The functions contained in the package gogarch facilitate the estimation of generalized orthogonal GARCH (GO-GARCH) models. The following estimation methods are implemented: fast ICA, Methods of Moments, non-linear Least-Squares and Maximum Likelihood. Aside of these estimation routines, methods for plotting, updating, predicting, retrieving the conditional correlations and covariances, residuals and coefficients are implemented as well as summarizing GO-GARCH models. The package is purely written in R and S4-classes are employed. The package does depend on the CRAN packges fGarch fastICA. For more information and references see the package’s documentation.
Keywords for this software
References in zbMATH (referenced in 10 articles )
Showing results 1 to 10 of 10.
- Dedduwakumara, Dilanka S.; Prendergast, Luke A.; Staudte, Robert G.: An efficient estimator of the parameters of the generalized lambda distribution (2021)
- 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
- Dedduwakumara, Dilanka S.; Prendergast, Luke A.; Staudte, Robert G.: A simple and efficient method for finding the closest generalized lambda distribution to a specific model (2019)
- Pfaff, Bernhard: Financial risk modelling and portfolio optimization with R (2016)
- Ranković, Vladimir; Drenovak, Mikica; Urosevic, Branko; Jelic, Ranko: Mean-univariate GARCH VaR portfolio optimization: actual portfolio approach (2016)
- Vijverberg, Chu-Ping C.; Vijverberg, Wim P. M.; Taşpınar, Süleyman: Linking Tukey’s legacy to financial risk measurement (2016)
- Çetinkaya, Elçin; Thiele, Aurélie: Data-driven portfolio management with quantile constraints (2015)
- Grebenkov, Denis S.; Serror, Jeremy: Optimal allocation of trend following strategies (2015)
- Hayter, A. J.: Confidence bounds on the coefficient of variation of a normal distribution with applications to win-probabilities (2015)
- Pfaff, Bernhard: Financial risk modelling and portfolio optimization with R (2013)