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Introduction to R for Quantitative Finance von [Daróczi, Gergely, Puhle, Michael, Berlinger, Edina, Csóka, Péter, Havran, Daniel, Michaletzky, Márton, Tulassay, Zsolt, Váradi, Kata, Vidovics-Dancs, Agnes]
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Introduction to R for Quantitative Finance Kindle Edition

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Länge: 164 Seiten Sprache: Englisch

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Produktbeschreibungen

Kurzbeschreibung

In Detail

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.

Approach

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

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

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

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

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

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

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

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

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

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.


Produktinformation

  • Format: Kindle Edition
  • Dateigröße: 3826 KB
  • Seitenzahl der Print-Ausgabe: 164 Seiten
  • Verlag: Packt Publishing (22. November 2013)
  • Verkauf durch: Amazon Media EU S.à r.l.
  • Sprache: Englisch
  • ASIN: B00GUKM7CS
  • Text-to-Speech (Vorlesemodus): Aktiviert
  • X-Ray:
  • Word Wise: Nicht aktiviert
  • Verbesserter Schriftsatz: Nicht aktiviert
  • Durchschnittliche Kundenbewertung: 2.0 von 5 Sternen 1 Kundenrezension
  • Amazon Bestseller-Rang: #396.376 Bezahlt in Kindle-Shop (Siehe Top 100 Bezahlt in Kindle-Shop)

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Format: Taschenbuch Verifizierter Kauf
I admit, it is not easy to write an introduction text covering two subjects like quantitative finance and a programming language like R. It is quite easy to disappoint readers on one of the two aspects. However, this book disappoints on both.

At the back cover, the publisher states that some basic knowledge of finance theory is assumed. Working for more than 25 years in the area of fixed income and commodity research, I have some knowledge of the subjects. However, I found that the authors assume too much knowledge from the reader, especially as the book covers 9 different areas and one can not be an expert in all those fields. Thus, some more detailed explanations of the basics would have been advantageous. Furthermore, some aspects of quantitative finance are not covered. For example in the chapter on the term structure of interest rate, there are some standard models like the Vasicek or the Cox-Ingersoll-Ross (CIR) factor models. It would have been a plus to include an example of estimating the parameters of these models by state space models in R.

I do most of my quantitative work with a commercial software which I regard as very good. However, also this software has some limitations. Thus, I looked for an additional software and saw in the literature that more and more examples are provided in R. Another advantage of R is that it is available as free of charge open source. But also this comes at a cost. Beside the core components, which are installed with R at the computer, there are further packages required to perform some basic tasks. A plethora of packages are available and as one would suppose with open source software, some packages are providing features already being included in other ones.
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Die hilfreichsten Kundenrezensionen auf Amazon.com (beta)

Amazon.com: 3.0 von 5 Sternen 13 Rezensionen
7 von 7 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen An optional, below-average-quality convenience 25. Februar 2014
Von Dimitri Shvorob - Veröffentlicht auf Amazon.com
Format: Kindle Edition
The thin book has about 140 small pages and combines nine short contributed papers, which look like a set of notes for a master's-level finance course. Here's the list of topics, and the R packages in use.

AR and GARCH estimation (timeSeries, zoo, FinTS, urca, rugarch)
Mean-variance portfolio optimization (fPortfolio)
CAPM / "beta" (n/a)
Yield-curve fitting (termstrc)
Black-Scholes, SDE simulation and copulae (fOptions, sde, copula)
Extreme-value theory / GPD fitting (evir)
Network analysis (igraph)

With 10-20 pages per chapter, coverage is brief, and assumes that the reader knows the finance and statistics parts, and is familiar with R, so only the specific R examples need to be shown. This creates a problem, as finance/statistics/R-beginners are discouraged, while the non-beginners find sketchily explained basics. You really can find equally good, or better, references online - the R-Bloggers site has many; sometimes you can just look up the R package's vignette - but if you are willing to pay $15 for a convenience, go for it.

UPD. With the benefit of a little more life experience, I would say: don't spend your time on *any* R book. Python is the way to go.
11 von 14 Kunden fanden die folgende Rezension hilfreich
1.0 von 5 Sternen This is a book completely without redeeming value 29. Januar 2014
Von Ian K. - Veröffentlicht auf Amazon.com
Format: Kindle Edition Verifizierter Kauf
This book claims to be a book on R in finance. It provides very little information on either R or finance.

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).
1 von 1 Kunden fanden die folgende Rezension hilfreich
3.0 von 5 Sternen Useful for Existing Users 26. März 2014
Von JamesR - Veröffentlicht auf Amazon.com
Format: Taschenbuch
All in all, this is an excellent book for anyone keen on learning R in a quantitative finance framework. I think it would have benefited from a formal introduction to R and a data Export/Import capabilities review but both topics are extensively covered in many other R resources...
3.0 von 5 Sternen Interesting, but room for improvement 14. Mai 2014
Von Swiss_Finance - Veröffentlicht auf Amazon.com
Format: Taschenbuch
As there are currently no R book which specifically cover quantitative finance in broader terms, the book is interesting for all finance guys who want to quickly understand implementation in R.
The book is intended to be an introduction to R for readers with some knowledge in finance. It is basically a collection of self-contained essays on some well-known concepts of finance like portfolio optimization, term structure of interest rates and derivatives pricing. These concepts are then implemented in R. To really understand what is going on, a fairly advanced understanding of finance theory and econometrics (master level at least) and a working knowledge or R are required, although the authors claim that no prior R knowledge is needed. There is no R intro included and the code is not easy to follow through without prior knowledge (e.g. the apply functionality is utilized and for R novices this is a complicated animal since it is unique to R and is one of the hottest topics in all help forums). The R code is usually sound, however, there are parts where it could be simplified and there are parts of the code that are not fully explained.

A drawback of the book is that it is very short and sometimes the text lacks technical precision or uses unconventional approaches (examples: if the conditions of the CAPM are satisfied, all securities will be on the SML while the text states that they “should” or “the APT states that, in equilibrium, no arbitrage opportunities can exist” – the APT is not an equilibrium model, absence of arbitrage is a necessary but not a sufficient condition for the existence of an equilibrium). At a later stage, it is said that the CAPM is an equilibrium model, while APT is a statistical model (it is in fact a no-arbitrage model). The literature references are not optimal, e.g. the original papers of Markowitz are not cited in the literature references on portfolio theory.

The book also contains some relevant typos such as “keynote” duration instead of key rate duration.

The R code is generally commented well, however in some cases the code is complicated and could be simplified substantially. For example, the code on p. 79 is certainly unreadable for somebody with no prior knowledge in R. Some important aspects of the code are not explained.

All in all, the book does a reasonable job to show how some key concepts of finance can be implemented in R. The book, however, is neither an introduction to R nor an introduction to finance. The book would benefit from a more stringent presentation of finance theory and a simplification of the code chunks.
3.0 von 5 Sternen Useful for existing R users 2. März 2014
Von Arnold Salvacion - Veröffentlicht auf Amazon.com
Format: Taschenbuch
The Introduction to R for Quantitative Finance which is around 164 pages (including cover page and back pages) discuss the implementation different quantitative methods used in finance using R language . The book consists of nine (9) chapters cover topics from time series analysis to finance networks.

For individuals with little background in quantitative methods in finance, the theoretical and application discussion in the start of each chapter provided a good overview and basics of the method being discussed. Also, the problem-solution approached used by the authors added practicality on the used of the book for quantitative analysis. However, for individuals (i.e. finance people) with little R background, the book somehow lacks the basic introduction to the R language and environment commonly found in most R books and tutorials. I think for finance individuals who are R beginners, it will be handy to use this book along with R introductory books/websites (e.g. Instant R, Quick R)
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