Positive Kurtosis Graph. So the further the tails are from the mean the higher the risk of getting an extremely low return and the higher the chance of getting an extremely high return. A gaussian distribution has a kurtosis of 0.
Kurtosis quantifies whether the tails of the data distribution matches the gaussian distribution. For example data that follow a t distribution have a positive kurtosis value. Skewness and kurtosis skewness.
If z g2 2 the population very likely has positive excess kurtosis kurtosis 3 leptokurtic though you don t know how much.
If we get low kurtosis too good to be true then also we need to investigate and trim the dataset of unwanted results. The solid line shows the normal distribution and the dotted line shows a distribution that has a positive kurtosis value. Let s see the main three types of kurtosis. This distribution has fatter tails and a sharper peak.
