Further building out the OpenCPM set of tools, we release version 0.3 of the ConcentrationMetrics 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
In an insightful recent piece, “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the Non-performing loan (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.
Here are four individuals that can look straight into your eyes
They are all different characters from different walks of life. Yet they are united by the power of the #opensource software movement.
Open Risk released version 0.1 of the Transition Matrix Library
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.
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.
Get an Open Risk Academy account and get started with the course here
Maintaining the confidence of market participants, clients, shareholders, regulators and governments is uniquely important for the financial sector. Trust is, quite literally, the real currency. Yet it is a truism that confidence is hard to build up and rather easy to destroy. Why is this so?
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!
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.
Open Risk is proud to be funded by the FIWARE FINODEX accelerator!
Finodex, the European accelerator for ICT projects based on Open Data and FIWARE technologies, has already chosen over one hundred projects via two open calls for proposal.
This week the results of the second call evaluation closed in last September have been published, and 52 projects from a total of 297 have been chosen by a panel of experts. These projects will join the other 49 selected in the first open call.
If you work in financial risk management you will most likely recognize where the following sentence is coming from:
One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks information technology (IT) and data architectures were inadequate to support the broad management of financial risks. This had severe consequences to the banks themselves and to the stability of the financial system as a whole
The Zen of Modeling aims to capture the struggle for risk modeling beauty
The web as we now know it burst first into the open in the early nineties. It certainly did not start among the more socially active classes. It was an invention by and for nerdy CERN physicists, to exchange data about elementary particle experiments. But it wasn’t long before academics figured out additional valuable uses of this technology: You could put your face online, along with a CV. This is how “personal” webpages came to life. Those early home pages were mostly dour affairs, replete with long publication lists.
Our white paper Revisiting simple concentration indexes reviews the definitions of widely used concentration metrics such as the concentration ratio, the HHI index and the Gini and clarify their meaning and relationships.
This page is a summary of a presentation given at the 2014 Autumn TopQuants Meeting, aka, the Open Source Risk Modeling Manifesto.
The current framework of internal risk modeling at financial institutions has had a fatal triple stroke. We saw in quick sequence: market risk, operational risk, and credit risk measurement failures, covering practically all business models.
Financial Risk Modelling has suffered enormous setbacks in recent years, with all major strands of modelling (market, credit, operational risk) proven to have debilitating limitations. It is impossible to imagine a modern financial system that does not make extensive use of risk quantification tools, yet rebuilding confidence that these tools are fit-for-purpose will require significant changes. These need to improve governance, transparency, quality standards and in some areas even the development of completely new strands of modelling.
Overview of the motivation for initiating Open Risk and the solutions being delivered in the current phase.
A Python module of analytic solutions suitable for testing credit portfolio models has been uploaded in the Open Risk github repository
The first batch of open source tools focusing on Credit Portfolio calculations is now available here. The functionality aims to standardize Name Concentration Measurement (concentrationMetrics)