Mixed-Frequency GARCH Models

Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, ) and related statistical inference, accompanying the paper "Two are better than one: volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2018, ). The GARCH-MIDAS model decomposes the conditional variance of (daily) stock returns into a short- and long-term component, where the latter may depend on an exogenous covariate sampled at a lower frequency.


News

v0.1.7

  • Improved documentation for simulation functions
  • Bugfix for simulating when using student-t
  • Added K = K.two = 1, i.e. two covariates each with one lag
  • Added K > 0 and K.two = 1 for unrestricted beta-weighting scheme

v0.1.6

  • Hessian matrix more accurate

v0.1.5

  • Support for two covariates

v0.1.4

  • Predict function bugfix
  • Better error messages
  • Bugfix for calculating the variance ratio

v0.1.3

  • New generic plot function

v0.1.2

  • First release on CRAN

Reference manual

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install.packages("mfGARCH")

0.1.7 by Onno Kleen, 10 months ago


https://github.com/onnokleen/mfGARCH/


Report a bug at https://github.com/onnokleen/mfGARCH/issues


Browse source code at https://github.com/cran/mfGARCH


Authors: Onno Kleen [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports Rcpp, graphics, stats, numDeriv, zoo

Suggests testthat, dplyr, ggplot2, covr, rmarkdown

Linking to Rcpp


See at CRAN