Empirical likelihood test for the application of swqmele in fitting an arma-garch model
Empirical likelihood test for the application of swqmele in fitting an arma-garch model
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DOI：
10.1111/jtsa.12563
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发表时间：
2021
影响因子：
0.9
通讯作者：
Zhang Rongmao
中科院分区：
文献类型：
--
作者：
Zhou Mo;Peng Liang;Zhang Rongmao
拟合 ARMA-GARCH 模型已成为金融计量经济学中的常见做法。由于准最大似然估计（QMLE）的渐近正态性要求误差和序列本身都有有限的四阶矩，因此提出了自加权准最大指数似然估计（SWQMELE）来减少矩约束，但要求误差具有零中值而不是零均值。由于将零均值改为零中位数会破坏ARMA-GARCH结构，并对偏态数据产生严重影响，因此本文在SWQMELE的应用中提出了一种有效的误差零均值经验似然检验，以确保模型仍然关注条件均值。在将测试应用于美国房价指数和财务回报以研究联动性之前，模拟研究证实了良好的有限样本性能。
Fitting an ARMA‐GARCH model has become a common practice in financial econometrics. Because the asymptotic normality of the quasi maximum likelihood estimation (QMLE) requires finite fourth moment for both errors and the sequence itself, self‐weighted quasi maximum exponential likelihood estimation (SWQMELE) has been proposed to reduce the moment constraints but requires the errors to have zero median instead of zero mean. Because changing zero mean to zero median destroys the ARMA‐GARCH structure and has a serious effect on skewed data, this article proposes an efficient empirical likelihood test for zero mean of errors in the application of SWQMELE to ensure that the model still concerns conditional mean. A simulation study confirms the good finite sample performance before applying the test to the US housing price indexes and financial returns for the study of comovement.