Improvement and importance indicator analysis on Volatility Forecasting of GARCH Model by Random Forest Algorithm - Case of Bitcoins
This is a simplify code of my Master thesis research. We use the random forest algorithm to predict the volitility
with diffrernt features, and compare with the traditional GARCH(1,1) model in the long-term time series data.
the result is significant which the random forest algorithm beats the GARCH(1,1) model in volatility forcasting,
and the prediction error of the optimal model has below 0.01% .
Please read the Makefile and follow the instruction. All the expirements can be done by your terminal