1. Compute the variance-covariance matrix for 5 selected stocks. Use the Matrix of demeaned returns to do the calculation. 2. Compute the Expected return of the portfolio assuming equal weights. 3. Calculate the variance of the portfolio assuming equal weights. 4. Assuming no short-selling, optimize the portfolio using the solver and report the expected return, variance, and vector of optimum weights for the MVP. 5. Assume that short selling is allowed, optimize the portfolio using the solver and report the expected return, variance, and the vector of optimum weights for the MVP.. 6. Plot the MVS assuming that short selling is not allowed. 7. Plot the CML and be sure that is tangent to the MVS. Instructions: 1. You submit a soft- and a hardcopy of your work. 2. You can work as a group of two. 3. Due date 30/3/2021. 4. Write down the function used for calculation. Assume that I do not know how to do the calculation and I will use your command as a guide. For example: Suppose that I want to calculate the covariance of 2 arrays, then the command that I need to show is: -Covariance.s(D6, D150) The result is 1.5526 If you fail to show the commands for each step you will lose at least 3 points. This is a preparation for the final. 1 1. Compute the variance-covariance matrix for 5 selected stocks. Use the Matrix of demeaned returns to do the calculation. 2. Compute the Expected return of the portfolio assuming equal weights. 3. Calculate the variance of the portfolio assuming equal weights. 4. Assuming no short-selling, optimize the portfolio using the solver and report the expected return, variance, and vector of optimum weights for the MVP. 5. Assume that short selling is allowed, optimize the portfolio using the solver and report the expected return, variance, and the vector of optimum weights for the MVP.. 6. Plot the MVS assuming that short selling is not allowed. 7. Plot the CML and be sure that is tangent to the MVS. Instructions: 1. You submit a soft- and a hardcopy of your work. 2. You can work as a group of two. 3. Due date 30/3/2021. 4. Write down the function used for calculation. Assume that I do not know how to do the calculation and I will use your command as a guide. For example: Suppose that I want to calculate the covariance of 2 arrays, then the command that I need to show is: -Covariance.s(D6, D150) The result is 1.5526 If you fail to show the commands for each step you will lose at least 3 points. This is a preparation for the final. 1