Bayesian semiparametric modeling of realized covariance matrices
By Xin Jin, John M. Maheu. 2016
54 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Finance, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/abacd6769089da484716f94a7981c3f864f03a80
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Pawel Janus
- Dick Van Dijk
- André Lucas
- A. Opschoor
- Ruben Loaiza-Maya
- Gary Koop
- Gael M. Martin
- John M. Maheu
In Finance
- Pawel Janus
- Dick Van Dijk
- A. Opschoor
- André Lucas
- Ruben Loaiza-Maya
- Gary Koop
- Gael M. Martin
- John M. Maheu
In Statistics and Probability
- Pawel Janus
- Dick Van Dijk
- André Lucas
- A. Opschoor
- Ruben Loaiza-Maya
- Gary Koop
- Gael M. Martin
- John M. Maheu
Citations in the corpus
- Bayesian forecasting in economics and finance: A modern review (2022, methodology): “The IHMM structure has been used to model the univariate GARCH distribution (Dufays, 2016), and the multivariate GARCH distribution (Li, 2022); and to provide a nonparametric model for realized measures, including realized covariance matrices (Jin and Maheu, 2016; Liu and Maheu, 2018; Jin et al., 20”
- New HEAVY Models for Fat-Tailed Realized Covariances and Returns (2017, methodology): “For the IW-A model, we follow Jin and Maheu (2016) and a Student’s t distribution for the returns with νIW − k + 1 degrees of freedom, with νIW the degrees estimated for the inverseWishart.”