英文标题：
《New fat-tail normality test based on conditional second moments with
applications to finance》
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作者：
Damian Jelito, Marcin Pitera
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最新提交年份：
2020
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英文摘要：
In this paper we introduce an efficient fat-tail measurement framework that is based on the conditional second moments. We construct a goodness-of-fit statistic that has a direct interpretation and can be used to assess the impact of fat-tails on central data conditional dispersion. Next, we show how to use this framework to construct a powerful normality test. In particular, we compare our methodology to various popular normality tests, including the Jarque--Bera test that is based on third and fourth moments, and show that in many cases our framework outperforms all others, both on simulated and market stock data. Finally, we derive asymptotic distributions for conditional mean and variance estimators, and use this to show asymptotic normality of the proposed test statistic.
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中文摘要：
本文介绍了一种基于条件二阶矩的有效厚尾度量框架。我们构建了一个拟合优度统计量，该统计量具有直接的解释，可用于评估胖尾对中心数据条件离散度的影响。接下来，我们将展示如何使用该框架构建强大的正态性测试。特别是，我们将我们的方法与各种流行的正态性测试进行了比较，包括基于三阶矩和四阶矩的Jarque-Bera测试，并表明在许多情况下，我们的框架在模拟和市场股票数据方面都优于所有其他框架。最后，我们推导了条件均值和方差估计的渐近分布，并用它来证明所提出的检验统计量的渐近正态性。
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分类信息：

一级分类：Quantitative Finance 数量金融学
二级分类：Statistical Finance 统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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