Exercise

# The time series of portfolio returns

In the previous exercise, you created a variable called `returns`

from the daily prices of stocks of Apple and Microsoft. In this exercise, you will create two portfolios using the return series you previously created. The two portfolios will differ in one way, and that is the weighting of the assets.

In the last video, you were introduced to two weighting strategies: the buy and hold strategy, and a monthly rebalancing strategy. In this exercise, you will create a portfolio in which you don’t rebalance, and one where you rebalance monthly. You will then visualize the portfolio returns of both.

You will use the function Return.portfolio() for your calculations. For this function, you will provide three arguments: `R`

, `weights`

, and `rebalance_on`

. `R`

is a time series of returns, `weights`

is a vector containing asset weights, and `rebalance_on`

specifies which calendar-period to rebalance on. If you need help, be sure to check the documentation by clicking on the function!

For this exercise, you will be working with the `returns`

data that are pre-loaded in your workspace.

Instructions

**100 XP**

- Create a vector of weights for two equally weighted assets called
`eq_weights`

. Remember that weights must add to 1. - Create a portfolio using the buy and hold strategy using
`Return.portfolio()`

. Note, you do not need to specify a rebalance period. Call this`pf_bh`

. - Create a portfolio where you rebalance your weights monthly. Use
`Return.portfolio()`

with the argument`rebalance_on = "months"`

. Call this`pf_rebal`

. - Plot the time series of each portfolio using
`plot.zoo()`

.`par(mfrow = c(2, 1), mar = c(2, 4, 2, 2))`

is used to organize the plots you create. Do not alter this code.