arch 7.0.0

Description:

arch 7.0.0

arch

Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
financial econometrics, written in Python (with Cython and/or Numba used
to improve performance)



Metric





Latest Release







Continuous Integration



Coverage



Code Quality







Citation



Documentation




Module Contents

Univariate ARCH Models
Unit Root Tests
Cointegration Testing and Analysis
Bootstrapping
Multiple Comparison Tests
Long-run Covariance Estimation

Python 3
arch is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
Documentation
Documentation from the main branch is hosted on
my github pages.
Released documentation is hosted on
read the docs.
More about ARCH
More information about ARCH and related models is available in the notes and
research available at Kevin Sheppard's site.
Contributing
Contributions are welcome. There are opportunities at many levels to contribute:

Implement new volatility process, e.g., FIGARCH
Improve docstrings where unclear or with typos
Provide examples, preferably in the form of IPython notebooks

Examples

Volatility Modeling

Mean models

Constant mean
Heterogeneous Autoregression (HAR)
Autoregression (AR)
Zero mean
Models with and without exogenous regressors


Volatility models

ARCH
GARCH
TARCH
EGARCH
EWMA/RiskMetrics


Distributions

Normal
Student's T
Generalized Error Distribution



See the univariate volatility example notebook for a more complete overview.
import datetime as dt
import pandas_datareader.data as web
st = dt.datetime(1990,1,1)
en = dt.datetime(2014,1,1)
data = web.get_data_yahoo('^FTSE', start=st, end=en)
returns = 100 * data['Adj Close'].pct_change().dropna()

from arch import arch_model
am = arch_model(returns)
res = am.fit()


Unit Root Tests

Augmented Dickey-Fuller
Dickey-Fuller GLS
Phillips-Perron
KPSS
Zivot-Andrews
Variance Ratio tests

See the unit root testing example notebook
for examples of testing series for unit roots.

Cointegration Testing and Analysis

Tests

Engle-Granger Test
Phillips-Ouliaris Test


Cointegration Vector Estimation

Canonical Cointegrating Regression
Dynamic OLS
Fully Modified OLS



See the cointegration testing example notebook
for examples of testing series for cointegration.

Bootstrap

Bootstraps

IID Bootstrap
Stationary Bootstrap
Circular Block Bootstrap
Moving Block Bootstrap


Methods

Confidence interval construction
Covariance estimation
Apply method to estimate model across bootstraps
Generic Bootstrap iterator



See the bootstrap example notebook
for examples of bootstrapping the Sharpe ratio and a Probit model from statsmodels.
# Import data
import datetime as dt
import pandas as pd
import numpy as np
import pandas_datareader.data as web
start = dt.datetime(1951,1,1)
end = dt.datetime(2014,1,1)
sp500 = web.get_data_yahoo('^GSPC', start=start, end=end)
start = sp500.index.min()
end = sp500.index.max()
monthly_dates = pd.date_range(start, end, freq='M')
monthly = sp500.reindex(monthly_dates, method='ffill')
returns = 100 * monthly['Adj Close'].pct_change().dropna()

# Function to compute parameters
def sharpe_ratio(x):
mu, sigma = 12 * x.mean(), np.sqrt(12 * x.var())
return np.array([mu, sigma, mu / sigma])

# Bootstrap confidence intervals
from arch.bootstrap import IIDBootstrap
bs = IIDBootstrap(returns)
ci = bs.conf_int(sharpe_ratio, 1000, method='percentile')


Multiple Comparison Procedures

Test of Superior Predictive Ability (SPA), also known as the Reality
Check or Bootstrap Data Snooper
Stepwise (StepM)
Model Confidence Set (MCS)

See the multiple comparison example notebook
for examples of the multiple comparison procedures.

Long-run Covariance Estimation
Kernel-based estimators of long-run covariance including the
Bartlett kernel which is known as Newey-West in econometrics.
Automatic bandwidth selection is available for all of the
covariance estimators.
from arch.covariance.kernel import Bartlett
from arch.data import nasdaq
data = nasdaq.load()
returns = data[["Adj Close"]].pct_change().dropna()

cov_est = Bartlett(returns ** 2)
# Get the long-run covariance
cov_est.cov.long_run

Requirements
These requirements reflect the testing environment. It is possible
that arch will work with older versions.

Python (3.9+)
NumPy (1.19+)
SciPy (1.5+)
Pandas (1.1+)
statsmodels (0.12+)
matplotlib (3+), optional

Optional Requirements

Numba (0.49+) will be used if available and when installed without building the binary modules. In order to ensure that these are not built, you must set the environment variable ARCH_NO_BINARY=1 and install without the wheel.

export ARCH_NO_BINARY=1
python -m pip install arch

or if using Powershell on windows
$env:ARCH_NO_BINARY=1
python -m pip install arch


jupyter and notebook are required to run the notebooks

Installing
Standard installation with a compiler requires Cython. If you do not
have a compiler installed, the arch should still install. You will
see a warning but this can be ignored. If you don't have a compiler,
numba is strongly recommended.
pip
Releases are available PyPI and can be installed with pip.
pip install arch

You can alternatively install the latest version from GitHub
pip install git+https://github.com/bashtage/arch.git

Setting the environment variable ARCH_NO_BINARY=1 can be used to
disable compilation of the extensions.
Anaconda
conda users can install from conda-forge,
conda install arch-py -c conda-forge

Note: The conda-forge name is arch-py.
Windows
Building extension using the community edition of Visual Studio is
simple when using Python 3.8 or later. Building is not necessary when numba
is installed since just-in-time compiled code (numba) runs as fast as
ahead-of-time compiled extensions.
Developing
The development requirements are:

Cython (0.29+, if not using ARCH_NO_BINARY=1, supports 3.0.0b2+)
pytest (For tests)
sphinx (to build docs)
sphinx-immaterial (to build docs)
jupyter, notebook and nbsphinx (to build docs)

Installation Notes

If Cython is not installed, the package will be installed
as-if ARCH_NO_BINARY=1 was set.
Setup does not verify these requirements. Please ensure these are
installed.