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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Gergely Daroczi is a Ph.D. candidate in Sociology with around eight years' experience in data management and analysis tasks within the R programming environment. Besides teaching Statistics at different Hungarian universities and doing data analysis jobs for several years, Gergely has founded and coordinated a UK-based online reporting startup company recently. This latter software or platform as a service which is called rapporter.net will potentially provide an intuitive frontend and an interface to all the methods and techniques covered in the book. His role in the book was to provide R implementation of the QF problems and methods.
Michael Puhle obtained a Ph.D. in Finance from the University of Passau in Germany. He worked for several years as a Senior Risk Controller at Allianz Global Investors in Munich, and as an Assistant Manager at KPMG's Financial Risk Management practice, where he was advising banks on market risk models. Michael is also the author of Bond Portfolio Optimization published by Springer Publishing.
Edina Berlinger has a Ph.D. in Economics from the Corvinus University of Budapest. She is an Associate Professor, teaching corporate fi nance, investments, and fi nancial risk management. She is the Head of Department for Finance of the university and is also the Chair of the Finance Sub committee the Hungarian Academy of Sciences. Her expertise covers student loan systems, risk management, and, recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk. Peter
Peter Csoka is an Associate Professor at the Department of Finance, Corvinus University of Budapest, and a research fellow in the Game Theory Research Group, Centre For Economic and Regional Studies, Hungarian Academy of Sciences. He received his Ph.D. i
Daniel Havran is a postdoctoral research fellow at Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences. He also holds a part-time assistant professor position at the Corvinus University of Budapest, where he teaches corporate finance (BA, PhD) and credit risk management (MSc). He obtained his PhD in economics at Corvinus University of Budapest in 2011.
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Paperback. Condition: New. R is a statistical computing language that s ideal for answering quantitative finance questions. This book gives you both theory and practice, all in clear language with stacks of real-world examples. Ideal for R beginners or expert alike.Key FeaturesUse time series analysis to model and forecast house pricesEstimate the term structure of interest rates using prices of government bondsDetect systemically important financial institutions by employing financial network analysisBook DescriptionIntroduction to R for Quantitative Finance will show you how to solve real-world quantitative fi nance problems using the statistical computing language R. The book covers diverse topics ranging from time series analysis to fi nancial networks. Each chapter briefl y 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 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 such as credit risk management.What you will learnHow to model and forecast house prices and improve hedge ratios using cointegration and model volatilityHow to understand the theory behind portfolio selection and how it can be applied to real-world dataHow to utilize the Capital Asset Pricing Model and the Arbitrage Pricing TheoryHow to understand the basics of fixed income instrumentsYou will discover how to use discrete- and continuous-time models for pricing derivative securitiesHow to successfully work with credit default models and how to model correlated defaults using copulasHow to understand the uses of the Extreme Value Theory in insurance and fi nance, model fitting, and risk measure calculationWho 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. Seller Inventory # LU-9781783280933
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