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Generalized Variance‐Ratio Tests in the Presence of Statistical Dependence
Author(s) -
Nankervis John C.,
Kougoulis Periklis,
Coakley Jerry
Publication year - 2015
Publication title -
journal of time series analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.576
H-Index - 54
eISSN - 1467-9892
pISSN - 0143-9782
DOI - 10.1111/jtsa.12124
Subject(s) - mathematics , statistics , estimator , heteroscedasticity , autoregressive model , test statistic , martingale difference sequence , conditional variance , statistical hypothesis testing , sample size determination , monte carlo method , null hypothesis , autocovariance , covariance matrix , econometrics , martingale (probability theory) , autoregressive conditional heteroskedasticity , mathematical analysis , volatility (finance) , fourier transform
This article extends and generalizes the variance‐ratio (VR) statistic by employing an estimator of the asymptotic covariance matrix of the sample autocorrelations. The estimator is consistent under the null for general classes of innovations exhibiting statistical dependence including exponential generalized autoregressive conditional heteroskedasticity and non‐martingale difference sequence processes. Monte Carlo experiments show that our generalized test statistics have good finite sample size and superior power properties to other recently developed VR versions. In an application to two major US stock indices, our new generalized VR tests provide stronger rejections of the null than do competing VR tests.