CFRM 542 Financial Data Modeling and Analysis in R

This course is an in-depth hands-on introduction to the R statistical programming language (www.r-project.org) for computational finance. The course will focus on R code and code writing, R packages, and R software development for statistical analysis of financial data including topics on factor models, time series analysis, and portfolio analytics. Topics include:

  • The R Language. Syntax, data types, resources, packages and history
  • Graphics in R. Plotting and visualization
  • Statistical analysis of returns. Fat-tailed skewed distributions, outliers, serial correlation
  • Financial time series modeling. Covariance matrices, AR, VecAR
  • Factor models. Linear regression, LS and robust fits, test statistics, model selection
  • Multidimensional models. Principal components, clustering, classification
  • Optimization methods. QP, LP, general nonlinear
  • Portfolio optimization. Mean-variance optimization, out-of-sample back testing
  • Bootstrap methods. Non-parametric, parametric, confidence intervals, tests
  • Portfolio analytics. Performance and risk measures, style analysis
Instructor: 
Guy Yollin
Textbooks: 
D. Ruppert (2010). Statistics and Data Analysis for Financial Engineering, Springer and J. Adler (2009). R in a Nutshell: A Desktop Reference, O’Reilly Media
Software: 
R and R packages.
Prerequisites: 
CFRM 541 Investment Science or equivalent, and competency in R at the level of CFRM 463, or by permission of instructor.
Credits: 
4