Free course

# Intro to Computational Finance with R

7 Hours87 Exercises18,754 Learners
7700 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.<br>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

Learn how to calculate, analyze and plot simple and continuously compounded returns in R.
2. 2

### Random variables and probability distributions

Learn how to work with probability distributions in R in the context of return and value-at-risk calculations.
3. 3

### Bivariate distributions

Explore bivariate probability distributions in R.
4. 4

### Simulating time series data

Learn how to use R to simulate autoregressive and moving average processes.
5. 5

### Analyzing stock returns

Learn how to analyze stock returns with the R packages PerformanceAnalytics, zoo and tseries.
6. 6

### 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.
7. 7

### 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.
8. 8

### 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.

## What do other learners have to say?

I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.

Devon Edwards Joseph
Lloyds Banking Group

DataCamp is the top resource I recommend for learning data science.

Louis Maiden
Harvard Business School

DataCamp is by far my favorite website to learn from.

Ronald Bowers
Decision Science Analytics, USAA