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arch

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

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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.