The aim of this workshop is to provide an introduction to the statistical software R for professionals and academics in Finance. This course gives an insight into possibilities of data analysis and statistics with R, import of data sets, generation of graphics and the preparation of reports. The main focus is on applications in Finance. An example of portfolio optimization highlights the options of Rmetrics, which is a collection of several hundreds of functions in the area of Financial Engineering and Computational Finance.

Key features:
-Preparations and installation of R
-Data import, data types and variables
-Simulations in R
-Graphics in R
-Exploratory analysis in R with special focus to time series data
-Applications in Finance: Portfolio optimization and VaR
-R and Excel
-Preparing reports

The workshop provides inside in the statistical models and concepts in R, which are useful for various problems arising in Finance. The attendees will be able to import datasets into R, analyse them statistically and apply concepts from time series modelling. An example on how to optimise a portfolio in R will show various concepts in financial mathematics and statistics, which are provided in R. In practical sessions, the attendees will learn and practice how to use R.

Day One

• An introductory example

• Preparations and installation of R
• Background of R
• Obtaining R
• R installation
• Contributed Packages
• R documentation, R help and web resources
• R Console and R GUI

• Data import, data types and variables
• Elementary methods
• Data types
• Variable generation, inspection and modification
• Coding
• Indexing
• Date types
• Missing data

• Simulations in R
• Random in R: sample
• Simulating from given distributions
• Reproducible numbers: seed
• Simulating Outliers

• Graphics in R
• Workhorse: plot
• Where to plot to: devices
• Setting the setting: par
• High-level and low-level plotting functions
• Legends
• Math in Plots

Day Two

• Application in Finance
• R Metrics
• Calculating Value at Risk
• Packages for Portfolio Optimization

Guest Presentations:

Random Permutation Tests With R.
Patrick Burns,  Burns Statistics

R makes performing random permutation tests extremely easy.  These are very intuitive tests that are often very useful in practice. They can also provide the conceptual link to make more traditional statistical tests understandable.

The Statistical Bootstrap with R.
Patrick Burns, Burns Statistics

We'll cover what it is, why it is important, and how to do it in R.  Emphasis will be placed on using it in every day data analysis (as opposed to formal statistical use).

Workshop Sessions Continued:-

• R and Excel
• The classic way: im-/exporting .csv files
• Excel-like: Rcommander (J.Fox)
• Real Excel: RExcel (T. Baier, E. Neuwirth)
• xlsReadWrite

• Preparing reports
• No web idea
• Sweave/ odfWeave

• Outlook
• Programming in R
• Writing R Documentation
• Own Packages

Dr Peter Ruckdeschel is a research associate at the Financial mathematics group at Fraunhofer ITWM, Kaiserslautern and at the University of Kaiserslautern.
He received his PhD in Statistics at University Bayreuth in 2001 with an award-winning dissertation on Robust Kalman Filtering. He is co-/author of 13 R packages available on the comprehensive R archive network, ranging from integration of automatic markup to R documents, over objected-oriented implementations of distributions to optimally robust estimation. Within the collaborative software development platform r-forge, he is developing—with other authors—R package robKalman. Since his affiliation to ITWM, he has been working on several industry projects covering parameter estimation in stochastic correlation models, quantification of operational risk, and pricing of loans in illiquid markets.

 
Dr Christina Erlwein  is a research associate at the Financial mathematics group at Fraunhofer ITWM, Kaiserslautern. She received her PhD in financial ma¬the¬matics on hidden Markov models in Finance from CARISMA, Brunel University in 2008. She was awarded a Marie Curie Fellowship for Early Stage Researchers and worked within international research projects on financial mathematics at CMA, University of Oslo, Norway, Heriott-Watt University, UK and University of Western Ontario, Canada. She published several papers on applications of HMMs in Finance. Since 2008 she is affiliated to ITWM, where she works on various projects with the financial industry ranging from modelling alternative investments to software concepts for statistical models and credit pricing.

Patrick Burns – Burns Statistics
Patrick Burns owns Burns Statistics which focuses on software and consulting for fund management. He was in the equity and equity research departments of Citigroup before resigning in 2002 to found Burns Statistics.Before entering finance he was a lead developer of S-PLUS in its early days. Patrick is well-known in the R community.