correlationMatrix is a Python powered library for the statistical analysis and visualization of state correlation phenomena. It can be used to analyze any dataset that captures timestamped correlations in a discrete state space. Use cases include credit rating correlations, system state event logs etc.
You can use correlationMatrix to
- Estimate correlation matrices from historical event data using a variety of estimators
- Visualize event data and correlation matrices
- Manipulate correlation matrices (generators, comparisons etc.)
- Provide standardized data sets for testing
- Model correlations using threshold processes
- Author: Open Risk
- License: Apache 2.0
- Code Documentation: Read The Docs
- Mathematical Documentation: Open Risk Manual
- Development website: Github
NB: correlationMatrix is still in active development. If you encounter issues please raise them in our github repository
- The Open Risk Academy has free courses demonstrating the use of the library. The current list is:
- Commercial Support for correlationMatrix is provided as part of OpenCPM
The code documentation includes a large number of examples, jupyter notebooks and more.
Plotting individual correlation trajectories
Sampling correlation data
Estimation of correlation matrices using cohort methods
Estimation of correlation matrices using duration methods
Visualization of a correlation matrix
Generating stochastic process correlation thresholds
Stressing correlation Matrices
Computation and Visualization of Credit Curves
