# How would you correct a GARCH model to deal with non mean reverting volatility?

I am currently attempting to model and forecast volatility of bitcoin but have not been able to find a GARCH model that fits the data appropriately. I've used tick data sampled at 1 hour intervals over a 2 year period and converted it into hourly returns. The best model i have been able to produce so far is an asymmetric garch (3,3) model.

The portmanteau stat is 198.4**

alpha(1)+beta(1) 1.02753

I have tried GARCH-M,EGARCH,TGARCH all up to (3,3). For some reason I cannot specify (p,q) to be any higher than 3? What steps can I take to improve the model further?

Would it be beneficial to account for seasonality or jumps similair to todrov (2011) and andersen and bollerslev(2005)?

Note: limited programming knowledge so would prefer to avoid R, output produced by PCGIVE10.

-

The mean reversion of the volatility is due to the Moving Average part of the volatility process. The solution would be to set $\beta = 0$. In other words you have to use an AR process for the volatility (so an ARCH model for price).