Blog

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.

Motivation for the comparison

A large component of risk management relies on data processing and quantitative tools. In turn, such information processing pipelines and numerical algorithms must be implemented in computer systems.

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.

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:

1. Feature: Added functionality for conditioning multi-period transition matrices
2. Training: Example calculation and visualization of conditional matrices
3. Datasets: State space description and CGS mappings for top-6 credit rating agencies

Release of version 0.4 of the transitionMatrix package

Further building the open source OpenCPM toolkit this realease of transitionMatrix features:

1. Feature: Added Aalen-Johansen Duration Estimator
2. Documentation: Major overhaul of documentation, now targeting ReadTheDocs distribution
3. Training: Streamlining of all examples
4. Installation: Pypi and wheel installation options
5. Datasets: Synthetic Datasets in long format

Enjoy!

Is the IFRS 9 or CECL standard more volatile? Its all relative

Objective

In this study we compare the volatility of reported profit-and-loss (PnL) for credit portfolios when those are measured (accounted for) following respectively the IFRS 9 and CECL accounting standards.

The objective is to assess the impact of a key methodological difference between the two standards, the so-called Staging approach of IFRS 9. There are further explicit differences in the two standards. Importantly, given the standards are not prescriptive, it is very likely that there will be material differences in interpretation and implementation of the principles (for example on the nature and construction of scenarios). In this study we perform a controlled comparison adopting a ‘ceteris-paribus’ mentality: We assume that all other implementation details are similar and we focus on the impact of the Staging approach.

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.

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.

Credit Portfolio PnL volatility under IFRS 9 and CECL

Objective

We explore conceptually a selection of key structural drivers of profit-and-loss (PnL) volatility for credit portfolios when profitability is measured following the principles underpinning the new IFRS 9 / CECL standards

Methodology

We setup stylized calculations for a credit portfolio with the following main parameters and assumptions:

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.

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 While both Regulatory Stress Testing and IFRS 9 / CECL accounting require investment in analytic capabilities and provide unique new insights, both are aimed at satisfying evolving prudential or investor disclosure requirements. Neither is designed to help credit portfolio managers analyse and steer their portfolios in the bottom-up fashion that is an essential part their mandate.

The above developments are overlaid into pre-existing conceptual and practical frameworks such as

The new IFRS 9 financial reporting standard

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.

What are European Safe Bonds?

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.

Release of version 0.3 of the ConcentrationMetrics Library

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

Motivation for Building an open source database based on EBA’s Standardized NPL Templates

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.

Data Scientists Have No Future

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!

Four individuals that can look straight into your eyes

Here are four individuals that can look straight into your eyes

• Torvalds developed the #linux operating system, the software engine now powering anything from the tiniest #raspberrypi to the scariest supercomputer. Humanity’s best guarantee that the digital era remains an equal playing field
• Mullenweg developed the #wordpress blogging platform. Gave voice and content ownership to millions of digital authors making him the closest to the Gutenberg of our era
• Dougiamas developed #moodle, the world’s digital Academy. Capturing millenia of teacher’s experience into one powerful #elearning system makes him an educational innovator on a par with the Greek masters of antiquity
• Wales developed #wikipedia. It is not only the world’s digital encyclopedia and top 5 website. It is the most blinding evidence that tech enabled mass collaboration will change the human condition

They are all different characters from different walks of life. Yet they are united by the power of the #opensource software movement.

Machine Learning Ballyhoo

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.

If programming languages were human languages which one would be which?

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 (tonque-in-cheek mind you!) compilation of a mapping between programming languages and human languages. Suggestions / corrections are welcome via the feedback button