Package: mfGARCH 0.2.2

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.

Authors:Onno Kleen [aut, cre]

mfGARCH_0.2.2.tar.gz
mfGARCH_0.2.2.zip(r-4.7)mfGARCH_0.2.2.zip(r-4.6)mfGARCH_0.2.2.zip(r-4.5)
mfGARCH_0.2.2.tgz(r-4.6-x86_64)mfGARCH_0.2.2.tgz(r-4.6-arm64)mfGARCH_0.2.2.tgz(r-4.5-x86_64)mfGARCH_0.2.2.tgz(r-4.5-arm64)
mfGARCH_0.2.2.tar.gz(r-4.7-arm64)mfGARCH_0.2.2.tar.gz(r-4.7-x86_64)mfGARCH_0.2.2.tar.gz(r-4.6-arm64)mfGARCH_0.2.2.tar.gz(r-4.6-x86_64)
mfGARCH_0.2.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
mfGARCH/json (API)

# Install 'mfGARCH' in R:
install.packages('mfGARCH', repos = c('https://onnokleen.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/onnokleen/mfgarch/issues

Uses libs:
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

cpp

5.16 score 76 stars 38 scripts 625 downloads 5 exports 9 dependencies

Last updated from:cb76089ae4. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK229
linux-devel-x86_64OK200
source / vignettesOK196
linux-release-arm64OK191
linux-release-x86_64OK207
macos-release-arm64OK212
macos-release-x86_64OK379
macos-oldrel-arm64OK170
macos-oldrel-x86_64OK354
windows-develOK170
windows-releaseOK187
windows-oldrelOK172
wasm-releaseOK125

Exports:fit_mfgarchplot_weighting_schemesimulate_mfgarchsimulate_mfgarch_diffusionsimulate_mfgarch_rv_dependent

Dependencies:digestgenericslatticemaxLikmiscToolsnumDerivRcppsandwichzoo