volume 40 pages 100517

Modeling meaningful volatility events to classify monetary policy announcements

Publication typeJournal Article
Publication date2025-05-01
scimago Q1
wos Q1
SJR0.914
CiteScore11.3
Impact factor4.2
ISSN22145796
Abstract
Central Bank monetary policy interventions frequently have direct implications for financial market volatility. In this paper, we introduce an intradaily Asymmetric Multiplicative Error Model with Meaningful Volatility (MV) events (AMEM-MV), which decomposes realized variance into a base component and an MV component. A novel model-based classification of monetary announcements is developed based on their impact on the MV component of the variance. By focusing on the 30-minute window following each Federal Reserve communication, we isolate the specific impact of monetary announcements on the volatility of seven US tickers.
Found 

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
0
Share
Cite this
GOST |
Cite this
GOST Copy
Gallo G. M. et al. Modeling meaningful volatility events to classify monetary policy announcements // Big Data Research. 2025. Vol. 40. p. 100517.
GOST all authors (up to 50) Copy
Gallo G. M., Lacava D., Otranto E. Modeling meaningful volatility events to classify monetary policy announcements // Big Data Research. 2025. Vol. 40. p. 100517.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1016/j.bdr.2025.100517
UR - https://linkinghub.elsevier.com/retrieve/pii/S2214579625000127
TI - Modeling meaningful volatility events to classify monetary policy announcements
T2 - Big Data Research
AU - Gallo, Giampiero M
AU - Lacava, Demetrio
AU - Otranto, Edoardo
PY - 2025
DA - 2025/05/01
PB - Elsevier
SP - 100517
VL - 40
SN - 2214-5796
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2025_Gallo,
author = {Giampiero M Gallo and Demetrio Lacava and Edoardo Otranto},
title = {Modeling meaningful volatility events to classify monetary policy announcements},
journal = {Big Data Research},
year = {2025},
volume = {40},
publisher = {Elsevier},
month = {may},
url = {https://linkinghub.elsevier.com/retrieve/pii/S2214579625000127},
pages = {100517},
doi = {10.1016/j.bdr.2025.100517}
}