Packt is pleased to announce the release of its new book Introduction to R for Quantitative Finance; a tutorial-based guide that helps readers to understand the basics of, and become accomplished with, the use of R for quantitative finance.
About the Author:
Gergely Daróczi is a Ph.D. candidate in Sociology with around eight years' experience in data management and analysis tasks within the R programming environment.
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.
Edina Berlinger has a Ph.D. in Economics from the Corvinus University of Budapest. She is an Associate Professor, teaching corporate finance, investments, and financial risk management.
Péter Csóka Péter Csóka 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.
Daniel Havran is a Post Doctoral Fellow at the Institute of Economics, Centre for Economic and Regional Studies, Hungarian Academy of Sciences. He also holds a part-time Assistant Professorship position at the Corvinus University of Budapest.
Márton Michaletzky obtained his Ph.D. degree in Economics in 2011 from Corvinus University of Budapest. Between 2000 and 2003, he has been a Risk Manager and Macroeconomic Analyst with Concorde Securities Ltd.
Zsolt Tulassay works as a Quantitative Analyst at a major US investment bank, validating derivatives pricing models. Previously, Zsolt worked as an Assistant Lecturer at the Department of Finance at Corvinus University, teaching courses on Derivatives, Quantitative Risk Management, and Financial Econometrics.
Kata Váradi has been an Assistant Professor at the Department of Finance, Corvinus University of Budapest since 2013. Kata graduated in Finance from Corvinus University of Budapest, 2009, and was awarded a Ph.D. degree in 2012 for her thesis on the analysis of the market liquidity risk on the Hungarian stock market.
Agnes Vidovics-Dancs is a Ph.D. candidate and an Assistant Professor at the Department of Finance, Corvinus University of Budapest. Previously, she worked as a Junior Risk Manager in the Hungarian Government Debt Management Agency.
Introduction to R for Quantitative Finance, is a guide on how to use and master R in order to solve quantitative finance problems. This book covers the essentials of quantitative finance, taking readers through a number of clear and practical examples in R that will not only help them to understand the theory, but how to effectively deal with own real-life problems.
Introduction to R for Quantitative Finance, starts with time series analysis, and readers 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.
Introduction to R for Quantitative Finance covers the following topics:
Chapter 1: Time Series Analysis
Chapter 2: Portfolio Optimization
Chapter 3: Asset Pricing Models
Chapter 4: Fixed Income Securities
Chapter 5: Estimating the Term Structure of Interest Rates
Chapter 6: Derivatives Pricing
Chapter 7: Credit Risk Management
Chapter 8: Extreme Value Theory
Chapter 9: Financial Networks
The book is ideal for those who are looking to use R to solve problems in quantitative finance. Basic knowledge of financial theory is needed, 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 beginners and more experienced users.
|Introduction to R for Quantitative Finance|
|Use time series analysis to model and forecast house prices
For more information, please visit: http://www.packtpub.com/introduction-to-r-for-quantitative-finance/book