Publication Type
Journal Article
Version
submittedVersion
Publication Date
7-2023
Abstract
The log realized volatility (RV) is often modeled as an autoregressive fractionally integrated moving average model ARFIMA(1,d,01,d,0). Two conflicting empirical results have been found in the literature. One stream shows that log RV has a long memory (i.e., the fractional parameter d > 0). The other stream suggests that the autoregressive coefficient α is near unity with antipersistent errors (i.e., d α close to 0 and d close to 0.5) from Model 2Model 2 (ARFIMA(1,d,01,d,0) with α close to unity and d close to –0.5). An intuitive explanation is given. For the 10 financial assets considered, despite that no definitive conclusions can be drawn regarding the data-generating process, we find that the frequency domain maximum likelihood (or Whittle) method can generate the most accurate out-of-sample forecasts.
Keywords
long memory, fractional integration, roughness, short-run dynamics, realized volatility
Discipline
Econometrics
Research Areas
Econometrics
Publication
Management Science
Volume
69
Issue
7
First Page
3861
Last Page
3883
ISSN
0025-1909
Identifier
10.1287/mnsc.2022.4552
Publisher
Institute for Operations Research and Management Sciences
Citation
SHI, Shuping and Jun YU.
Volatility puzzle: Long memory or anti-persistency. (2023). Management Science. 69, (7), 3861-3883.
Available at: https://ink.library.smu.edu.sg/soe_research/2638
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/10.1287/mnsc.2022.4552