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Question 3 (Testing the CAPM - 5 marks) Your client from Question 2 is quite skeptical about the profitability of the strategy that you've...
Question 3 (Testing the CAPM - 5 marks)
Your client from Question 2 is quite skeptical about the profitability of the strategy that you've recommended. The client argues that if the market is efficient and the CAPM holds, then the only way to earn a higher risk premium is to take on more systematic risk (measured by beta).
(a) Based on the adjusted closing prices for BHP, CBA, TLS, WOW, FLT, MYR, COH, JBH, WES, QBE and ASX200, estimate the stock betas βk and the annualized expected returns E[rk]. Plot the SML predicted by the CAPM, also indicate the positions of the 10 individual stocks and of ASX200 (you can treat ASX200 as a proxy for the market portfolio and assume the risk-free rate is 2.3% p.a.). Are the positions of the individual stock consistent with the CAPM prediction? Why or why not? [1.5 marks]
(b) Based on the stock beta estimates and expected returns of the 10 stocks from Part (a), run the following linear regression
E[rk]=α+γβk +εk,
where E[rk] is the dependent variable and βk is the independent variable. Essentially, you are trying to explain the variation in expected stock returns by variation in the stock betas. Report the estimates for α and γ and their p values. Plot the best-fitted line together with the SML from Part (a), you should also indicate the positions of the 10 stocks. Finally, comment on the regression results in terms of the observed risk-return relationship. [2 marks]
(c) Plot the CML and indicate the positions of the 10 individual stocks in the (σ,E[r]) space. Are the positions of the individual stocks consistent with CAPM, why or why not? Finally, explain to your client why the market may not be perfectly efficient and therefore arbitrage opportunities may exist
The positions on the individual stock are not consistent with the CAPM predictions. Most SMLlines are usually upward sloping. However, in this case the SML line is downward sloping.Linear...