# Accuracy for GARCH models

How does one calculate the accuracy of forecasts given by GARCH models considering GARCH is run on returns. Assuming GARCH is a derivative of a regression based prediction model, would regular statistics like R squared, MAPE/ SMAPE etc be the right indicator for the performance? Unlike ARIMA where the predictive power just dies down after a forecast interval, I experience GARCH forecasting values of almost any time period specified. How would one be able to identify if there is any randomness in the forecasted values?

• Compare forecast vs actual observations, check Q-Q plots to see if forecasts are biased?
– rbm
Mar 15 '18 at 13:40

1. Estimate arch-type your model in monthly data using a window from $[t_{start}, t_{end}$. Compute volatility forecast for month $t_{end+1}$.
2. Compute realized volatility during month $t_{end+1}$ using daily data (e.g. sum of squared returns).