https://github.com/alexiosg/rugarch

Univariate GARCH models in R
https://github.com/alexiosg/rugarch

Last synced: 5 months ago
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Univariate GARCH models in R

• Host: GitHub
• URL: https://github.com/alexiosg/rugarch
• Owner: alexiosg
• Created: 2022-01-18T15:16:27.000Z (about 3 years ago)
• Default Branch: master
• Last Pushed: 2024-08-19T15:32:22.000Z (8 months ago)
• Last Synced: 2024-08-20T16:14:04.179Z (8 months ago)
• Language: R
• Homepage:
• Size: 7.81 MB
• Stars: 20
• Watchers: 2
• Forks: 5
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • Changelog: ChangeLog

Awesome Lists containing this project

• awesome-quant - rugarch - Univariate GARCH Models. (R / Time Series)

README

        
 

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# rugarch #

The rugarch package is the premier open source software for univariate GARCH modelling. It is written in R using S4 methods and classes with a significant part of the code in C and C++ for speed. It contains a number of GARCH models beyond the vanilla version including IGARCH, EGARCH, GJR, APARCH, FGARCH, Component-GARCH, multiplicative Component-GARCH for high frequency returns and the realized-GARCH model, as well as a very large number of conditional distributions including (Skew)-Normal, (Skew)-GED, (Skew)-Student (Fernandez/Steel), (Skew)-Student (GH), Normal Inverse Gaussian (NIG), Generalized Hyperbolic (GH) and Johnson?s SU (JSU). The conditional mean equation includes ARFIMA and ARCH-in-mean, and is estimated in a joint step with the GARCH model. Both the conditional mean and variance parts allow for external regressors to be used. A comprehensive set of methods to work with these models are implemented, and include estimation, filtering, forecasting, simulation, inference tests and plots, with additional functionality in the form of the GARCH bootstrap, parameter uncertainty via the GARCH distribution function, misspecification tests (Hansen's GMM and Hong & Li Portmanteau type test), predictive accuracy tests (Pesaran & Timmermann, Anatolyev  & Gerko), and Value at Risk tests (VaR Exceedances and Expected Shortfall tests).

The stable version is on [CRAN](https://CRAN.R-project.org/package=rugarch).

The development version is now on [github](https://github.com/alexiosg/rugarch).

A new package based on a moden rewrite of rugarch is available [here](https://github.com/tsmodels/tsgarch)