Took me only about 20 minutes of google to find some really good info on:
#1 Find the best ARMA model for a subset of time-series.
#2 Apply the ARMA model to optimal ARMA model with a GARCH(1,1)
#3 Use the resulting to predict/forecast next in time-series.
in R. For free. Total code less very small, just consists of a few API calls. Does being a compsci absolve me of not understanding all the underlying stats (I get the gist?)? :
Maybe not too useful for returns (then again ) but apparently, very very useful for predicting/forecasting (monthly) volatility.
What if my time-series is mazzas jazzed up realised vol equation?
Suddenly my head is spinning...
#1 Find the best ARMA model for a subset of time-series.
#2 Apply the ARMA model to optimal ARMA model with a GARCH(1,1)
#3 Use the resulting to predict/forecast next in time-series.
in R. For free. Total code less very small, just consists of a few API calls. Does being a compsci absolve me of not understanding all the underlying stats (I get the gist?)? :
Maybe not too useful for returns (then again ) but apparently, very very useful for predicting/forecasting (monthly) volatility.
What if my time-series is mazzas jazzed up realised vol equation?
The overriding point here however is that market estimations of volatility aren't very accurate and I believe an edge can be obtained by being a better forecaster of volatility
Suddenly my head is spinning...