Mastering R for Quantitative Finance: Use R to Optimize Your Trading Strategy and Build Up Your Own Risk Management System

This book is intended for those who want to learn how to use R's capabilities to build models in quantitative finance at a more advanced level. If you wish to perfectly take up the rhythm of the chapters, you need to be at an intermediate level in quantitative finance and you also need to have a reasonable knowledge of R.

Edina Berlinger has a PhD 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 the Finance department of the university, and is also the chair of the finance subcommittee of the Hungarian Academy of Sciences. Her expertise covers loan systems, risk management, and more recently, network analysis. She has led several research projects in student loan design, liquidity management, heterogeneous agent models, and systemic risk. Ferenc Illes has an MSc degree in mathematics from Eotvos Lorand University. A few years after graduation, he started studying actuarial and financial mathematics, and he is about to pursue his PhD from Corvinus University of Budapest. In recent years, he has worked in the banking industry. Currently, he is developing statistical models with R. His interest lies in large networks and computational complexity. Milan Badics has a master's degree in finance from the Corvinus University of Budapest. Now, he is a PhD student and a member of the PADS PhD scholarship program. He teaches financial econometrics, and his main research topics are time series forecasting with data-mining methods, financial signal processing, and numerical sensitivity analysis on interest rate models. He won the competition of the X. Kochmeister-prize organized by the Hungarian Stock Exchange in May 2014.