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In the previous video, you saw the difference between a capital allocation budget and a risk budget. In this exercise, you will construct a risk budget, and discover how large each asset's percent risk contribution is in the total portfolio volatility.

For this last exercise, you will calculate the risk contributions for a portfolio that is again invested 40% in equities, 40% in bonds, 10% in real estate, and 10% in commodities. The function StdDev() plays an important role in this exercise. The StdDev() function creates a list of the assets' standard deviation ($StdDev), their risk contribution ($contribution), and their percent risk contribution ($pct_contrib_StdDev).

You will be using three arguments in the StdDev() function to do this calculation. The first is R, a vector, matrix, data frame, time series, or zoo object of returns. The second is portfolio_method, which you will set to component, and the third is weights.

The object returns is loaded in your workspace.

Diese Übung ist Teil des Kurses

Introduction to Portfolio Analysis in R

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Anleitung zur Übung

Create a vector of the portfolio weights called weights. Remember, order matters!
Calculate your volatility budget using StdDev() on the return series returns. Set portfolio_method = "component" and weights equal to the created vector of weights. Call this vol_budget.
Combine the weights and the percentage risk contributions in a table called weights_percrisk using cbind().
Print the table and note how different the percentage risk contributions are compared to the portfolio weights.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Create portfolio weights


# Create volatility budget
vol_budget <- StdDev(___, portfolio_method = "___", weights = ___)

# Make a table of weights and risk contribution

colnames(weights_percrisk) <- c("weights", "perc vol contrib")

# Print the table

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