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stats_solution

# stats_solution - Statistics minicase(solution Sample...

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Statistics minicase (solution)

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Sample statistics Variable | Obs Mean Std. Dev. -------------+------------------------------------ sp500_return | 212 .0080417 .0393682 sp500_excess | 212 .0045301 .0393159 ibm_return | 212 .0126155 .0877935 ibm_excess | 212 .0091039 .0876801 ge_return | 212 .0133744 .0614989 ge_excess | 212 .0098628 .0614041 We can use these to compute Sharpe ratios: SP500 Sharpe ratio = .00453/.0394 = .115 IBM Sharpe ratio = .00910/.0878 = .104 GE Sharpe ratio = .00986/.0615 = .160 2
Figure — log prices 1990 1995 2000 2005 6.0 6.5 7.0 SP500 Time Log price 1990 1995 2000 2005 2.5 3.0 3.5 4.0 4.5 IBM Time Log price 3

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Figure — returns 1990 1995 2000 2005 -0.15 -0.05 0.00 0.05 0.10 SP500 Time Returns 1990 1995 2000 2005 -0.2 0.0 0.1 0.2 0.3 IBM Time Returns 4
Figure — scatterplot -0.15 -0.10 -0.05 0.00 0.05 0.10 -0.2 -0.1 0.0 0.1 0.2 0.3 SP500 vs IBM excess returns SP500 IBM Correlation = 0.55 5

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CAPM regression ------------------------------------------------------- ibm_excess | Coef. Std. Err. t P>|t| -------------+----------------------------------------- sp500_excess | 1.233578 .1282078 9.62 0.000 _cons | .0035157 .0050621 0.69 0.488 ------------------------------------------------------- Number of obs = 212 R-squared = 0.3060 Adj R-squared = 0.3027 Root MSE = .07322 Source | SS df MS -------------+------------------------------ Model | .496309765 1 .496309765

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