Question: a) Daily Lower level Upper level Comments/calculation: -0. 178669955 0. 178669955 Kurtosis estimate from data 5. 0587 Standard error 0. 069337525 Z value 2. 576815

 a) Daily Lower level Upper level Comments/calculation: -0. 178669955 0. 178669955Kurtosis estimate from data 5. 0587 Standard error 0. 069337525 Z value2. 576815 Monthly Lower level Upper level -0. 822846967 0. 822846967 Kurtosisestimate from data 1. 8152 Standard error 0. 31688864 Z value 2.

596643941 b ) Daily Reject/Not reject Comments/calculation: 0. 995014 Monthly Reject/Not reject4.(2 points) (Kurtosis) Under the assumption that returns are Normally distributed, thesample excess kurtosis, from a sample of size n, will be normallydistributed with mean 0 and variance 24 (standard error = V(24)). a)

a) Daily Lower level Upper level Comments/calculation: -0. 178669955 0. 178669955 Kurtosis estimate from data 5. 0587 Standard error 0. 069337525 Z value 2. 576815 Monthly Lower level Upper level -0. 822846967 0. 822846967 Kurtosis estimate from data 1. 8152 Standard error 0. 31688864 Z value 2. 596643941 b ) Daily Reject/Not reject Comments/calculation: 0. 995014 Monthly Reject/Not reject4. (2 points) (Kurtosis) Under the assumption that returns are Normally distributed, the sample excess kurtosis, from a sample of size n, will be normally distributed with mean 0 and variance 24 (standard error = V(24)). a) (1 point) Using this information, construct a 99% confidence interval for the sample excess kurtosis for your daily and monthly returns of your data set from YELLOW TABLE. b) (1 point) Does your estimate of excess kurtosis appear in this confidence interval? What do you conclude about your assumption that returns are normally distributed

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