About this Course

This course is an in-depth hands-on introduction to the R statistical programming language 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.

Topics include:

• The R language – syntax, data types, resources, packages and history, graphics, visualization
• 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

Prerequisites: R Programming for Quantitative Finance (CFRM 463) or prior programming experience in R, or experience in a modern programming language (C family, Java, Python).