In this paper we show how to use a statistical phenomenon commonly known as the 20-60-20 rule to construct an efficient fat-tail measurement framework. We construct a powerful statistical goodness-of-fit test that has a direct (financial) interpretation and can be used to assess the impact of fat-tails on central data normality assumption. In contrast to the Jarque-Bera test that is based on the third and fourth moment, our test relies on the conditional second moments. We show asymptotic normality of the proposed test statistic and perform the empirical study on market data to emphasise the usefulness of our approach.
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