Non-Performing Loans The covid-19 crisis will certainly impact the concentration of Non-Performing Loans but given the special nature of this economic crisis compared (in particular) with the 2008 financial crisis it is unclear how precisely things will evolve. In a previous post and white paper (OpenRiskWP07_022616) we discussed the importance of advancing open and transparent methodologies for managing the risks associated with such credit portfolios. Effective management of NPL is also a top regulatory priority.
The community mobility reports and OpenCPM In a previous post we introduced the new OpenCPM functionality that integrates COVID-19 community mobility data (currently from Google). The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential. While these reports are unlikely to persist as open data sources, the current availability (as of May 2020) enables providing within OpenCPM a mobility data dashboard that can help draw insights through visualization and statistical analysis.
The community mobility reports and OpenCPM As the COVID-19 pandemic unfolded technology providers (notably Google and Apple) made available to the public aggregated, anonymized insights about human mobility in this crisis period on the basis of smartphone location data. These Community Mobility Reports provide insights into how mobility patterns changed in response to news and policies aimed at combating COVID-19. The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
Making Open Risk Data easier In an earlier blog post we discussed the promise of Open Risk Data and how the widespread availability of good information that is relevant for risk management can substantially help mitigate diverse risks. The list of Open Risk Data providers, particularly from public sector, keeps increasing and we are aiming to document all available datasets in the dedicated page of the Open Risk Manual.
Semantic Web Technologies The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies. In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things.
The motivation for federated credit risk models Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc).
In two new Open Risk Academy courses we figure step by step how to use python to work to review risk data from a data quality perspective and how to perform exploratory data analysis with pandas, seaborn and statsmodels: Introduction to Risk Data Review Exploratory Data Analysis using Pandas, Seaborn and Statsmodels
Open Source Securitisation Motivation After the Great Financial Crisis securitisation has become the poster child of a financial product exhibiting complexity and opaqueness. The issues and lessons learned post-crisis were many, involving all aspects of the securitisation process, from the nature and quality of the underlying assets, the incentives of the various agents involved and the ability of investors to analyze the products they invested in. While the most egregious complications involved various types of re-securitisation and/or the interplay of structured credit derivatives undoubtedly even “vanilla” securitisation structure has a considerable amount of business logic.
Visualization of large scale economic data sets Economic data are increasingly being aggregated and disseminated by Statistics Agencies and Central Banks using modern API’s (application programming interfaces) which enable unprecedented accessibility to wider audiences. In turn the availability of relevant information enables more informed decision making by a variety of actors in both public and private sectors. An excellent example of such a modern facility is the European Central Bank’s Statistical Data Warehouse (SDW), an online economic data repository that provides features to access, find, compare, download and share the ECB’s published statistical information.
The ESMA Securitisation Templates are now fully documented at the Open Risk Manual. Users can browse, search and cross-reference with the rest of the knowledge base. Category Browsing The ESMA Templates Categories are part of both the Securitisation category and the Information Technology Category. Each one of the templates and each one of the sections within a template forms its own category. In turn all the fields in that section of the template are part of that category
The challenge with historical credit data Historical credit data are vital for a host of credit portfolio management activities: Starting with assessment of the performance of different types of credits and all the way to the construction of sophisticated credit risk models. Such is the importance of data inputs that for risk models impacting significant decision making / external reporting there are even prescribed minimum requirements for the type and quality of necessary historical credit data.
Release of version 0.4.1 of the transitionMatrix package focuses on stressing transition matrices Further building the open source OpenCPM toolkit this realease of transitionMatrix features: Feature: Added functionality for conditioning multi-period transition matrices Training: Example calculation and visualization of conditional matrices Datasets: State space description and CGS mappings for top-6 credit rating agencies Conditional Transition Probabilities The calculation of conditional transition probabilities given an empirical transition matrix is a highly non-trivial task involving many modelling assumptions.
Release of version 0.4 of the transitionMatrix package Further building the open source OpenCPM toolkit this realease of transitionMatrix features: Feature: Added Aalen-Johansen Duration Estimator Documentation: Major overhaul of documentation, now targeting ReadTheDocs distribution Training: Streamlining of all examples Installation: Pypi and wheel installation options Datasets: Synthetic Datasets in long format Enjoy!
Release of version 0.4 of the Concentration Library adds Geographic / Industrial concentration indexes Further building out the OpenCPM set of tools, we release version 0.4 of the Concentration Library, a python library for the computation of various concentration, diversification and inequality indices. The below list provides documentation URL’s for each one of the implemented classic indexes (the Hoover index is a new addition in this release Atkinson Index Hoover Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index An important new direction that appears first in this release is the introduction of indexes that measure geographical and industrial concentration.
Representing economic activity using pictograms Visualization can produce significant new insights when applied to quantitative data. It is currently undergoing a renaissance that mirrors other developments in computing and data science. Sophisticated open source libraries such as d3.js or matplotlib, to name but a couple, are enabling an ever wider range of users to distill valuable information from the avalanche of data being produced. Yet when it comes to visualizing data that relate to abstract concepts it can be quite difficult to find an appropriate grammar to express the quantitative context.
Extending the Open Risk API to include the EBA Portfolio Data Templates The Open Risk API provides a mechanism to integrate arbitrary collections of risk data and risk modelling resources in the context of assessing and managing financial risk. It is based on two key technologies of the modern Web, RESTful architectures and Semantic Data. OpenCPM, the credit portfolio management platfrom we launched recently fully integrates the latest versions of the Open Risk API.
Credit Portfolio Management in the IFRS 9 / CECL and Stress Testing Era The post-crisis world presents portfolio managers with the significant challenge to asimilate in day-to-day management the variety of conceptual frameworks now simultaneously applicable in the assessment of portfolio credit risk: The first major strand is the widespread application of regulatory stress testing methodologies in the estimation of regulatory risk capital requirements The second major strand is the introduction of new accounting standards (IFRS 9 / CECL) for the measurement and disclosure of expected credit losses
Release of version 0.3 of the Concentration Library Further building out the OpenCPM set of tools, we release version 0.3 of the Concentration Library. This python library for the computation of various concentration, diversification and inequality indices. The below list provides documentation URL’s for each one of the implemented indexes Atkinson Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index The image illustrates a simple use of the library where the HHI and Gini indexes are computed and compared for a range of randomly generated portfolio exposures.
First Release: OpenCPM NPL Database Further building out the OpenCPM set of tools, we took the Eupean Banking Authority’s recommended Non-Performning Loan templates and created an #opensource production grade database for capturing NPL portfolio data sets. Motivation for Building an open source database based on EBA’s Standardized NPL Templates In a recent insightful piece “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that perpetuate the NPL problem.
Open Risk released version 0.1 of the Transition Matrix Library Motivation State transition phenomena where a system exhibits stochastic (random) migration between well defined discrete states (see picture below for an illustration) are very common in a variety of fields. Depending on the precise specification and modelling assumptions they may go under the name of multi-state models, Markov chain models or state-space models. In financial applications a prominent example of phenomena that can be modelled using state transitions are credit rating migrations of pools of borrowers.