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 |
- Univariate ARCH Models
- Unit Root Tests
- Cointegration Testing and Analysis
- Bootstrapping
- Multiple Comparison Tests
- Long-run Covariance Estimation
arch is Python 3 only. Version 4.8 is the final version that supported Python 2.7.
Documentation from the main branch is hosted on my github pages.
Released documentation is hosted on read the docs.
More information about ARCH and related models is available in the notes and research available at Kevin Sheppard's site.
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
- 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()- 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.
- 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.
- 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')- 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.
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_runThese 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
- 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=1and install without the wheel.
export ARCH_NO_BINARY=1
python -m pip install archor if using Powershell on windows
$env:ARCH_NO_BINARY=1
python -m pip install arch- jupyter and notebook are required to run the notebooks
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.
Releases are available PyPI and can be installed with pip.
pip install archYou can alternatively install the latest version from GitHub
pip install git+https://github.com/bashtage/arch.gitSetting the environment variable ARCH_NO_BINARY=1 can be used to
disable compilation of the extensions.
conda users can install from conda-forge,
conda install arch-py -c conda-forgeNote: The conda-forge name is arch-py.
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.
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)
- If Cython is not installed, the package will be installed
as-if
ARCH_NO_BINARY=1was set. - Setup does not verify these requirements. Please ensure these are installed.