https://github.com/bashtage/arch
Tip revision: 2cb8a8e57fd488e65ba620062a280500e71f866e authored by Kevin Sheppard on 13 October 2018, 17:30:28 UTC
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Tip revision: 2cb8a8e
README.md
# ARCH
Autoregressive Conditional Heteroskedasticity (ARCH) and other tools for
financial econometrics, written in Python (with Cython and/or Numba used
to improve performance)
###### Continuous Integration
[](https://travis-ci.org/bashtage/arch)
[](https://ci.appveyor.com/project/bashtage/arch/branch/master)
###### Documentation
[](http://arch.readthedocs.org/en/latest/)
###### Coverage
[](https://coveralls.io/r/bashtage/arch?branch=master)
[](https://codecov.io/gh/bashtage/arch)
###### Code Inspections
[](https://lgtm.com/projects/g/bashtage/arch/context:python)
[](https://lgtm.com/projects/g/bashtage/arch/alerts)
[](https://www.codacy.com/app/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade)
[](https://codebeat.co/projects/github-com-bashtage-arch-master)
###### Citation
[](https://zenodo.org/badge/latestdoi/23468876)
## Module Contents
* [Univariate ARCH Models](#volatility)
* [Unit Root Tests](#unit-root)
* [Bootstrapping](#bootstrap)
* [Multiple Comparison Tests](#multiple-comparison)
## Documentation
Released documentation is hosted on
[read the docs](http://arch.readthedocs.org/en/latest/).
Current documentation from the master branch is hosted on
[my github pages](http://bashtage.github.io/arch/doc/index.html).
## More about ARCH
More information about ARCH and related models is available in the notes and
research available at [Kevin Sheppard's site](http://www.kevinsheppard.com).
## 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
<a name="volatility"/>
### 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](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/univariate_volatility_modeling.ipynb) for a more complete overview.
```python
import datetime as dt
import pandas.io.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()
```
<a name="unit-root"/>
### Unit Root Tests
* Augmented Dickey-Fuller
* Dickey-Fuller GLS
* Phillips-Perron
* KPSS
* Variance Ratio tests
See the [unit root testing example notebook](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/unitroot_examples.ipynb) for examples of testing series for unit roots.
<a name="bootstrap"/>
### 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](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/bootstrap_examples.ipynb)
for examples of bootstrapping the Sharpe ratio and a Probit model from
Statsmodels.
```python
# Import data
import datetime as dt
import pandas as pd
import pandas.io.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')
```
<a name="multiple-comparison"/>
### 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](http://nbviewer.ipython.org/github/bashtage/arch/blob/master/examples/multiple-comparison_examples.ipynb)
for examples of the multiple comparison procedures.
## Requirements
These requirements reflect the testing environment. It is possible
that arch will work with older versions.
* Python (2.7, 3.5 - 3.7)
* NumPy (1.13+)
* SciPy (0.19+)
* Pandas (0.21+)
* statsmodels (0.8+)
* matplotlib (2.0+), optional
* cached-property (1.5.1+), optional
### Optional Requirements
* Numba (0.35+) will be used if available **and** when installed using
the --no-binary option
* 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`.
```bash
pip install arch
```
This command should work whether you have a compiler installed or not.
If you want to install with the `--no-binary` options, use
```bash
pip install arch --install-option="--no-binary"
```
You can alternatively install the latest version from GitHub
```bash
pip install git+https://github.com/bashtage/arch.git
```
`--install-option="--no-binary"` can be used to disable compilation of
the extensions.
### Anaconda
`conda` users can install from my channel,
```bash
conda install arch -c bashtage
```
### Windows
Building extension using the community edition of Visual Studio is
well supported for Python 3.5+. Building extensions for 64-bit Windows
for use in Python 2.7 is also supported using Microsoft Visual C++
Compiler for Python 2.7. Building on other combinations of Python/Windows
is more difficult and 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 (0.24+, if not using --no-binary)
* py.test (For tests)
* sphinx (to build docs)
* guzzle_sphinx_theme (to build docs)
* jupyter, notebook and nbsphinx (to build docs)
### Installation Notes:
1. If Cython is not installed, the package will be installed
as-if `--no-binary` was used.
2. Setup does not verify these requirements. Please ensure these are
installed.
