Metadata-Version: 2.3
Name: mvgarch
Version: 1.0.0
Summary: Multivariate GARCH modelling in Python
Project-URL: Homepage, https://github.com/jamesjtobin/mvgarch
Project-URL: Bug Tracker, https://github.com/jamesjtobin/mvgarch/issues
Author-email: Jack Tobin <tobjack330@gmail.com>
License: MIT License
        
        Copyright (c) 2022 jamesjtobin
        
        Permission is hereby granted, free of charge, to any person obtaining a copy
        of this software and associated documentation files (the "Software"), to deal
        in 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:
        
        The above copyright notice and this permission notice shall be included in all
        copies or substantial portions of the Software.
        
        THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
        IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
        FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
        AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
        LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
        OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
        SOFTWARE.
License-File: LICENSE
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: Microsoft :: Windows :: Windows 11
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# mvgarch
Multivariate GARCH modelling in Python

## Description
This project performs a basic multivariate GARCH modelling exercise in Python. Such approaches are available in other environments such as R, but there is yet to exist a tractable framework for performing the same tasks in Python. This package should help alleviate such limitations and allow Python users to deploy multivariate GARCH models easily.

## Installation

```bash
$ pip install mvgarch
```

## Usage

```python
# get return data
# returns = pd.DataFrame() of periodic returns of shape (n_periods, n_assets)

# import modules
from mvgarch.mgarch import DCCGARCH
from mvgarch.ugarch import UGARCH

# FIT UNIVARIATE GARCH MODEL

# get one of the return series
asset = returns.iloc[:, 0]

# fit a gjr-garch(1, 1) model to the first return series
garch = UGARCH(order=(1, 1))
garch.spec(returns=asset)
garch.fit()

# FIT MULTIVARIATE DCC GARCH MODEL

# make a list of garch(1, 1) objects
garch_specs = [UGARCH(order=(1, 1)) for _ in range(n_tickers)]

# fit DCCGARCH to the return data
dcc = DCCGARCH()
dcc.spec(ugarch_objs=garch_specs, returns=returns)
dcc.fit()

# forecast 4 weeks ahead
dcc.forecast(n_ahead=4)
```

## Contributing
Pull requests are welcome.

## License
[MIT](https://choosealicense.com/licenses/mit/)
