DCC- and DECO-HEAVY : multivariate GARCH models based on realized variances and correlations
Year of publication: |
2023
|
---|---|
Authors: | Bauwens, Luc ; Xu, Yongdeng |
Published in: |
International journal of forecasting. - Amsterdam [u.a.] : Elsevier, ISSN 0169-2070, ZDB-ID 283943-X. - Vol. 39.2023, 2, p. 938-955
|
Subject: | Correlation forecasting | Dynamic conditional correlation | Equicorrelation | High-frequency data | Multivariate volatility | Volatilität | Volatility | ARCH-Modell | ARCH model | Korrelation | Correlation | Prognoseverfahren | Forecasting model | Multivariate Analyse | Multivariate analysis | Schätztheorie | Estimation theory | Varianzanalyse | Analysis of variance | Zeitreihenanalyse | Time series analysis | Kapitaleinkommen | Capital income | Schätzung | Estimation | Börsenkurs | Share price |
-
Forecasting covariance matrices : a mixed approach
Halbleib, Roxana, (2016)
-
Realized variances vs. correlations : unlocking the gains in multivariate volatility forecasting
Capera Romero, Laura, (2024)
-
Realized Wishart-GARCH : a score-driven multi-Asset volatility model
Hansen, Peter Reinhard, (2016)
- More ...
-
The contribution of realized covariance models to the economic value of volatility timing
Bauwens, Luc, (2023)
-
DCC-HEAVY : a multivariate GARCH model based on realized variances and correlations
Bauwens, Luc, (2019)
-
The contribution of realized covariance models to the economic value of volatility timing
Bauwens, Luc, (2023)
- More ...