What is 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. The conditional mean equation includes ARFIMA and ARCH-in-mean, and is estimated in a joint step with the GARCH model.

What is univariate GARCH?

Univariate GARCH models are used to model/forecast volatility of one time series. Multivariate GARCH models are used to model/forecast volatility of several time series when there are some linkages between them.

What is ARCH in time series?

Autoregressive conditional heteroskedasticity (ARCH) is a statistical model used to analyze volatility in time series in order to forecast future volatility. In the financial world, ARCH modeling is used to estimate risk by providing a model of volatility that more closely resembles real markets.

How do I choose a good GARCH model?

(1) define a pool of candidate models, (2) estimate the models on part of the sample, (3) use the estimated models to predict the remainder of the sample, (4) pick the model that has the lowest prediction error.

How to get the same results using fGARCH and rugarch?

I’m trying to get the same GARCH (1,1) on both fGARCH and rugarch packages but the ‘sigma’ series I get from both seems to be very different. The code I use is below. How can I set up rugarch to be exactly like fGARCH?

Is the rugarch package part of the rgarch project?

The rugarchpackage forms part of the rgarch project on r-forge rgarch.r-forge.r-project. org/ which also includes the rmgarch package for multivariate GARCH models. Previously, both univariate and multivariate models were included in one large package which was split for release to CRAN in August 2011.

Is the mean constant in the rugarch model?

The mean is constant in our model so the forecast of the returnseries for any period is simply the estimated constant mean coef(modelfit)[“mu\\.

How to model volatility with arch and GARCH?

A GARCH model subsumes ARCH models, where a GARCH (0, q) is equivalent to an ARCH (q) model. For p = 0 the process reduces to the ARCH (q) process, and for p = q = 0 E (t) is simply white noise.