The following data is taken from the first table in Campbell, Lo, and McKinley's Financial Econometric text (Princeton U. Press, 1996). The data cover the period 1962-1994. The particular data given below corresponds to MONTHLY data for a value-weighted portfolio of stocks. The notion of a value-weighted portfolio index is discussed in Bailey's text (pp. 26-28).1 They report the sample estimates: RA o = 0.96% per month 4.33% per month = -.029 2.43, S K where RA and o have the same meaning as in problem 1, s is the skewness parameter, and k is the excess kurtosis parameter. The latter two parameters are pure numbers. They do not have units of measurement, unlike the sample mean, RA. 1 The S&P 500 is a value-weigthted index. The DJIA is not a value weighted index. 1 Many financial economists would assume that the data is generated by a series of independent draws from a normal distribution, with mean 0.96% per month and standard deviation 4.33% per month (square it to find the variance per month-squared). Denote this normal distribution by N (0.0096; 0.002) with the variance approximated as (0.0433)2 = .002 after rounding. The normality assumption says that the only two parameters that matter in the description of the returns distribution are the mean and variance. Does the evidence presented in the above sample estimates of the parameters support the hypothesis that the returns data is, in fact, a sequence of independent draws from the normal distribution N (0.0096; 0.002)? Briefly explain your answer. Hint: What is the null hypothesis (that is, what is assumed about the data generating process)? What is the implication of the null hypothesis, if true, for the magnitudes of the skewness and excess kurtosis parameter values? You do not have sufficient information to construct a formal statistical test that the monthly returns distribution is a normal distribution. So, you basically need to briefly explain what the data suggests. The following data is taken from the first table in Campbell, Lo, and McKinley's Financial Econometric text (Princeton U. Press, 1996). The data cover the period 1962-1994. The particular data given below corresponds to MONTHLY data for a value-weighted portfolio of stocks. The notion of a value-weighted portfolio index is discussed in Bailey's text (pp. 26-28).1 They report the sample estimates: RA o = 0.96% per month 4.33% per month = -.029 2.43, S K where RA and o have the same meaning as in problem 1, s is the skewness parameter, and k is the excess kurtosis parameter. The latter two parameters are pure numbers. They do not have units of measurement, unlike the sample mean, RA. 1 The S&P 500 is a value-weigthted index. The DJIA is not a value weighted index. 1 Many financial economists would assume that the data is generated by a series of independent draws from a normal distribution, with mean 0.96% per month and standard deviation 4.33% per month (square it to find the variance per month-squared). Denote this normal distribution by N (0.0096; 0.002) with the variance approximated as (0.0433)2 = .002 after rounding. The normality assumption says that the only two parameters that matter in the description of the returns distribution are the mean and variance. Does the evidence presented in the above sample estimates of the parameters support the hypothesis that the returns data is, in fact, a sequence of independent draws from the normal distribution N (0.0096; 0.002)? Briefly explain your answer. Hint: What is the null hypothesis (that is, what is assumed about the data generating process)? What is the implication of the null hypothesis, if true, for the magnitudes of the skewness and excess kurtosis parameter values? You do not have sufficient information to construct a formal statistical test that the monthly returns distribution is a normal distribution. So, you basically need to briefly explain what the data suggests