Could someone please explain/compare/contrast these two concepts? I took the time to do the math on them over the last two hours and now feel like jumping out of a building. Thanks!
Skewness is how disproportionate the distribution is around the mean. Basically, it measures the shift away from a normal distribution. It’s measures how lopsided your graph is whether it’s to the left or right. If your mean=median=mode your distribution and chart would look perfectly normal. A normal chart would also exhibit kurtosis of 3, which is normal and simply measures the “peakness” of the distribution. You won’t have to calculate kurtosis on the exam.
Variance, skewness and kurtosis are all central moments about the mean of a distribution. Variance is the second central moment; skewness the third and kurtosis the fourth. This curriculum might skip over the concept of a “moment” (I’m not sure because I don’t have the CFA texts yet), but I find that it’s first helpful to know what a concept is before we jump right to the graphs. So in the end, you will learn about skewness and kurtosis in relation to symmetry and flatness – the graphic concepts. But if you start with the idea of central moments and variance, and then realize that the exponents are increasing (skewness, n = 3, kurtosis = 4), your brain might understand better.
My brain understands the concepts a lot better thanks to the help of both of you. I appreciate it.