Publication Type
Journal Article
Version
acceptedVersion
Publication Date
4-2019
Abstract
We propose a test for the rank of a cross-section of processes at a set of jump events. The jump events are either specific known times or are random and associated with jumps of some process. The test is formed from discretely sampled data on a fixed time interval with asymptotically shrinking mesh. In the first step, we form nonparametric estimates of the jump events via thresholding techniques. We then compute the eigenvalues of the outer product of the cross-section of increments at the identified jump events. The test for rank r is based on the asymptotic behavior of the sum of the squared eigenvalues excluding the largest r. A simple resampling method is proposed for feasible testing. The test is applied to financial data spanning the period 2007–2015 at the times of stock market jumps. We find support for a one-factor model of both industry portfolio and Dow 30 stock returns at market jump times. This stands in contrast with earlier evidence for higher-dimensional factor structure of stock returns during “normal” (nonjump) times. We identify the latent factor driving the stocks and portfolios as the size of the market jump.
Keywords
Factor model, High-frequency data, jumps, Rank test, Semimartingale
Discipline
Econometrics
Research Areas
Econometrics
Publication
Journal of Business and Economic Statistics
Volume
37
Issue
2
First Page
312
Last Page
321
ISSN
0735-0015
Identifier
10.1080/07350015.2017.1328362
Publisher
Taylor & Francis
Citation
LI, Jia; TODOROV, Viktor; TAUCHEN, George; and LIN, Huidi..
Rank tests at jump events. (2019). Journal of Business and Economic Statistics. 37, (2), 312-321.
Available at: https://ink.library.smu.edu.sg/soe_research/2586
Copyright Owner and License
Authors
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
https://doi.org/10.1080/07350015.2017.1328362