Financial Time Series Data

Welcome to this course on financial time series analysis using R. In this course, we will learn about financial time series data analysis in R. You will learn about how to explore and build time series data, calculate its key statistics, and plot time series charts. You will also learn about how to use the important time series models such as White Noise, Random Walk, Autoregression and Moving Average, learn how to simulate these models, fit these models into financial time series data and use the models to predict the future. So, let's get started...

What is Time Series?

Time series refers to a series of data in a chronological order. A lot of data in this world is recorded sequentially, over time, in the form of time series. Some common examples include the weather in a city over time, the prices of a listed stock, the commodity prices and so on. While studying financial assets, the asset prices as well as asset returns are represented as time series. Investors generally prefer to use asset returns, over asset prices, in their analysis. This is primarily for two reasons: 1) the asset returns provide a complete and scale-free summary of asset returns and 2) the asset returns are easier to analyze compared to asset prices because of their statistical properties. In our lessons, we will use examples of both asset prices and asset returns.

Financial Time Series Data

There are plenty of financial time series data sources on the Internet. One popular source is Quandl, which contains thousands of datasets including financial and economic datasets.

For the purpose of this course, I would suggest you to signup for a free account on Quandl.com. Once you signup, you will get an API key that you can use to fetch data directly in R from Quandl.

Install Quandl R Package

In order to work with Quandl datasets in R, you need to install and load the Quandl package.