The openNPL credit portfolio management platform implements and builds on the detailed European Banking Authority loan templates for NPL data. It thereby enables the collection and easy management of non-performing loan data according to best-practices.
In the the latest update (0.5.3) the platform is expanded to enable working also with US Agency (Fannie Mae) Mortgage Loan Performance data. This functionality is still under development and will mature with the upcoming 0.6 release of openNPL (along with other related resources) so stay tuned! The updated code documentation is available here. The corresponding data dictionaries and concepts are documented with a new category at the Open Risk Manual.
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Academy Course DAT31063 (Class Inheritance in Data Science) has been updated
Another year in permacrisis, yet the future is in our hands
Dear users and friends of the Open Risk Academy, it is this time of the year again... Where the, all too-visible, signs of fragility and failure in how we collectively organize our affairs and communities across the globe must be internalized and transformed into positive learnings towards a more resilient future.
Not an easy task: our infatuations, lazy habits, poor information access and poor decisions that make no rational or emotional sense time and again take the upper hand and create exhaustion and a sense of impossibility. Yet there is no option but persist with an agenda that empowers invididuals and organizations to enhance welfare at all levels. After all, everything positive that has been achieved was due to somebody acting.
At Open Risk we will continue to try contibute our small effort towards the open future of risk management. We wish you too, a regenerative break this holiday season and a healthy and productive 2023.
Solstice - a new framework for simulating economic networks
Dear all,
we've started releasing bits and pieces of a new open source tool we are building (the Solstice simulation framework) on our github repo and more is comming over the next weeks. You can check out the first installment of the Solstice manual which describes the analytic framework behind Solstice.
The analytic framework has the following core components:
- A representation of economic entities as property graphs (nodes and edges with associated attributes) as discussed in our earlier white papers (number 8 and 10 in particular)
- A representation of network-wide factors and dynamics drivers as macro variables
- Sources of uncertainty (both macro (system-wide) and idiosyncratic (entity-specific)
- A discrete temporal grid where future states of the economic network and (any) macro variables are modeled
- Network evolution along the temporal grid due to both deterministic and stochastic elements that follows by a variety of risk distributions / models using potentially a combination of model components (satellite models)
- Bottom up composition of network state changes using elementary "system updates"
- Ability to condition on specific realizations of (in particular) macro factors, which emulates scenario analysis / stress testing
- Introducing special entities that interact with the network in systemic ways (e.g. financial intermediaries, sovereigns or regulatory entities) with specialized internal states
- Collection of sampling statistics of network evolution and distillation of useful risk metrics and reports
If you are interested to use and/or contribute to our open source projects please follow and star them on github and (if appropriate) raise issues with ideas or functionality that you would like to see.
Step into python data science the hacktoberfest way. Two small libraries that can help you get started
transitionMatrix
- Estimate transition matrices from historical event data using a variety of estimators
- Manipulate transition matrices (generators, comparisons etc.)
- Visualize event data and transition matrices
- Provide standardized data sets for testing
- Model transitions using threshold processes
- Map credit ratings using mapping tables between popularly used rating systems
concentrationMetrics
- access an exhaustive collection of such indexes and metrics
- perform file input/output in both json and csv formats
- compute indexes with confidence intervals via bootstraping
- visualize using matplotlib
How you can contribute
- fork the repos from the above links
- look at the code / documentation and / try the examples
- find bugs or other issues and raise issues
- think and work on possible extensions, better documentation or any other ideas that fit within the scope of each library
- eventually contribute via a pull request
- get a tree planted in your name, or the Hacktoberfest 2022 t-shirt :-)
New season, new members and new Open Risk White Paper
