The purpose of our blog is to provide updates on important news and developments around Open Risk and a running commentary on external developments related to our mission.
You can view posted articles either from the front-page or by selecting the relevant post category or tag or tag from the right column. In our archive page blog entries are grouped chronologically.
In a previous post we introduced new OpenCPM functionality that integrates COVID-19 community mobility data (currently from Google). The reports chart movement trends over time by geography, across different categories of places such as retail and recreation, groceries and pharmacies, parks, transit stations, workplaces, and residential.
As the COVID-19 pandemic unfolded technology providers (most notably Google and Apple) made available to the public aggregated and anonymized data about human mobility in the crisis period (on the basis of smartphone location data). These Community Mobility Reports provide insights into how mobility patterns changed in response both to pandemic news and policies aimed at combating COVID-19.
This course is a CrashProgram (short course) introducing the GeoJSON specification for the encoding of geospatial features. The course is at an introductory technical level. It requires some familiarity with data specifications such as JSON and a very basic knowledge of Python
Agent-based models is a major class of simulation models, with many potential applications in economics and finance
According to Wikipedia an agent-based model (ABM) is
ABM: class of computational models for simulating the actions and interactions of autonomous agents (both individual or collective entities such as organizations or groups) with a view to assessing their effects on the system as a whole.
This course is an introduction to the concept of credit contagion. It covers the following topics:
We develop a quantitative framework that approaches economic networks from the point of view of contractual relationships between agents (and the interdependencies those generate). The representation of agent properties, transactions and contracts is done in the context of a property graph.
A typical use case for the proposed framework is the study of credit networks.
Why is Risk so poorly defined?
When looking up the meaning of Risk we are confronted with a surprising situation. There is no satisfying and authoritative general purpose one-line definition that we can adopt without second thoughts. Let us start with the standard dictionary definitions:
FOSDEM means Free and Open Source Software Developers European Meeting
FOSDEM is a non-commercial, volunteer-organized European event centered on free and open-source software development. It is aimed at developers and anyone interested in the free and open-source software movement. It aims to enable developers to meet and to promote the awareness and use of free and open-source software. FOSDEM is held annually since 2001, usually during the first weekend of February, at the Université Libre de Bruxelles Solbosch campus in the southeast of Brussels, Belgium. The history of FOSDEM is neatly available at Wikipedia, while the current conference (2020) website is available here.
We introduce an online database that allows the (relatively) easy publication of structured risk data
In an earlier blog post we discussed the promise of Open Risk Data and how the widespread availability of good information that is relevant for risk management can substantially help mitigate diverse risks.
The list of Open Risk Data providers, particularly from public sector, keeps increasing and we are aiming to document all available datasets in the dedicated page of the Open Risk Manual.
The integration of climate risk and broader sustainability constraints into risk management is a monumental task and many tools are still lacking. Yet there is strong support and bold initiatives from policy bodies and an increasing focus from the private sector side.
The Risk Model Ontology is a framework that aims to represent and categorize knowledge about risk models using semantic web information technologies.
In principle any semantic technology can be the starting point for a risk model ontology. The Open Risk Manual adopts the W3C’s Web Ontology Language (OWL). OWL is a Semantic Web language designed to represent rich and complex knowledge about things, groups of things, and relations between things. OWL is a computational logic-based language such that knowledge expressed in OWL can be exploited by computer programs, e.g., to verify the consistency of that knowledge or to make implicit knowledge explicit. OWL documents, known as ontologies, can be published in the World Wide Web and may refer to or be referred from other OWL ontologies. OWL is part of the W3C’s Semantic Web technology stack, which includes RDF, RDFS, SPARQL, etc
Federated learning is a machine learning technique that is receiving increased attention in diverse data driven application domains that have data privacy concerns. The essence of the concept is to train algorithms across decentralized servers, each holding their own local data samples, hence without the need to exchange potentially sensitive information. The construction of a common model is achieved through the exchange of derived data (gradients, parameters, weights etc). This design stands in contrast to traditional model estimation where all data reside (or are brought into one computational environment).
We have updated the logo for the Open Risk Manual.
The new logo aims to make more explicit both the inspiration that the Open Risk Manual project draws from the trail-blazing Wikipedia initiative (and increasing collection of associated Wikimedia projects) and the reliance on the open source ecosystem of software and tools, including the mediawiki software and the important semantic mediawiki extension.
In the European Union there are several ongoing large scale legislative and regulatory projects that transform the context within which individual, firms and the public sector interact economically. While financial and regulatory reform is an ongoing process in all jurisdictions globally, the size and supra-national nature of the European Union makes those projects particularly interesting.
We introduce a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, the trio sometimes abbreviated as Jupyter
A new Open Risk Manual entry offers a side-by-side review of the main open source ecosystems supporting the Data Science domain: Julia, Python, R, sometimes abbreviated as Jupyter.
A large component of Quantitative Risk Management relies on data processing and quantitative tools (aka Data Science ). In recent years open source software targeting Data Science finds increased adoption in diverse applications. The overview of the Julia-Python-R Universe article is a side by side comparison of a wide range of aspects of Python, Julia and R language ecosystems.
In two new Open Risk Academy courses we figure step by step how to use python to work to review risk data from a data quality perspective and how to perform exploratory data analysis with pandas, seaborn and statsmodels:
There are various formal definitions of risk taxonomies (and we will go over those below), but it might be useful to first look at a very intuitive example of a risk taxonomy: the classification of fire hazards (also known as fire classes)
A Limit Framework is a set of policies used by financial institutions (or other firms that actively assume quantifiable risks) to govern in a quantitative manner the maximum risk exposure permitted for an individual, trading desk, business line etc.
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.
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.