Testing serial correlation in a general <i>d</i>-factor model with possible infinite variance

Fan, Yawen, Liu, Xiaohui, Luo, Ting, Rao, Yao ORCID: 0000-0003-1341-3456 and Li, Hanqing (2023) Testing serial correlation in a general <i>d</i>-factor model with possible infinite variance. JOURNAL OF APPLIED STATISTICS, ahead- (ahead-). pp. 1-20.

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Abstract

It is well-known that the presence of serial correlation may result in an inefficient or even biased estimation in time series analysis. In this paper, we consider testing serial correlation in a general d-factor model when the model errors follow the GARCH process, which is frequently used in modeling financial data. Two empirical likelihood-based testing statistics are suggested as a way to deal with problems that might come up with infinite variance. Both statistics are shown to be chi-squared distributed asymptotically under mild conditions. Simulations confirm the excellent finite-sample performance of both tests. Finally, to emphasize the importance of using our tests, we explore the impact of the exchange rate on the stock return using both monthly and daily data from eight countries.

Item Type:	Article
Uncontrolled Keywords:	Empirical likelihood, weighted empirical likelihood, GARCH process, infinite variance, serial correlation
Divisions:	Faculty of Humanities and Social Sciences > School of Management
Depositing User:	Symplectic Admin
Date Deposited:	05 Jun 2023 07:49
Last Modified:	17 Mar 2024 16:50
DOI:	10.1080/02664763.2023.2231175
Related URLs:	Author Publisher
URI:	https://livrepository.liverpool.ac.uk/id/eprint/3170815