Dear Academy users, its been a year for the history books, a year most of us would probably want to wish away. But it has also been a year of reflection, of lessons re-learned, of finding hope, purpose and community. I wish you all for 2021 health, personal and professional growth, deeper and more meaningful connections with each other. I hope that the resources and tools we create at Open Risk will be a positive contribution to your journey! Best, Philippos

People frequently use the term 'astronomical' to describe global debt levels, but is this factually true? In this commentary we discuss whether global debt can be compared with true astronomical objects! In the process we touch various issues around the design and function of monetary systems and the behavioral implications of such designs. Enjoy!

What do risk managers do for a living? A new semantic web ontology and a collection of articles at the Open Risk Manual aim to put some machine readable structure around the question!

• Blog Post
• Live Ontology
• Job Description Catalog

Enjoy!

Monte Carlo simulation can be used when the model we want to understand is too complex to yield explicit analytic models. Complexity in a system may arise from many underlying architectural characteristics. Through the use of random numbers simulation aims to address the curse of combinatorial explosion that plagues the analysis of complex systems. In this new blog post we go over a simple simulation exercise where we tackle the challenge that with 51 states and a specific allocation of the 538 electoral votes there are many permutations ("paths to victory") that lead of the desired grand total of 270 or more votes. Read more about how this simulation was setup (spoiler alert - there is nothing politically relevant here!) what we might expect to see on the basis of the law of large numbers and the central limit theoreme and watch an animated video of how the simulation statistics starts accumulating! Enjoy

As an architectural design and information technology approach, federation has received increased attention in domains such as the medical sector (under the name federated analysis), in official statistics (under the name trusted data) and in mass computing devices (smartphones), under the name federated learning. In this (the first of series of three) white paper, we introduce and explore the concept of federated credit systems.

We introduce a number of fundamental business entities (sub-units) and their associated functions and discuss the underlying business models. We discuss, in particular, how and why they exchange data and metrics and the key risk management challenges of each. Finally, we sketch current architectures for credit information sharing with an overture to the new possibilities opening up with federation architectures.

• You can find the white paper here
• Get involved in open source software development of federated credit systems.

The latest release of transitionMatrix library (0.4.7) expands the documentation and example set. The Academy Course PYT26038 (Analysis of Credit Migration using Python TransitionMatrix) that takes you step by step through the basics of using the library. There are five steps (course sections) that cover the main functionality of the TransitionMatrix package, with applications to a variety of synthetic and real credit data. The examples used in the course are also available in the github repository (for those who do not like to type :-) A little bonus: Sankey diagrams are very useful for the visualization of flows, especially when there is a conserved quantity. They can be tricky when some of the flows are much smaller than others such as is typically the case with credit rating transitions. In the latest release of transitionMatrix we include an example of a log-scale version of Sankey