Testing conditional heteroscedasticity with systematic sampling of time series

It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect te...

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Bibliographic Details
Published in:Communications in statistics. Theory and methods 2023-08, Vol.52 (15), p.5427-5450
Main Author: Teles, Paulo
Format: Article
Language:English
Subjects:
Conditional heteroscedasticity
Empirical analysis
Lagrange multiplier test
portmanteau test
power loss
Sampling
Statistical tests
systematic sampling
Time series
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Summary:It is well known that conditional heteroscedasticity is exhibited by many economic and financial time series such as stock prices or returns. Empirical analysis is often based on a subseries obtained through systematically sampling from an underlying time series and we analyze how that can affect testing for heteroscedasticity. The results show the distribution of the test statistics is changed by systematic sampling, causing a serious power loss that increases with the sampling interval. Consequently, the tests often fail to reject the hypothesis of no conditional heteroscedasticity, leading to the wrong decision and missing the true nature of the data-generating process.
ISSN:0361-0926
1532-415X
DOI:10.1080/03610926.2021.2008976