Forecasting intraday volatility in the US equity market. Multiplicative component GARCH
By Robert F. Engle, M. Sokalska. 2012
185 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Finance
View Paper: https://www.semanticscholar.org/paper/ef12d153d7df3923ad2987933cf6636c0368abfc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- V. Todorov
- Torben G. Andersen
- O. Efimova
- Apostolos Serletis
- Martin Thyrsgaard
- Tim Bollerslev
- George Filis
- Stavros Degiannakis *
In Finance
- V. Todorov
- Torben G. Andersen
- Tim Bollerslev
- Martin Thyrsgaard
- O. Efimova
- Apostolos Serletis
- George Tauchen
- Andrew J. Patton
Citations in the corpus
- Time-Varying Periodicity in Intraday Volatility (2019, background): “The (constant) intraday periodicity is further explicitly modeled or accounted for in papers estimating volatility models and detecting jumps, such as [9], [12], [19] and [30].”
- Is the diurnal pattern sufficient to explain intraday variation in volatility? A nonparametric assessment (2017, unknown): “…the deterministic part is a smooth periodic function that is allowed to change within the day but is otherwise time-invariant (thus capturing the diurnal effect), see, e.g., Andersen and Bollerslev (1997, 1998); Boudt, Croux, and Laurent (2011); Engle and Sokalska (2012) and references therein.”
- Energy markets volatility modelling using GARCH (2014, methodology): “(1996)], nonparametric GARCH [see Bühlmann and McNeil (2002)], and multiplicative component GARCH [see Engle and Sokalska (2012)], simple models of the GARCH(1,1) type remain very useful because they converge much faster to a local maximum in quasi-maximum likelihood estimation, while delivering…”