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 datato emphasise the usefulness of our approach .
In this paper we show how to construct an efficient fat-tail measurement framework based on conditional second moments . 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. We derive asymptotic distributions for conditional trimmed mean and variance estimators, and use this to show asymptotic normality of the proposed test statistic. Using financial empirical data, we compare our framework to various statistical tests including the Jarque-Bera test that is based on third and fourth moments. We show that our methodology outperforms existing methods on market-stock data .
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use a statistical phenomenon commonly known as the 20-60-20 rule to
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based on conditional second moments
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(financial )
financial
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In contrast to the
We derive asymptotic distributions for conditional trimmed mean and variance estimators, and use this to show asymptotic normality of the proposed test statistic. Using financial empirical data, we compare our framework to various statistical tests including the
D
the
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moment, our test relies on the conditional second
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asymptotic normality of the proposed test statistic and perform the empirical study on market datato emphasise the usefulness of our approach
that our methodology outperforms existing methods on market-stock data