Package: rumidas 0.1.3

rumidas: Univariate GARCH-MIDAS, Double-Asymmetric GARCH-MIDAS and MEM-MIDAS

Adds the MIxing-Data Sampling (MIDAS, Ghysels et al. (2007) <doi:10.1080/07474930600972467>) components to a variety of GARCH and MEM (Engle (2002) <doi:10.1002/jae.683>, Engle and Gallo (2006) <doi:10.1016/j.jeconom.2005.01.018>, and Amendola et al. (2024) <doi:10.1016/j.seps.2023.101764>) models, with the aim of predicting the volatility with additional low-frequency (that is, MIDAS) terms. The estimation takes place through simple functions, which provide in-sample and (if present) and out-of-sample evaluations. 'rumidas' also offers a summary tool, which synthesizes the main information of the estimated model. There is also the possibility of generating one-step-ahead and multi-step-ahead forecasts.

Authors:Vincenzo Candila [aut, cre]

rumidas_0.1.3.tar.gz
rumidas_0.1.3.zip(r-4.7)rumidas_0.1.3.zip(r-4.6)rumidas_0.1.3.zip(r-4.5)
rumidas_0.1.3.tgz(r-4.6-any)rumidas_0.1.3.tgz(r-4.5-any)
rumidas_0.1.3.tar.gz(r-4.7-any)rumidas_0.1.3.tar.gz(r-4.6-any)
rumidas_0.1.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
rumidas/json (API)

# Install 'rumidas' in R:
install.packages('rumidas', repos = c('https://vincenzocandila.r-universe.dev', 'https://cloud.r-project.org'))
Datasets:
  • indpro - Monthly U.S. Industrial Production
  • rv5 - S&P 500 realized variance at 5-minutes
  • sp500 - S&P 500 daily log-returns
  • vix - VIX daily data

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.86 score 12 stars 2 packages 5 scripts 389 downloads 64 exports 23 dependencies

Last updated from:30ae373ce7. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK137
source / vignettesOK197
linux-release-x86_64OK145
macos-release-arm64OK156
macos-oldrel-arm64OK198
windows-develOK96
windows-releaseOK120
windows-oldrelOK104
wasm-releaseOK107

Exports:beta_functionDAGM_2M_cond_volDAGM_2M_cond_vol_no_skewDAGM_2M_loglikDAGM_2M_loglik_no_skewDAGM_2M_long_runDAGM_2M_long_run_no_skewDAGM_cond_volDAGM_cond_vol_no_skewDAGM_loglikDAGM_loglik_no_skewDAGM_long_run_volDAGM_long_run_vol_no_skewDAGM_X_cond_volDAGM_X_cond_vol_no_skewDAGM_X_loglikDAGM_X_loglik_no_skewDAGM_X_long_run_volDAGM_X_long_run_vol_no_skewexp_almonGM_2M_cond_volGM_2M_cond_vol_no_skewGM_2M_loglikGM_2M_loglik_no_skewGM_2M_long_run_volGM_2M_long_run_vol_no_skewGM_cond_volGM_cond_vol_no_skewGM_loglikGM_loglik_no_skewGM_long_run_volGM_long_run_vol_no_skewGM_X_cond_volGM_X_cond_vol_no_skewGM_X_loglikGM_X_loglik_no_skewGM_X_long_run_volGM_X_long_run_vol_no_skewMEM_loglikMEM_loglik_no_skewMEM_MIDAS_loglikMEM_MIDAS_loglik_no_skewMEM_MIDAS_lr_predMEM_MIDAS_lr_pred_no_skewMEM_MIDAS_predMEM_MIDAS_pred_no_skewMEM_MIDAS_X_loglikMEM_MIDAS_X_loglik_no_skewMEM_MIDAS_X_lr_predMEM_MIDAS_X_lr_pred_no_skewMEM_MIDAS_X_predMEM_MIDAS_X_pred_no_skewMEM_predMEM_pred_no_skewMEM_X_loglikMEM_X_loglik_no_skewMEM_X_predMEM_X_pred_no_skewmulti_step_ahead_predmv_into_matprint.rumidassummary.rumidasugmfitumemfit

Dependencies:cpp11curldigestgenericsjsonlitelatticelubridatemaxLikmiscToolsquadprogquantmodrbibutilsRcppRcppArmadilloRcppParallelRdpackrollsandwichtimechangetseriesTTRxtszoo