Problem Set 2 (2nd of 2) helpful hints)

The first set of problem set 2 hints appears at

Here’s the second problem set 2 hint, based on the following questions:

Finance 4335 Student Question: “I am in the process of completing Problem Set 2, but I am getting stumped, are there any class notes that can help me with 1E and question 2? I am having trouble finding the correct formulas. Thank you.”

Dr. Garven’s Answer (for question 2): Yesterday, I provided some hints about problem set #2 @  Regarding question #2, here is what I wrote there: “The second problem involves using the standard normal probability distribution to calculate the probabilities of earning various levels of return by investing in risky securities and portfolios; see pp. 13-19 of the lecture note for coverage of that topic.”

The inputs for this problem are all specified in parts A and B; in part A, the expected return is 10% and the standard deviation is 20%, whereas in part B, the expected return is 6.5% and the standard deviation is 10%.  The probability of losing money in parts A and B requires calculating the z statistics for both cases.  In part A, the z stat is z = (0-mu)/sigma= (0-10)/20, and in part B it is z = (0-mu)/sigma= (0-6.5)/10.  Once you have the z stats, you can obtain probabilities of losing money in Parts A and B by using the z table from the course website.

Part C asks a different probability question – What the probability of earning more than 6% is, given the investment alternatives described in parts A and B?  To solve this, calculate 1-N(z) using the z table, where z = (6-mu)/sigma.

Dr. Garven’s Answer (for question 1E): Regarding 1E – since returns on C and D are uncorrelated, this means that they are statistically independent of each other.  Thus, the variance of an equally weighted portfolio consisting of C and D is simply the weighted average of these securities’ variances.  See the lecture note, page 9, the final two bullet points on that page.  Also see the portfolio variance equation on page 13, which features two variance terms and one covariance term – Since C and D are statistically independent, the third term there (2w(1)w(2)sigma(12)) equals 0.

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