armagarch 1.0

Library for flexible mean and volatility modelling

armagarch package

The package provides a flexible framework for modelling time-series data. The main focus of the package is implementation of the ARMA-GARCH type models.

Full documentation and installation instruction coming soon.

Example: Modelling conditional volatility of the US excess market returns

The code requires: NumPy, Pandas, SciPy, Shutil, Matplotlib, Pandas_datareader and Statsmodels

import armagarch as ag
import pandas_datareader as web
import matplotlib.pyplot as plt
import numpy as np

# load data from KennethFrench library
ff = web.DataReader('F-F_Research_Data_Factors_daily', 'famafrench')
ff = ff[0]

# define mean, vol and distribution
meanMdl = ag.ARMA(order = {'AR':1,'MA':0})
volMdl = ag.garch(order = {'p':1,'o':1,'q':1})
distMdl = ag.normalDist()

# create a model
model = ag.empModel(ff['Mkt-RF'].to_frame(), meanMdl, volMdl, distMdl)
# fit model
model.fit()

# get the conditional mean
Ey = model.Ey

# get conditional variance
ht = model.ht
cvol = np.sqrt(ht)

# get standardized residuals
stres = model.stres

Authors

• Ian Khrashchevskyi - iankhr

License

This project is licensed under the MIT License - see the LICENSE.md file for details

Acknowledgments

• Special thanks to Kevin Sheppard for his Python for Econometrics, which was an inspiration to write current code