In this course, you'll make use of R to analyze financial data, estimate statistical models, and construct optimized portfolios. You will learn how to build probability models for assets returns, the way you should apply statistical techniques to evaluate if asset returns are normally distributed, methods to evaluate statistical models, and portfolio optimization techniques.
The material in this course was originally developed as a complement to Prof. Eric Zivot's Coursera lectures. Having a good mathematical basis, and an interest in financial markets is recommended.
Learn how to calculate, analyze and plot simple and continuously compounded returns in R.
Random variables and probability distributions
Learn how to work with probability distributions in R in the context of return and value-at-risk calculations.
Explore bivariate probability distributions in R.
Simulating time series data
Learn how to use R to simulate autoregressive and moving average processes.
Analyzing stock returns
Learn how to analyze stock returns with the R packages PerformanceAnalytics, zoo and tseries.
Constant expected return model
Estimate parameters of the constant expected return (CER) model, compute standard errors and confidence intervals and test various hypotheses about the parameters and assumptions of the model. Perform bootstrapping of CER model estimates.
Introduction to portfolio theory
Compute portfolios that consist of Boeing and Microsoft, T-bills and Boeing, T-bills and Microsoft and T-bills and combinations of Boeing and Microsoft. Use R functions to compute the global minimum variance portfolio and the tangency portfolio.
Computing efficient portfolios using matrix algebra
Using the monthly closing price data on four Northwest stocks, you will estimate expected returns, variances and covariances to be used as inputs to the Markowitz algorithm. You will compute the global minimum variance portfolio, efficient portfolios, and the tangency portfolio for short-sales allowed and for short-sales not allowed.