garch_model.params['alpha[1]'],\
garch_model.params['beta[1]']
forecaster = hlp.GarchForecaster(params)
garch_predict = pd.Series(test_volatility,dtype=float)\
.apply(lambda x: forecaster.forecast(x))\
.shift(1)
dFrame = pd.DataFrame(test_volatility, columns=['Actual'])
dFrame['Predict'] = garch_predict
dFrame.plot(title=cur)
y = dFrame.loc[1:,'Actual']
yhat = dFrame.loc[1:,'Predict']
mfe = hlp.mean_forecast_err(y,yhat)
mae = hlp.mean_absolute_err(y,yhat)
print 'MFE={}\nMAE={}\n\n'.format(mfe,mae*100)
plt.show()
#http://www.quantatrisk.com/2014/10/23/garch11-model-in-python/
#https://onlinecourses.science.psu.edu/stat510/node/62
#http://statsmodels.sourceforge.net/devel/examples/notebooks/generated/tsa_arma.html
#http://cims.nyu.edu/~almgren/timeseries/Vol_Forecast1.pdf
