Metadata-Version: 2.1
Name: arch
Version: 7.1.0
Summary: ARCH for Python
Author-email: Kevin Sheppard <kevin.k.sheppard@gmail.com>
Maintainer-email: Kevin Sheppard <kevin.k.sheppard@gmail.com>
License: # License
        
        **Copyright (c) 2017 Kevin Sheppard. All rights reserved.**
        
        Developed by: Kevin Sheppard (<kevin.sheppard@economics.ox.ac.uk>,  
        <kevin.k.sheppard@gmail.com>)
        [https://www.kevinsheppard.com](https://www.kevinsheppard.com)
        
        Permission is hereby granted, free of charge, to any person obtaining a copy of
        this software and associated documentation files (the "Software"), to deal with
        the Software without restriction, including without limitation the rights to
        use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies
        of the Software, and to permit persons to whom the Software is furnished to do
        so, subject to the following conditions:
        
        Redistributions of source code must retain the above copyright notice, this
        list of conditions and the following disclaimers.
        
        Redistributions in binary form must reproduce the above copyright notice, this
        list of conditions and the following disclaimers in the documentation and/or
        other materials provided with the distribution.
        
        Neither the names of Kevin Sheppard, nor the names of its contributors may be
        used to endorse or promote products derived from this Software without specific
        prior written permission.
        
        **THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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Project-URL: homepage, https://github.com/bashtage/arch
Project-URL: documentation, https://bashtage.github.io/arch/
Project-URL: repository, https://github.com/bashtage/arch
Project-URL: changelog, https://bashtage.github.io/arch/changes.html
Keywords: arch,ARCH,variance,econometrics,volatility,finance,GARCH,bootstrap,random walk,unit root,Dickey Fuller,time series,confidence intervals,multiple comparisons,Reality Check,SPA,StepM
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Financial and Insurance Industry
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: License :: OSI Approved :: University of Illinois/NCSA Open Source License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: POSIX
Classifier: Programming Language :: Python
Classifier: Programming Language :: Cython
Classifier: Topic :: Scientific/Engineering
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE.md
Requires-Dist: numpy>=1.22.3
Requires-Dist: scipy>=1.8
Requires-Dist: pandas>=1.4
Requires-Dist: statsmodels>=0.12

# arch

[![arch](https://bashtage.github.io/arch/doc/_static/images/color-logo-256.png)](https://github.com/bashtage/arch)

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**         | [![PyPI version](https://badge.fury.io/py/arch.svg)](https://badge.fury.io/py/arch)                                                                                                                                                      |
|                            | [![conda-forge version](https://anaconda.org/conda-forge/arch-py/badges/version.svg)](https://anaconda.org/conda-forge/arch-py)                                                                                                          |
| **Continuous Integration** | [![Build Status](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_apis/build/status/bashtage.arch?branchName=main)](https://dev.azure.com/kevinksheppard0207/kevinksheppard/_build/latest?definitionId=1&branchName=main)        |
| **Coverage**               | [![codecov](https://codecov.io/gh/bashtage/arch/branch/main/graph/badge.svg)](https://codecov.io/gh/bashtage/arch)                                                                                                                       |
| **Code Quality**           | [![Codacy Badge](https://api.codacy.com/project/badge/Grade/93f6fd90209842bf97fd20fda8db70ef)](https://www.codacy.com/manual/bashtage/arch?utm_source=github.com&utm_medium=referral&utm_content=bashtage/arch&utm_campaign=Badge_Grade) |
|                            | [![codebeat badge](https://codebeat.co/badges/18a78c15-d74b-4820-b56d-72f7e4087532)](https://codebeat.co/projects/github-com-bashtage-arch-main)                                                                                         |
| **Citation**               | [![DOI](https://zenodo.org/badge/doi/10.5281/zenodo.593254.svg)](https://doi.org/10.5281/zenodo.593254)                                                                                                                                  |
| **Documentation**          | [![Documentation Status](https://readthedocs.org/projects/arch/badge/?version=latest)](https://arch.readthedocs.org/en/latest/)                                                                                                          |

## Module Contents

- [Univariate ARCH Models](#volatility)
- [Unit Root Tests](#unit-root)
- [Cointegration Testing and Analysis](#cointegration)
- [Bootstrapping](#bootstrap)
- [Multiple Comparison Tests](#multiple-comparison)
- [Long-run Covariance Estimation](#long-run-covariance)

### Python 3

`arch` is Python 3 only. Version 4.8 is the final version that supported Python 2.7.

## Documentation

Documentation from the main branch is hosted on
[my github pages](https://bashtage.github.io/arch/).

Released documentation is hosted on
[read the docs](https://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](https://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

### 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](https://bashtage.github.io/arch/univariate/univariate_volatility_modeling.html) for a more complete overview.

```python
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()
```


### Unit Root Tests

- Augmented Dickey-Fuller
- Dickey-Fuller GLS
- Phillips-Perron
- KPSS
- Zivot-Andrews
- Variance Ratio tests

See the [unit root testing example notebook](https://bashtage.github.io/arch/unitroot/unitroot_examples.html)
for examples of testing series for unit roots.

### Cointegration Testing and Analysis

- Tests
  - Engle-Granger Test
  - Phillips-Ouliaris Test
- Cointegration Vector Estimation
  - Canonical Cointegrating Regression
  - Dynamic OLS
  - Fully Modified OLS

See the [cointegration testing example notebook](https://bashtage.github.io/arch/unitroot/unitroot_cointegration_examples.html)
for examples of testing series for cointegration.

### 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](https://bashtage.github.io/arch/bootstrap/bootstrap_examples.html)
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 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')
```


### 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](https://bashtage.github.io/arch/multiple-comparison/multiple-comparison_examples.html)
for examples of the multiple comparison procedures.

### Long-run Covariance Estimation

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.

```python
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_run
```

## Requirements

These 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


### Optional Requirements

- 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=1` and install without the wheel.

```shell
export ARCH_NO_BINARY=1
python -m pip install arch
```

or if using Powershell on windows

```powershell
$env:ARCH_NO_BINARY=1
python -m pip install arch
```

- 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`.

```shell
pip install arch
```

You can alternatively install the latest version from GitHub

```bash
pip install git+https://github.com/bashtage/arch.git
```

Setting the environment variable `ARCH_NO_BINARY=1` can be used to
disable compilation of the extensions.

### Anaconda

`conda` users can install from conda-forge,

```bash
conda install arch-py -c conda-forge
```

**Note**: The conda-forge name is `arch-py`.

### Windows

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.

### Developing

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)

### Installation Notes

1. If Cython is not installed, the package will be installed
    as-if `ARCH_NO_BINARY=1` was set.
2. Setup does not verify these requirements. Please ensure these are
    installed.
