Package: BEKKs 1.4.5

Markus J. Fülle

BEKKs: Multivariate Conditional Volatility Modelling and Forecasting

Methods and tools for estimating, simulating and forecasting of so-called BEKK-models (named after Baba, Engle, Kraft and Kroner) based on the fast Berndt–Hall–Hall–Hausman (BHHH) algorithm described in Hafner and Herwartz (2008) <doi:10.1007/s00184-007-0130-y>. For an overview, we refer the reader to Fülle et al. (2024) <doi:10.18637/jss.v111.i04>.

Authors:Markus J. Fülle [aut, cre], Alexander Lange [aut], Christian M. Hafner [aut], Helmut Herwartz [aut]

BEKKs_1.4.5.tar.gz
BEKKs_1.4.5.tar.gz(r-4.5-noble)BEKKs_1.4.5.tar.gz(r-4.4-noble)
BEKKs_1.4.5.tgz(r-4.4-emscripten)BEKKs_1.4.5.tgz(r-4.3-emscripten)
BEKKs.pdf |BEKKs.html✨
BEKKs/json (API)

# Install 'BEKKs' in R:
install.packages('BEKKs', repos = 'https://cloud.r-project.org')
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

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

openblascppopenmp

1.00 score 536 downloads 6 exports 69 dependencies

Last updated 4 months agofrom:ec2041d91f. Checks:3 OK. Indexed: no.

TargetResultLatest binary
Doc / VignettesOKMar 25 2025
R-4.5-linux-x86_64OKMar 25 2025
R-4.4-linux-x86_64OKMar 25 2025

Exports:backtestbekk_fitbekk_specportmanteau.testVaRvirf

Dependencies:clicodetoolscolorspacecpp11cubaturedigestdplyrfansifarverFNNfuturefuture.applyGASgenericsggfortifyggplot2globalsgluegridExtragtableisobandkernlabKernSmoothkslabelinglatticelifecyclelistenvlubridatemagrittrMASSmathjaxrMatrixmclustmgcvmomentsmulticoolmunsellmvtnormnlmenumDerivparallellypbapplypillarpkgconfigplyrpracmapurrrR6RColorBrewerRcppRcppArmadilloreshape2rlangRsolnpscalesstringistringrtibbletidyrtidyselecttimechangetruncnormutf8vctrsviridisLitewithrxtszoo

Citation

To cite BEKKs in publications use:

Fülle MJ, Lange A, Hafner CM, Herwartz H (2024). “BEKKs: An R Package for Estimation of Conditional Volatility of Multivariate Time Series.” Journal of Statistical Software, 111(4), 1–34. doi:10.18637/jss.v111.i04.

Corresponding BibTeX entry: 

  @Article{,
    title = {{BEKKs}: An {R} Package for Estimation of Conditional
      Volatility of Multivariate Time Series},
    author = {Markus J. F\"ulle and Alexander Lange and Christian M.
      Hafner and Helmut Herwartz},
    journal = {Journal of Statistical Software},
    year = {2024},
    volume = {111},
    number = {4},
    pages = {1--34},
    doi = {10.18637/jss.v111.i04},
  }