An R package for using mixed-frequency GARCH models
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README.md

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mfGARCH - mixed-frequency GARCH models

An R package for estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels and 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 (2018, doi:10.2139/ssrn.2752354). 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.

Highlights

  • A comprehensive toolbox for estimating and forecasting using GARCH-MIDAS models
  • Easy to use, both with one or two explanatory covariates
  • Built for handling irregularly spaced mixed-frequency data

Please cite as

Conrad, Christian and Kleen, Onno (2018). Two Are Better Than One: Volatility Forecasting Using Multiplicative Component GARCH Models. Available at SSRN: https://doi.org/10.2139/ssrn.2752354.

and

Kleen, Onno (2018). mfGARCH: Mixed-Frequency GARCH Models. R package version 0.1.7.

Installation

CRAN:

install.packages("mfGARCH")

Development version:

# Install package via devtools
# install.packages("devtools")
library(devtools)
install_github("onnokleen/mfGARCH")

Example

library(mfGARCH)
# df_financial
fit_mfgarch(data = df_financial, y = "return", x = "nfci", low.freq = "week", K = 52)