Quantitative 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.
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.
Who this book is for
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.
Über den Autor und weitere Mitwirkende
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. 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 finance, investments, and financial 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.
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. He received his Ph.D. in Economics from Maastricht University in 2008. His research topics include risk measures, risk capital allocation, game theory, corporate finance, and general equilibrium theory. He is currently focused on analyzing risk contributions for systemic risk and for illiquid portfolios. He has papers published in journals such as Mathematical Methods of Operational Research, European Journal of Operational Research, Games and Economic Behaviour, and Journal of Banking and Finance. He is the Chair of the organizing committee of the Annual Financial Market Liquidity Conference in Budapest.
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, where he teaches Corporate Finance (BA and Ph.D. levels), and Credit Risk Management (MSc) courses. He obtained his Ph.D. in Economics at Corvinus University of Budapest in 2011. His research interests are corporate cash, funding liquidity management, and credit derivatives over-the-counter markets.
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. As Capital Market Transactions Manager, he gained experience in an EUR 3 bn securitization at the Hungarian State Motorway Management Company. In 2012, he took part in the preparation of an IPO and the private placement of a Hungarian financial services provider. Prior to joining DBH Investment, he was an assistant professor at the Department of Finance of CUB.
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. Zsolt holds MA degrees in Economics from Corvinus University of Budapest and Central European University. His research interests include derivatives pricing, yield curve modeling, liquidity risk, and heterogeneous agent models.
Kata Váradi is an Assistant Professor at the Department of Finance, Corvinus University of Budapest since 2013. Kata graduated in Finance in 2009 from Corvinus University of Budapest, 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. Her research areas are market liquidity, fixed income securities, and networks in healthcare systems. Besides doing research, she is active in teaching as well. She teaches mainly Corporate Finance, Investments, Valuation, and Multinational Financial Management.
Agnes Vidovics-Dancs is a PhD candidate and an Assistant Professor at the Department of Finance, Corvinus University of Budapest. Previously, she worked as a Junior Risk Manager for the Hungarian Government Debt Management Agency. Her main research areas are government debt management in general and sovereign crises and defaults.