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.
ESMA Securitisation Templates are now documented at the Open Risk Manual: 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.
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.
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 help perpetuate the NPL problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.
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.
Loan Level Templates Using Python: In this Open Risk Academy course we figure step by step how to use python to work with Loan Level Templates, using the ECB SME template as an example. Overview of the loan level template Manipulating spreadsheets with Python The Python Dictionary Organization of Portfolio Data Generating Test Portfolios Get an Open Risk Academy account and get started with the course here
How much digital bank can we fit in a 50 euro bill? Much has been said about the impact of Big Data and high-end GPU computing on the provision of digital financial services. At Open Risk we wanted to explore the boundary of what is possible at the diametrically opposite end of the cost spectrum: What is the absolutely minimum cost for providing digital financial services? . In this post we begin the journey of finding out the answer to that question and it promises to be fascinating!
StatsNews: Aggregating Economic Open Data news: StatsNews Version 1.3 of the RegNews Aggregator includes a separate stream of economic opendata news releases. Regnews is a web app developed by Open Risk to assist with keeping abreast with diverse financial regulatory news releases and publications. The app data are directly derived from the published regulatory RSS sheets (NB some are not conforming to RSS standards). If in doubt please refer to the original feeds (links provided).
Fintech Risk Events: Fintech Risk Events is an open catalog of observed and publicized operational failures of fintech business models. The catalog aims to document, in due course, such events reasonably accurately, to allow risk managers understand the (potentially new) vulnerabilities of new financial services models. Scope The scope of the operational risk database is Fintech companies. By that we mean newly established financial services providers that operate exclusively via new (digital) platforms and are (mostly) unregulated.
Regnews: Financial Regulatory News Aggregation Version 1.2: The #RegNews Aggregator is a web app developed by Open Riskto assist with keeping abreast with diverse financial regulatory news releases and publications. The app data are directly derived from the published regulatory RSS sheets (NB some are not conforming to RSS standards). If in doubt please refer to the original feeds (links provided). Copyright of the publications is with the respective authoring institutions.
Visualizing the risk management of the future: How do we communicate risk insights? The information tools used by risk managers to communicate insights have been transformed multiple times over the ages. In each era we have adopted existing technologies, but we also created demand for new technologies. Our era is no exception. To understand where we are going, we need to understand where we are coming from. So lets briefly recap our industrious past before we peer briefly into the visually exciting crystal ball.
Risk Management Internship: In finance, it’s the best of times, it’s the worst of times It is a special moment to start a career in financial services. We are walking amid the ruins of the previous financial order. Fallen banks, broken markets, negative interest rates, shell-shocked economies and discredited theoretical assumptions. We see the enormous cost and impact to the welfare of society of a less than perfect financial system which has not kept pace with the advancement of our general knowledge and technical capabilities in most other domains.