisher: Imperial College Press

Published: 2009

Total Pages: 315

ISBN-13: 1848161921

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Although there are several publications on similar subjects, this book mainly focuses on pricing of options and bridges the gap between Mathematical Finance and Numerical Methodologies. The author collects the key contributions of several monographs and selected literature, values and displays their importance, and composes them here to create a work which has its own characteristics in content and style.This invaluable book provides working Matlab codes not only to implement the algorithms presented in the text, but also to help readers code their own pricing algorithms in their preferred programming languages. Availability of the codes under an Internet site is also offered by the author.Not only does this book serve as a textbook in related undergraduate or graduate courses, but it can also be used by those who wish to implement or learn pricing algorithms by themselves. The basic methods of option pricing are presented in a self-contained and unified manner, and will hopefully help readers improve their mathematical and computational backgrounds for more advanced topics.Errata(s)Errata


An Introduction To Machine Learning In Quantitative Finance

An Introduction To Machine Learning In Quantitative Finance

Author: Hao Ni

Publisher: World Scientific

Published: 2021-04-07

Total Pages: 263

ISBN-13: 1786349388

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In today's world, we are increasingly exposed to the words 'machine learning' (ML), a term which sounds like a panacea designed to cure all problems ranging from image recognition to machine language translation. Over the past few years, ML has gradually permeated the financial sector, reshaping the landscape of quantitative finance as we know it.An Introduction to Machine Learning in Quantitative Finance aims to demystify ML by uncovering its underlying mathematics and showing how to apply ML methods to real-world financial data. In this book the authorsFeatured with the balance of mathematical theorems and practical code examples of ML, this book will help you acquire an in-depth understanding of ML algorithms as well as hands-on experience. After reading An Introduction to Machine Learning in Quantitative Finance, ML tools will not be a black box to you anymore, and you will feel confident in successfully applying what you have learnt to empirical financial data!


An Introduction to Mathematical Finance with Applications

An Introduction to Mathematical Finance with Applications

Author: Arlie O. Petters

Publisher: Springer

Published: 2016-06-17

Total Pages: 499

ISBN-13: 1493937839

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This textbook aims to fill the gap between those that offer a theoretical treatment without many applications and those that present and apply formulas without appropriately deriving them. The balance achieved will give readers a fundamental understanding of key financial ideas and tools that form the basis for building realistic models, including those that may become proprietary. Numerous carefully chosen examples and exercises reinforce the student’s conceptual understanding and facility with applications. The exercises are divided into conceptual, application-based, and theoretical problems, which probe the material deeper. The book is aimed toward advanced undergraduates and first-year graduate students who are new to finance or want a more rigorous treatment of the mathematical models used within. While no background in finance is assumed, prerequisite math courses include multivariable calculus, probability, and linear algebra. The authors introduce additional mathematical tools as needed. The entire textbook is appropriate for a single year-long course on introductory mathematical finance. The self-contained design of the text allows for instructor flexibility in topics courses and those focusing on financial derivatives. Moreover, the text is useful for mathematicians, physicists, and engineers who want to learn finance via an approach that builds their financial intuition and is explicit about model building, as well as business school students who want a treatment of finance that is deeper but not overly theoretical.


Paul Wilmott on Quantitative Finance

Paul Wilmott on Quantitative Finance

Author: Paul Wilmott

Publisher: Wiley

Published: 2000-06-20

Total Pages: 0

ISBN-13: 9780471874386

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The only comprehensive reference encompassing both traditional and new derivatives and financial engineering techniques Based on the author's hugely successful Derivatives: The Theory and Practice of Financial Engineering, Paul Wilmott on Quantitative Finance is the definitive guide to derivatives and related financial products. In addition to fully updated and expanded coverage of all the topics covered in the first book, this two-volume set also includes sixteen entirely new chapters covering such crucial areas as stochastic control and derivatives, utility theory, stochastic volatility and utility, mortgages, real options, power derivatives, weather derivatives, insurance derivatives, and more. Wilmott has also added clear, detailed explanations of all the mathematical procedures readers need to know in order to use the techniques he describes. Paul Wilmott, Dphil (Oxford, UK), is one of Europe's leading writers and consultants in the area of financial mathematics. He is also head of Wilmott Associates, a leading international financial consulting firm whose clients include Citibank, IBM, Bank of Montreal, Momura, Daiwa, Maxima, Dresdner Klienwort Benson, Origenes, and Siembra.