Jackknife Empirical Likelihood Ratio Test for Cauchy Distribution

Avhad Ganesh Vishnu, Ananya Lahiri, Sudheesh K. Kattumannil
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Abstract

Heavy-tailed distributions, such as the Cauchy distribution, are acknowledged for providing more accurate models for financial returns, as the normal distribution is deemed insufficient for capturing the significant fluctuations observed in real-world assets. Data sets characterized by outlier sensitivity are critically important in diverse areas, including finance, economics, telecommunications, and signal processing. This article addresses a goodness-of-fit test for the Cauchy distribution. The proposed test utilizes empirical likelihood methods, including the jackknife empirical likelihood (JEL) and adjusted jackknife empirical likelihood (AJEL). Extensive Monte Carlo simulation studies are conducted to evaluate the finite sample performance of the proposed test. The application of the proposed test is illustrated through the analysing two real data sets.
考奇分布的积弱经验似然比检验
重尾分布(如考奇分布)被认为能为金融回报提供更准确的模型,因为正态分布被认为不足以捕捉现实世界资产中的显著波动。以离群点敏感性为特征的数据集在金融、经济、电信和信号处理等多个领域都至关重要。本文探讨了考奇分布的拟合优度检验。所提出的检验利用了经验似然法,包括千分经验似然法(JEL)和调整千分经验似然法(AJEL)。为了评估所提出检验的有限样本性能,我们进行了广泛的蒙特卡洛模拟研究。通过分析两个真实数据集,说明了拟议检验的应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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