Our blog provides updates on important news and developments around Open Risk, and a running commentary on external developments when related to our mission. You can view posted articles either here in reverses chronological order, from the front-page or by selecting the relevant post tag from the right column.
Our archive page groups blog entries chronologically by year.
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.
The post-crisis world presents portfolio managers with the significant challenge to assimilate in day-to-day management the variety of conceptual frameworks now simultaneously applicable in the assessment of portfolio credit risk:
The above developments are overlaid into pre-existing conceptual and practical frameworks such as
IFRS 9 (and the closely related CECL) is a brand new financial reporting standard developed and approved by the International Accounting Standards Board (IASB).
Strictly speaking IFRS 9 concerns only the accounting and reporting of financial instruments (e.g. bank loans and similar credit products). Yet the introduction of the IFRS 9 standard has significant repercussions beyond financial reporting, and touches e.g., bank risk management as well. This is prompted by the fact that the framework requires embedding forward looking risk assessments in the measurement of the value of credit assets currently on the balance sheet.
While the creation of the eurozone was a landmark of the European integration process, the financial crisis highlighted that the eurozone remains an incomplete design which can lead to unpredictable and adverse situations in the event of a (the) next major crisis. One of the key such incompleteness features of the current eurozone architecture is that it does not have a truly risk-free (safe) euro debt instrument: one that continues being serviced (avoids a default event) at virtually any point in time and state of the world, no matter how severe.
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.
The working definition of a Data Scientist seems to be in the current overheated environment:
doing whatever it takes to get the job done in a digital #tech domain that we have long neglected but which is now coming back to haunt us!
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.
Are you getting a bit tired with all the machine learning ballyhoo?
You can blame it all on a German mathematician(*), Carl Friedrich Gauss, who started the futuristic mega-trend back in 1809: He showed us how to train a straight line to pass nicely through a cloud of unruly, scattered data points. To find, in effect, a path of least embarrassment.
Most developers know (or get to know quickly once they join a team) that programming languages are as much about communicating with other developers as they are about instructing the computer. Which raises the interesting question: If programming languages were human languages which one would be which? Here is a (tongue-in-cheek mind you!) compilation of a mapping between programming languages and human languages. Suggestions / corrections are welcome via the feedback button
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.
At Open Risk we are firm believers in balancing art and science when developing quantitative risk tools. The introduction of the IFRS 9 and CECL accounting frameworks for reporting credit sensitive financial instruments is a massive new worldwide initiative that relies in no small part on quantitative models. The scope and depth of the program in comparison with previous similar efforts (e.g. Basel II) suggests that much can go wrong and it will take considerable time, iterations, communication and training to develop a mature toolkit that is fit-for-purpose.
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
What are the guiding principles for a viable open source operational risk model (OSORM)? Such a framework should:
From Yahoo’s massive email data leaks, to Equifax’s exposing of sensitive data for a large segment of the US population, to Apple’s resisting the bypassing the security features of the iPhone, not a week goes by without some alarming piece of news around data privacy.
In recent decades we have been collectively spared the haunting images and existential anxiety provoked by the sight of detonating nuclear weapons for testing purposes - not to mention the increased levels of radiation in the atmosphere and other side-effects. This achievement is largely thanks to a series of treaties to control nuclear bomb testing that have been signed and enforced by most (unhappily not all) countries worldwide.
There is a legend that every time a data set is released into the open, somewhere dies a black swan
Well, it is not a true legend. Legends take centuries of oral storytelling to form. In our frantic age, dominated by the daily news cycle and viral twitter storms, legends have been replaced by the rather more short-lived memes and #hashtags.
Physics is one of those remarkably successful branches of science that have helped shape the modern era. Let your gaze drop on any man made gadget in your surroundings and its likely that its working principles go back to a fundamental discovery in physical science that dates back no more than two hundred years or so.
Ever since the scary turbulence of the Great Financial Crisis it has been instructive and illuminating to compare the travails of the financial industry with the state of other industries, especially those more down to earth, also known as real world industries.
The decision by the citizens of the United Kingdom to vote against continuing membership of the European Union (#brexit) will have wide ranging repercussions on many facets of the European (and even global) economic system. As of early 2017, we see this trend further reinforced by a new US administration that aims to revisit a wide range of policy choices, including aspects of financial services regulation. While the aftershocks of these events still reverberate, it seems that we can posit quite confidently that: