I'm currently facing an issue with GARCH modelling in python. Came across a datascale issue, where y is poorly scaled. Would really appreciate if could get an explanation on the error and perhaps a fix to the issue. GARCH model still runs but with warning messages of no successful convergence.
This is the data which I used for my y-values. It is taken from the residual values of an ARIMA model which i did.
GARCH output:
*Update
After setting rescale=False
inequality constraints incompatible
Minimal reproducable example
import pandas_datareader.data as pdr
import numpy as np
import datetime
import arch
from statsmodels.tsa.arima.model import ARIMA
#Extract Data, create column log returns
eurusd = pdr.DataReader('DEXUSEU', 'fred', start='1/1/2010', end='31/12/2019')
eurusd.index = pd.DatetimeIndex(eurusd.index).to_period('D')
eurusd = eurusd.to_timestamp()
eurusd['LR'] = np.log(eurusd) - np.log(eurusd.shift(1))
# ARIMA model
arima_model = ARIMA(eurusd.LR.dropna(), order=(1,0,1)).fit()
print(arima_model.summary())
# GARCH model
am = arch.arch_model(arima_model.resid)
res = am.fit()
print(res.summary())