Publication Type
Journal Article
Version
acceptedVersion
Publication Date
10-2025
Abstract
This paper explores implications of weak identification in common ‘long memory’ and recent ‘rough’ approaches to modeling volatility dynamics of financial assets. We unveil an asymptotic near-observational equivalence between a long memory model with weak autoregressive dynamics and a rough model with a near-unit autoregressive root. Standard methods struggle to distinguish them, and conventional asymptotics are invalid. We propose an identification-robust approach to construct confidence sets that reveal the uncertainty and aid inference. Empirical studies based on realized volatility and trading volume often fail to statistically reject either model, thereby providing evidence of their potential coexistence.
Keywords
Hypothesis testing, estimation, financial econometrics
Discipline
Econometrics
Research Areas
Econometrics
Publication
Review of Financial Studies
Volume
38
Issue
10
First Page
3117
Last Page
3148
ISSN
0893-9454
Identifier
10.1093/rfs/hhaf022
Publisher
Oxford University Press
Citation
LI, Jia; PHILLIPS, Peter C. B.; SHI, Shuping; and Jun YU.
Weak identification of long memory with implications for volatility modeling;. (2025). Review of Financial Studies. 38, (10), 3117-3148.
Available at: https://ink.library.smu.edu.sg/soe_research/2820
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/10.1093/rfs/hhaf022