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.
NANKERVIS John C;
KOUGOULIS Periklis;
COAKLEY Jerry;
2015-12-02
WILEY-BLACKWELL
JRC94131
0143-9782,
http://onlinelibrary.wiley.com/doi/10.1111/jtsa.12124/abstract?systemMessage=Wiley,
Online,
Library,
will,
have,
be,
unavailable,
on,
Saturday,
5th,
December,
from,
10%3A00-14%3A00,
GMT,
%2F,
05%3A00-09%3A00,
EST,
%2F,
18%3A00-22%3A00,
SGT,
for,
essential,
maintenance.,
Apologi,
https://publications.jrc.ec.europa.eu/repository/handle/JRC94131,
10.1111/jtsa.12124,
| Name | Country | City | Type |
|---|
This document is only visible at the Commission level.
You are not authorized to publish or distribute it outside the European Commission.
This is a public document. You can share this publication.
Datasets
| ID | Title | Public URL |
|---|
Dataset collections
| ID | Acronym | Title | Public URL |
|---|
Scripts / source codes
| Description | Public URL |
|---|
Additional supporting files
| File name | Description | File type |
|---|