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 (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.

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 (2020). Two are better than one: Volatility forecasting using multiplicative component GARCH-MIDAS models. Journal of Applied Econometrics 35: 19–45.

and

Kleen, Onno (2020). mfGARCH: Mixed-Frequency GARCH Models. R package version 0.2.0.

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)