- Paperback: 164 pages
- Publisher: Packt Publishing Limited (November 22, 2013)
- Language: English
- ISBN-10: 178328093X
- ISBN-13: 978-1783280933
- Product Dimensions: 7.5 x 0.4 x 9.2 inches
- Shipping Weight: 376 g
- Average Customer Review: Be the first to review this item
Introduction to R for Quantitative Finance Paperback – 22 November 2013
|New from||Used from|
Amazon Global Store US
About the Author
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. Marton 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 Varadi 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 PhD 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 mainly teaches 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 in the Hungarian Government Debt Management Agency. Her main research areas are government debt management (in general) and sovereign crises and defaults (in particular). She is a CEFA and CIIA diploma holder.
No customer reviews
|5 star (0%)||0%|
|4 star (0%)||0%|
|3 star (0%)||0%|
|2 star (0%)||0%|
|1 star (0%)||0%|
Review this product
Most helpful customer reviews on Amazon.com
Overall I think i got a great deal out of this book and it gives me much more confidence in working with R. Espeically for people just getting into either quant finance or working in R
Using the kindle version, i did have some annoying problems with the code. Because of 2 things. First off there are + signs on every line which is how its outputted in R, which you must remove. Also the kindle cut/paste function always gives the copyright and location. Which you have to delete out of every time you cut and paste. Its not really the authors fault just a quirk of the kindle cut and paste system. I did not subtract a star for this because of the kindle interface, however i can see where someone else would.
I'll use one example to illustrate the faults of this book. The authors give an example of cointegration and hedging of airplane fuel with options on heating oil. The authors state that they assume that you have a background in finance. Fine. I've done this kind of hedging, but it was a year ago. Having a brief discussion of the relevant equations would be useful. This would provide a context for the R code.
As far as the R code goes, all the authors really give are some function calls without much in the way of context. You would get almost as much reading the R on-line documentation.
This book is of no use to anyone who knows something about modern quantitative finance and it's of no use when it comes to learning to use R for finance. I short, the book is of no use at all.
Packt Publishing seems to specialize in short books that are at either poorly written or at a ridiculously introductory level. I also bought their Machine Learning with R. This could be retitled Machine Learning with R for High School students (and not High School students who are taking AP Calculus or Computer Science).
The portfolio optimization chapter is good.
The text is refreshingly straightforward.
On the other hand, not all of the the code is available for download.