Aggregating log-return series

In statistics, aggregate data are data combined from several measurements. You just learned that you can compute compute weekly, monthly and quarterly log-returns by summing daily log-returns with the corresponding apply.weekly(), apply.monthly() and apply.quarterly() functions.

For example, you can use the following code to form the quarterly returns for a univariate time series data and multivariate time series mv_data:

> # apply.quarterly(x, FUN, ...)
> data_q = apply.quarterly(data, sum)
> mv_data_q = apply.quarterly(mv_data, colSums)

In this exercise, you will practice aggregating time series data using these functions and plotting the results. The data DJ and DJ_const are available in your workspace, as are the objects djx, which contains daily log-returns of the Dow Jones index from 2000-2015, and djreturns, which contains the daily log-returns for the first four DJ_const stocks from 2000-2015. Use plot for univariate time series and plot.zoo for multivariate time series.

This exercise is part of the course

Quantitative Risk Management in R

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Exercise instructions

Plot the object djx.
In one line, plot the weekly log-returns of djx with vertical bars.
Plot the monthly log-returns of djx with vertical bars.
Plot the object djreturns using plot.zoo.
Plot the monthly log-returns for djreturns with vertical bars using plot.zoo.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Plot djx
___(___)

# Plot weekly log-returns of djx
___(___, ___)

# Plot monthly log-returns of djx
___(___, ___)

# Plot djreturns
___(___)

# Plot monthly log-returns of djreturns
___(___, ___)

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