I'm trying to produce one-day ahead volatility forecasts for Bitcoin with
Realized GARCH(1,1) using the rugarch package in R. The realized variance(
data$rv5) is aggregated based on a 5 minute frequency, and the returns(
data.xts$ret) are close-to-close. Here's the specs:
rgarch.spec<- ugarchspec(mean.model = list(armaOrder= c(0,0),
include.mean = FALSE),
variance.model = list(model= 'realGARCH',
garchOrder= c(1,1)),
distribution.model = 'norm')
rgarchroll<- ugarchroll(spec = rgarch.spec,
data= data.xts$ret,
n.ahead = 1,
forecast.length = forecast_len,
refit.every = 5,
solver= 'hybrid',
realizedVol= data.xts$rv5,
VaR.alpha = c(0.01, 0.05, 0.10))
realized_vol= sqrt(tail(data.xts$rv5,forecast_len)),
rgarch.prediction_vol= rgarchroll@forecast$density$Sigma)
A plot of the results can be found here: https://i.stack.imgur.com/XAA3r.png
As you can see, the predicted volatility is consistently higher than the
realized volatility. Needless to say, the VaR predictions are not accurate
at all. However, the standard GARCH(1,1) model works fine using the same
return data. So what could possibly be the issue?
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