Can a GARCH model be used to forecast returns?
Obviously, the GARCH model is about volatility and variance of returns. It can only forecast volatility, but not returns. Actually, It is much more difficult to forecast returns than to forecast volatility.
How is the GARCH model used in the ARIMA model?
The previous post used the ARIMA model to give structure to the changing mean of the series of price returns. Since the ARIMA model assumed constant variance, and the figure of SPY returns clearly has changing variance over time, this is something that can be improved upon, and the GARCH model is one way of accomplishing this.
Can a GARCH model be used for Spy Returns?
Since the ARIMA model assumed constant variance, and the figure of SPY returns clearly has changing variance over time, this is something that can be improved upon, and the GARCH model is one way of accomplishing this. Next, we will go through two ways that are commonly used to visualize the changing variance of returns.
Which is the best model for modeling volatility?
In this post we will learn a standard technique for modelling volatility in a series of prices, the generalized auto-regressive conditional heteroskedasticity (GARCH) model.
Which is more efficient estimating an AR-GARCH model?
For example, an AR-GARCH model could be estimated that way as an AR (p) model can be estimated consistently even in presence of GARCH errors. However, estimating an AR-GARCH model in one stage (simultaneously) would be more efficient.
What does a GARCH model do for conditional variance?
A GARCH model gives you a fitted value of the conditional variance for each data point.
How to determine the suitability of a GARCH model?
If you want to recover the true model and the true model happens to be among the set of candidate models, use Bayesian information criterion (BIC). Asymptotically it will select the true model with probability=1.