rmgarch

R package rmgarch: Multivariate GARCH Models. Feasible multivariate GARCH models including DCC, GO-GARCH and Copula-GARCH.

Keywords for this software

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References in zbMATH (referenced in 13 articles )

Showing results 1 to 13 of 13.
Sorted by year (citations)

  1. Nagler, Thomas; Krüger, Daniel; Min, Aleksey: Stationary vine copula models for multivariate time series (2022)
  2. Regis, Marta; Serra, Paulo; van den Heuvel, Edwin R.: Random autoregressive models: a structured overview (2022)
  3. Klaus Nordhausen, Markus Matilainen, Jari Miettinen, Joni Virta, Sara Taskinen: Dimension Reduction for Time Series in a Blind Source Separation Context Using R (2021) not zbMATH
  4. Philippe Rast; Stephen Martin: bmgarch: An R-Package for Bayesian Multivariate GARCH models (2021) not zbMATH
  5. Kreuzer, Alexander; Czado, Claudia: Efficient Bayesian inference for nonlinear state space models with univariate autoregressive state equation (2020)
  6. David Ardia; Kris Boudt; Leopoldo Catania: Generalized Autoregressive Score Models in R: The GAS Package (2019) not zbMATH
  7. Ardia, David; Bolliger, Guido; Boudt, Kris; Gagnon-Fleury, Jean-Philippe: The impact of covariance misspecification in risk-based portfolios (2017)
  8. Isogai, Takashi: Analysis of dynamic correlation of Japanese stock returns with network clustering (2017)
  9. Fengler, Matthias R.; Okhrin, Ostap: Managing risk with a realized copula parameter (2016)
  10. Kim, Jong-Min; Jung, Hojin: Linear time-varying regression with copula-DCC-GARCH models for volatility (2016)
  11. Okhrin, Ostap: Lévy copulae for financial returns (2016)
  12. Ranković, Vladimir; Drenovak, Mikica; Urosevic, Branko; Jelic, Ranko: Mean-univariate GARCH VaR portfolio optimization: actual portfolio approach (2016)
  13. Ku, Yu-Cheng; Bloomfield, Peter; Ghosh, Sujit K.: A flexible observed factor model with separate dynamics for the factor volatilities and their correlation matrix (2014)