Ex1-20_Homework Set_1 + Solutions.pdf(Finance).pdf - Homework Exercises NB These exercises are similar to those that you will encounter on the exam and

# Ex1-20_Homework Set_1 + Solutions.pdf(Finance).pdf -...

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Homework Exercises NB: These exercises are similar to those that you will encounter on the exam and more difficult than those in Berk and DeMarzo book. Making these exercises is not obligatory but of course if you do not make them, most likely you will not be able to pass the exam. During AWVs these exercises will be discussed. Needless to say, additionally you should be able to solve all the problems at the ends of Chapters 10-13; these problems are similar to your MyFinanceLab tests . 1. It is well-known that the volatility of gold prices has been very low historically: around 12% per annum. Lately, the annualized gold volatility has reached 32%. For both historical and current gold volatilities, compute also the daily, weekly and monthly gold volatilities. Historic: σ day = 12%/√(251) = 0.757% Current: σ day = 32%/√(251) = 2.020% σ week = 12%/√(52) = 1.664% σ week = 32%/√(52) = 4.438% σ month = 12%/√(12)= 3.464% σ month = 32%/√(12)= 9.238% 2. Historical annualized return on stocks is approximately 8% and the annualized volatility is 35%. Compute daily, weekly and monthly volatilities. σ day = 35%/√(251) = 2.209% , σ week = 35%/√(52) = 4.854% , σ month = 35%/√(12)= 10.104% 3. The following table gives monthly closing prices of two stocks for the last 12 months. Date QLogic Mattel Dec 27 . 6 22 . 8 Jan 25 . 4 21 . 7 Feb 26 . 4 22 . 6 Mar 28 . 6 23 . 5 Apr 27 . 9 23 . 6 May 25 . 2 22 . 0 Jun 25 . 8 22 . 8 Jul 26 . 1 23 . 2 Aug 21 . 5 19 . 9 Sep 19 . 2 17 . 2 Oct 19.1 16 . 3 Nov 19 . 9 16 . 4 a) Compute monthly stock price returns. Assume no dividends. Compute average monthly returns. b) Compute variances, volatilities and annualized volatilities of both stock returns. c) Compute the covariance between these stock returns and the correlation coefficient. d) What are the average return, variance and annualized volatility of the portfolio consisting for 50% of QLogic and 50% of Mattel? e) Now suppose you have 10 000 \$ to invest. You borrow and short sell 5 000 \$ worth of Mattel stock and invest the resulting 15 000 \$ into QLogic. What is now the expected return and the monthly and yearly volatility of your portfolio? f) Explain what happens to the portfolio volatility in e) and in f) if the correlation between the stock increases? Why this happens? a) Returns(t+1): (Rt+1 - Rt)/Rt Qlogic: Return(jan): (25.4 - 27.6)/27.6= - 0.0797 or - 7.97% Return(feb): (26.4 - 25.4)/25.4= 0.0394 or 3.94% etc. Mattel: Return(jan): (21.7 - 22.8)/22.8= - 4.82% Return(feb): (22.6 - 21.7)/21.7= 4.15% etc. Average monthly returns: : Qlogic= - 2.631% , Mattel= - 2.734% b) Var(R Qlogic , monthly)=( ( - 7.97% - - 2.631%)2 + (3.94% + 2.631%)2 + ……)/(11 - 1)= 0.00622, σ Qlogic (monthly)= √(0.00622)= 7.89%, σ Qlogic (yearly)= √(12)*7.89%= 27.32% Var(R Mattel ,monthly) = ( ( - 4.82% - - 2.734%)2 + (4.15% + 2.734%)2 + ……)/(11 - 1) = 0.00451, σ Mattel (monthly)= √(0.00451)= 6.71%, σ Mattel (yearly)= √(12)*6.71%= 23.26% c ) (( - 7.97% - - 2.631%) * ( - 4.82% - - 2.734%)+(3.94% + 2.631%) * (4.15% + 2.734%)+… … … …)/(11 - 1)= 0.004823 Measurement of the variance between stocks Positive: returns tend to move in the same direction, Negative: returns tend to move in the opposite direction Correlation i,j = 0.004823/(7.89%*6.71%)= 0.9165 ‘’ normalized covariance’’, always between - 1 and +1. If Ri on average changes with x% then Rj will on average change with ρ i,j *x% d) Rp=0.5* - 2.631%+0.5* - 2.734%= - 2.682% Var(Rp)= 0.5^2*0.00622 +0.5^2*0.00451 + 2*0.5*0.5*0.004823 = 0.00511 σ(monthly)= √(0.00511)= 7.147%, σ(yearly)= √(12)* 7.147%= 24.76% e) Weights : Qlogic = 15000/10000= 1.5(150%) , Mattel= - 5000/10000= - 0.5( - 50%) Rp=1.5* - 2.631%+ - 0.5* - 2.734%= - 2.579% Var(Rp)= 1.5^2*0.00622 +( - 0.5)^2*0.00451 + 2*1.5*0.5*0.004823 = 0.00784 σ(monthly)= √(0.00784)= 8.855%, σ(yearly)= √(12)* 8.855 % = 30.67 % f)  #### You've reached the end of your free preview.

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