Free course

# Intro to Computational Finance with R

7 Hours87 Exercises19,406 Learners7700 XP

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## Course Description

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.
1. 1

### Return calculations

Free

Learn how to calculate, analyze and plot simple and continuously compounded returns in R.

Load the monthly Starbucks return data
100 xp
Get a feel for the data
100 xp
Extract the price data
100 xp
Find indices associated with the dates 3/1/1994 and 3/1/1995
100 xp
Subset directly on dates
100 xp
Plot the price data
100 xp
Calculate simple returns
100 xp
Add dates to simple return vector
100 xp
Compute continuously compounded 1-month returns
100 xp
Compare simple and continuously compounded returns
100 xp
Graphically compare the simple and continuously compounded returns
100 xp
Calculate growth of \$1 invested in SBUX
100 xp
Compute one simple Starbucks return
50 xp
Compute one continuously compounded Starbucks return
50 xp
Monthly compounding
50 xp
Simple annual Starbucks return
50 xp
Annual continuously compounded return
50 xp
2. 2

### Random variables and probability distributions

Free

Learn how to work with probability distributions in R in the context of return and value-at-risk calculations.

3. 3

### Bivariate distributions

Free

Explore bivariate probability distributions in R.

4. 4

### Simulating time series data

Free

Learn how to use R to simulate autoregressive and moving average processes.

5. 5

### Analyzing stock returns

Free

Learn how to analyze stock returns with the R packages PerformanceAnalytics, zoo and tseries.

6. 6

### Constant expected return model

Free

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.

7. 7

### Introduction to portfolio theory

Free

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.

8. 8

### Computing efficient portfolios using matrix algebra

Free

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.