The CER model

Before diving into the actual arithmetics, you first need to calculate the parameters of the constant expected return model (CER). Make use of the data in returns_df to estimate the model parameters for all four stocks.

This exercise is part of the course

Intro to Computational Finance with R

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Exercise instructions

Assign to sigma2_month the estimates of \(\sigma_i^2\) for all four assets.
Calculate sigma_month, the estimates of \(\sigma_i\) for all four assets.
Estimate the correlations \(\rho_{ij}\) between all stocks, and assign the result to cor_mat_month.
Create the pairwise scatterplots between all four stocks. Use coredata() to extract the core data from returns_df and pairs() to create a matrix of scatter plots. Take the color blue and use 16 for points pch.

Hands-on interactive exercise

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

# All data is preloaded in your workspace.  Type ls() in the console to see what has been loaded.

# Parameters CER model
mu_hat_month <- apply(returns_df, 2, mean)
mu_hat_month
sigma2_month <-

sigma2_month
sigma_month <-

sigma_month
cov_mat_month <- var(returns_df)
cov_mat_month
cor_mat_month <-

cor_mat_month

# Pairwise scatterplots

Edit and Run Code