mfGARCH: Mixed-Frequency GARCH Models

Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, <doi:10.1162/REST_a_00300>) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, <doi:10.1002/jae.2742>). 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.

Version: 0.2.1
Depends: R (≥ 3.3.0)
Imports: Rcpp, graphics, stats, numDeriv, zoo, maxLik
LinkingTo: Rcpp
Suggests: testthat, dplyr, ggplot2, covr, rmarkdown
Published: 2021-06-17
Author: Onno Kleen ORCID iD [aut, cre]
Maintainer: Onno Kleen <r at onnokleen.de>
BugReports: https://github.com/onnokleen/mfGARCH/issues
License: MIT + file LICENSE
URL: https://github.com/onnokleen/mfGARCH/
NeedsCompilation: yes
Citation: mfGARCH citation info
Materials: NEWS
CRAN checks: mfGARCH results

Downloads:

Reference manual: mfGARCH.pdf
Package source: mfGARCH_0.2.1.tar.gz
Windows binaries: r-devel: mfGARCH_0.2.1.zip, r-devel-UCRT: mfGARCH_0.2.1.zip, r-release: mfGARCH_0.2.1.zip, r-oldrel: mfGARCH_0.2.1.zip
macOS binaries: r-release (arm64): mfGARCH_0.2.1.tgz, r-release (x86_64): mfGARCH_0.2.1.tgz, r-oldrel: mfGARCH_0.2.1.tgz
Old sources: mfGARCH archive

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