https://github.com/bashtage/arch
Tip revision: f2eb57eb91ebd4931558ed02ddf3d472ddaf5932 authored by Kevin Sheppard on 29 December 2016, 20:48:19 UTC
Merge pull request #141 from bashtage/release-4.0
Merge pull request #141 from bashtage/release-4.0
Tip revision: f2eb57e
README.md
[](http://arch.readthedocs.org/en/latest/)
[](https://travis-ci.org/bashtage/arch)
[](https://ci.appveyor.com/project/bashtage/arch/branch/master)
[](https://coveralls.io/r/bashtage/arch?branch=master)
[](https://codecov.io/gh/bashtage/arch)
[](https://landscape.io/github/bashtage/arch/master)
[](http://dx.doi.org/10.5281/zenodo.15681)
# ARCH
This is a work-in-progress for ARCH and other tools for financial econometrics,
written in Python (and Cython)
## What is in this repository?
* [Univariate ARCH Models](#volatility)
* [Unit Root Tests](#unit-root)
* [Bootstrapping](#bootstrap)
* [Multiple Comparison Tests](#multiple-comparison)
## Documentation
Documentation is hosted on [read the docs](http://arch.readthedocs.org/en/latest/)
## 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
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
* NumPy (1.9+)
* SciPy (0.15+)
* Pandas (0.16+)
* statsmodels (0.6+)
* matplotlib (1.3+)
### Optional Requirements
* Numba (0.21+) will be used if available **and** when installed using
the --no-binary option
* IPython (4.0+) is required to run the notebooks
### Installing
* Cython (0.20+, if not using --no-binary)
* py.test (For tests)
* sphinx (to build docs)
* sphinx-napoleon (to build docs)
**Note**: Setup does not verify requirements. Please ensure these are
installed.
### Linux/OSX
```
pip install git+https://github.com/bashtage/arch.git
```
**Anaconda**
_Anaconda builds are not currently available for OSX._
```
conda install -c https://conda.binstar.org/bashtage arch
```
### 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 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.
**With a compiler**
If you are comfortable compiling binaries on Windows:
```
pip install git+https://github.com/bashtage/arch.git
```
**No Compiler**
All binary code is backed by a pure Python implementation. Compiling
can be skipped using the flag `--no-binary`
```
pip install git+https://github.com/bashtage/arch.git --install-option "--no-binary"
```
*Note*: If Cython is not installed, the package will be installed as-if
--no-binary was used.
**Anaconda**
```
conda install -c https://conda.binstar.org/bashtage arch
```
