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mfGARCH (version 0.2.1)

Mixed-Frequency GARCH Models

Description

Estimating GARCH-MIDAS (MIxed-DAta-Sampling) models (Engle, Ghysels, Sohn, 2013, ) and related statistical inference, accompanying the paper "Two are better than one: Volatility forecasting using multiplicative component GARCH models" by Conrad and Kleen (2020, ). 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.

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Install

install.packages('mfGARCH')

Monthly Downloads

528

Version

0.2.1

License

MIT + file LICENSE

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Maintainer

Onno Kleen

Last Published

June 17th, 2021

Functions in mfGARCH (0.2.1)

simulate_mfgarch_rv_dependent

Simulate a GARCH-MIDAS similar to Wang/Ghysels with lagged RVol as covariate
df_mfgarch

Mixed-frequency data set.
df_financial

Stock returns and financial conditions.
simulate_mfgarch_diffusion

This function simulates a GARCH-MIDAS model where the short-term GARCH component is replaced by its diffusion limit, see Andersen (1998)
simulate_mfgarch

This function simulates a GARCH-MIDAS model. Innovations can follow a standard normal or student-t distribution.
plot_weighting_scheme

This function plots the weighting scheme of an estimated GARCH-MIDAS model
fit_mfgarch

This function estimates a multiplicative mixed-frequency GARCH model. For the sake of numerical stability, it is best to multiply log returns by 100.