DCC-GARCH is a Python package for a bivariate volatility model called Dynamic Conditional Correlation GARCH, which is widely implemented in the contexts of finance.

The basic statistical theory on DCC-GARCH can be found in Multivariate DCC-GARCH Model (Elisabeth Orskaug, 2009).

Since my module DCC-GARCH is intially designed for the computation of SRISK (Brownlees & Engle, 2016), it only performs a Dynamic Conditional Correlation of order (1,1) and a GARCH of order (1,1). However, empirical works find that DCC(1,1)-GARCH(1,1) is adequate in most of the financial problems so the inconvenience may be minor.

In addition, a multi-step Monte Carlo simulation is also provided for computing the expectation of one variable conditioning on the value of the other.

Note that some parts of the code are still experimental, as we haven't implemented public API for them. If you find a bug or have useful suggestions, please feel free to open an issue / pull request, or email Suoer Xu. Your contributions would be greatly appreciated!

DCC-GARCH depends on numpy, scikit-learn and scipy. Currently, it is only tested on Windows with Python 3.6.

DCC-GARCH is distributed under the Apache License, Version 2.0.