How are GARCH models used to forecast volatility?
GARCH are robust techniques developed for the modelling of high frequency time series data. Past experiments show that they efficiently capture the stylised feature of volatility clustering in financial data. GARCH models are therefore most often used to forecast volatility and subsequently Value-at-Risk (VaR), [4].
How are GARCH and EVT-pot used for forecasting?
Finally, an innovation approach is used whereby GARCH is combined with EVT-POT by using the two-step procedure of McNeil (1998). Statistical methods are used to evaluate the forecasting performance of all the models. In this study, it is found that the GARCH models perform quite well with all the innovations, except for the GARCH-N.
Which is the best GARCH model for value at risk?
GARCH models are therefore most often used to forecast volatility and subsequently Value-at-Risk (VaR), [4]. According to [5] and [6], the GARCH (1,1) specification is the most widely used and has proved to be a successful volatility technique in many past studies.
Who is the creator of the GARCH model?
The Generalised Autoregressive Conditional Heteroskedasticity (GARCH) model by Bollerslev [2] was developed as an extension to the Autoregressive Conditional Heteroskedasticity (ARCH) by Engle [3]. GARCH are robust techniques developed for the modelling of high frequency time series data.
How are GARCH and EVT used in forecasting?
Furthermore, an EVT-based dynamic approach is also investigated, using the popular Peak Over Threshold (POT) method. Finally, an innovation approach is used whereby GARCH is combined with EVT-POT by using the two-step procedure of McNeil (1998). Statistical methods are used to evaluate the forecasting performance of all the models.
Are there any your packages to fit GARCH models?
There are a few R packages to fit GARCH models. I will consider garch function from tseries package and garchFit function from fGarch package. The garch function from tseries package is fast but does not always find solution. The garchFit function from fGarch package is slower but does converge more consistently.