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Course Description
Financial Data Modeling and Analysis in R (AMATH 542)
Web, Online, Winter 2013, Coursera
Instructor: Guy Yollin
M, W, 1/7 - 3/15, 2013, 4:30-6:20 p.m.
Cost: $3,600 | 4 Credits
This course is an in-depth hands-on introduction to the R statistical programming language ([Dear Guest/Member you can't see link before click here to register]) 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
Prerequisites
Candidates will have minimum of a bachelors, preferable a Master's degree in science or engineering, including applied mathematics, economics, mathematics, physics, statistics, computer science or electrical engineering, and a strong academic record; or will be MBA graduates with exceptionally strong quantitative and computational skills and a strong academic record. A detailed listing of mathematics and programming courses, with grades achieved, is required for enrollment consideration.
Course Description
Financial Data Modeling and Analysis in R (AMATH 542)
Web, Online, Winter 2013, Coursera
Instructor: Guy Yollin
M, W, 1/7 - 3/15, 2013, 4:30-6:20 p.m.
Cost: $3,600 | 4 Credits
This course is an in-depth hands-on introduction to the R statistical programming language ([Dear Guest/Member you can't see link before click here to register]) 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
Prerequisites
Candidates will have minimum of a bachelors, preferable a Master's degree in science or engineering, including applied mathematics, economics, mathematics, physics, statistics, computer science or electrical engineering, and a strong academic record; or will be MBA graduates with exceptionally strong quantitative and computational skills and a strong academic record. A detailed listing of mathematics and programming courses, with grades achieved, is required for enrollment consideration.