Introduction to R for Quantitative Finance
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 ass...
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Main Authors | , , , , , , , , |
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Format | eBook |
Language | English |
Published |
Birmingham
Packt Publishing, Limited
2013
Packt Publishing |
Edition | 1 |
Subjects | |
Online Access | Get full text |
ISBN | 9781783280933 178328093X |
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Abstract | 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. |
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AbstractList | Explore how to use the statistical computing language R to solve complex quantitative finance problems with "Introduction to R for Quantitative Finance." This book offers a blend of theory and practice, empowering readers with both the foundational understanding and practical skills to tackle real-world challenges using R, making it an ideal resource for beginners and seasoned professionals alike.What this Book will help me doUtilize time series analysis in R to model and forecast financial and economic data.Apply key portfolio selection theories to analyze and optimize investment portfolios.Understand and implement a variety of pricing models, including the Capital Asset Pricing Model in R.Analyze and interpret fixed income instruments and derivatives, focusing on practical applications in finance.Leverage R for risk analysis through techniques such as Extreme Value Theory and copula-based modeling.Author(s)The authors of "Introduction to R for Quantitative Finance" are seasoned experts in the fields of quantitative finance and computational statistics. They bring a wealth of industry and academic experience to the table, having applied R to solve intricate financial problems in practical settings. Their approachable writing style ensures complex subjects remain accessible and engaging.Who is it for?This book is ideal for quantitative analysts, data scientists, or finance professionals eager to leverage R for financial analysis. It caters to individuals with a foundation in finance but new to the R programming language. Readers who aim to model, predict, and interpret financial phenomena using advanced statistical tools will particularly benefit from this guide. 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. In DetailQuantitative finance is an increasingly important area for businesses, and skilled professionals are highly sought after. The statistical computing language R is becoming established in universities and in industry as the lingua franca of data analysis and statistical computing.Introduction to R for Quantitative Finance will show you how to solve real-world quantitative finance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to financial networks. Each chapter briefly presents the theory behind specific concepts and deals with solving a diverse range of problems using R with the help of practical examples.This book will be your guide on how to use and master R in order to solve real-world quantitative finance problems. This book covers the essentials of quantitative finance, taking you through a number of clear and practical examples in R that will not only help you to understand the theory, but how to effectively deal with your own real-life problems.Starting with time series analysis, you will also learn how to optimize portfolios and how asset pricing models work. The book then covers fixed income securities and derivatives like credit risk management. The last chapters of this book will also provide you with an overview of exciting topics like extreme values and network analysis in quantitative finance.ApproachThis 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.Who this book is forIf 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. |
Author | Csoka, Peter Varadi, Kata Vidovics-Dancs, Agnes Berlinger, Edina Daroczi, Gergely Michaletzky, Márton Tulassay, Zsolt Puhle, Michael Havran, Daniel |
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Snippet | 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... Explore how to use the statistical computing language R to solve complex quantitative finance problems with "Introduction to R for Quantitative Finance." This... In DetailQuantitative finance is an increasingly important area for businesses, and skilled professionals are highly sought after. The statistical computing... |
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SubjectTerms | Economics Finance Mathematical models R (Computer program language) Statistical methods |
TableOfContents | 5. Estimating the Term Structure of Interest Rates -- The term structure of interest rates and related functions -- The estimation problem -- Estimation of the term structure by linear regression -- Cubic spline regression -- Applied R functions -- Summary -- 6. Derivatives Pricing -- The Black-Scholes model -- The Cox-Ross-Rubinstein model -- Connection between the two models -- Greeks -- Implied volatility -- Summary -- 7. Credit Risk Management -- Credit default models -- Structural models -- Intensity models -- Correlated defaults - the portfolio approach -- Migration matrices -- Getting started with credit scoring in R -- Summary -- 8. Extreme Value Theory -- Theoretical overview -- Application - modeling insurance claims -- Exploratory data analysis -- Tail behavior of claims -- Determining the threshold -- Fitting a GPD distribution to the tails -- Quantile estimation using the fitted GPD model -- Calculation of expected loss using the fitted GPD model -- Summary -- 9. Financial Networks -- Representation, simulation, and visualization of financial networks -- Analysis of networks' structure and detection of topology changes -- Contribution to systemic risk - identification of SIFIs -- Summary -- A. References -- Time series analysis -- Portfolio optimization -- Asset pricing -- Fixed income securities -- Estimating the term structure of interest rates -- Derivatives Pricing -- Credit risk management -- Extreme value theory -- Financial networks -- Index Intro -- Introduction to R for Quantitative Finance -- Table of Contents -- Introduction to R for Quantitative Finance -- Credits -- About the Authors -- About the Reviewers -- www.PacktPub.com -- Support files, eBooks, discount offers and more -- Why Subscribe? -- Free Access for Packt account holders -- Preface -- What this book covers -- What you need for this book -- Who this book is for -- Conventions -- Reader feedback -- Customer support -- Downloading the example code -- Errata -- Piracy -- Questions -- 1. Time Series Analysis -- Working with time series data -- Linear time series modeling and forecasting -- Modeling and forecasting UK house prices -- Model identification and estimation -- Model diagnostic checking -- Forecasting -- Cointegration -- Cross hedging jet fuel -- Modeling volatility -- Volatility forecasting for risk management -- Testing for ARCH effects -- GARCH model specification -- GARCH model estimation -- Backtesting the risk model -- Forecasting -- Summary -- 2. Portfolio Optimization -- Mean-Variance model -- Solution concepts -- Theorem (Lagrange) -- Working with real data -- Tangency portfolio and Capital Market Line -- Noise in the covariance matrix -- When variance is not enough -- Summary -- 3. Asset Pricing Models -- Capital Asset Pricing Model -- Arbitrage Pricing Theory -- Beta estimation -- Data selection -- Simple beta estimation -- Beta estimation from linear regression -- Model testing -- Data collection -- Modeling the SCL -- Testing the explanatory power of the individual variance -- Summary -- 4. Fixed Income Securities -- Measuring market risk of fixed income securities -- Example - implementation in R -- Immunization of fixed income portfolios -- Net worth immunization -- Target date immunization -- Dedication -- Pricing a convertible bond -- Summary |
Title | Introduction to R for Quantitative Finance |
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