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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
    * 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
* Bootstrapping
    * 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
* Unit Root Tests
    * Augmented Dickey-Fuller
    * Dickey-Fuller GLS
    * Phillips-Perron
    * KPSS
    * Variance Ratio tests
     

## Examples

### Volatility Modeling 

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()
```

### Bootstrap

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')    
```

### Unit Root 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.

## Documentation
Documentation is hosted on [read the docs](http://arch.readthedocs.org/en/latest/)
 
## Requirements

* NumPy (1.7+)
* SciPy (0.12+)
* Pandas (0.14+)
* statsmodels (0.5+)
* matplotlib (1.3+)

### Optional Requirements

* Numba (0.14+), only required if installing using --no-binary

### Installing

* Cython (0.20+, if not using --no-binary)
* nose (For tests)
* sphinx (to build docs)
* sphinx-napoleon (to build docs)

## Installing

Setup does not verify requirements.  Please ensure these are installed.

### Linux/OSX

```
pip install git+git://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+git://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+git://github.com/bashtage/arch.git --install-option "--no-binary"
```

_Note that it isn't possible to run the test suite. It will fail if installed with_ `--no-binary` _since it tests the Numba implementations against Cython implementations._

**Anaconda**

```
conda install -c https://conda.binstar.org/bashtage arch
```

## 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