Testing Serial Correlation and ARCH Effect of High-Dimensional Time-Series Data
Bibliographic Data
| ID | 19418168 |
|---|---|
| Authors | Shiqing Ling (0000-0002-4232-7744, Hong Kong University of Science and Technology), Ruey S Tsay (0000-0002-4949-4035, University of Chicago, corresponding author), Yaxing Yang (Xiamen University) |
| Year | 2021 |
| Volume | 39 |
| Issue | 1 |
| Pages | 136-147 |
| Publication date | 2021-01-02 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2019.1647844 |
| OpenAlex | W2963545191 |
| Language | EN |
| References cited | 20 |
This article proposes several tests for detecting serial correlation and ARCH effect in high-dimensional data. The dimension of data p=p(n) may go to infinity when the sample size n→∞. It is shown that the sample autocorrelations and the sample rank autocorrelations (Spearman’s rank correlation) of the L1-norm of data are asymptotically normal. Two portmanteau tests based, respectively, on the norm and its rank are shown to be asymptotically χ2-distributed, and the corresponding weighted portmanteau tests are shown to be asymptotically distributed as a linear combination of independent χ2 random variables. These tests are dimension-free, that is, independent of p, and the norm rank-based portmanteau test and its weighted counterpart can be used for heavy-tailed time series. We further discuss two standardized norm-based tests. Simulation results show that the proposed test statistics have satisfactory sizes and are powerful even for the case of small n and large p. We apply the tests to two real datasets. Supplementary materials for this article are available online
Autocorrelation · Combinatorics · Correlation · Rank correlation · Sample size determination · Spearman's rank correlation coefficient · Statistics · Complex Systems and Time Series Analysis · Financial Risk and Volatility Modeling · Mathematics · Statistical Methods and Inference · Applied Mathematics
| Citation velocity | historical |
|---|---|
| Highly cited | No |