Revision 3ff24ea18de59fc8ef0a6d6798844e9a5a3caf64 authored by Rob Capellini on 16 September 2015, 13:05:49 UTC, committed by Rob Capellini on 16 September 2015, 13:05:49 UTC
'python setup.py clean' fails due to the glob module not being present. Moved glob import statement to the top of the setup.py file to the top of the file to allow the clean command to work.
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README.md
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# 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 (1.7+)
* SciPy (0.12+)
* Pandas (0.14+)
* statsmodels (0.5+)
* matplotlib (1.3+)
### Optional Requirements
* Numba (0.15+) will be used if available **and** when installed using the
--no-binary option
* IPython (3.0+) is required to run the notebooks
### Installing
* Cython (0.20+, if not using --no-binary)
* nose (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
**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: the test suite compares the Numba implementations against Cython
implementations of some recursions, and so it is not possible to run the
test suite when installing with_ `--no-binary` .
**Anaconda**
```
conda install -c https://conda.binstar.org/bashtage arch
```
![swh spinner](/static/img/swh-spinner.gif)
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