1. Consider the Capital Asset Pricing Model that relates the excess return of a security to the market portfolio. The relevant data set (CAPM.xls) contains prices of different US stocks, S&P 500 index and the series of the US Treasury bills.

a) Estimate CAPM betas for each of the stock. [10 marks]

b) Examine the sizes and signs of the parameters in the regressions. Do these make sense? [10 marks]

c) Explain which of the stocks would you classify as defensive stocks and which as aggressive? [10 marks]

d) Do you agree that the CAPM provides some reasonable explanation of the variability of the returns to each of the stock? [10 marks]

e) Check if the CAMP model for FORD suffers from heteroscedasticity. Use a formal test to check that. [10 marks]

f) Can you use the standard errors in this regression if residuals are heteroscedastic? [10 marks]

g) For the model estimated in part e) formally test if the residuals are correlated? [10 marks]

h) How would you solve the problem of autocorrelation? [10 marks]

i) Suppose you want to regress GE log returns on FORD and S&P log returns. Would you expect to have multi-collinearity? Check it. [20 marks]

a) Estimate CAPM betas for each of the stock. [10 marks]

b) Examine the sizes and signs of the parameters in the regressions. Do these make sense? [10 marks]

c) Explain which of the stocks would you classify as defensive stocks and which as aggressive? [10 marks]

d) Do you agree that the CAPM provides some reasonable explanation of the variability of the returns to each of the stock? [10 marks]

e) Check if the CAMP model for FORD suffers from heteroscedasticity. Use a formal test to check that. [10 marks]

f) Can you use the standard errors in this regression if residuals are heteroscedastic? [10 marks]

g) For the model estimated in part e) formally test if the residuals are correlated? [10 marks]

h) How would you solve the problem of autocorrelation? [10 marks]

i) Suppose you want to regress GE log returns on FORD and S&P log returns. Would you expect to have multi-collinearity? Check it. [20 marks]

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