tailRisk is a C++ library for the calculation of various tail risk measures. Tail Risk is a core concept in Quantitative Risk Management , relevant in particular in Market Risk and Credit Risk Management. Tail Risk is both an informal term denoting unusually occurring tail events and a more precise term denoting concrete classes of risk measures
Connecting the Dots, Tensor Representations of Activitypub Networks
ActivityPub is a technical specification towards decentralized (more precisely, federated) social networking (termed the Fediverse) based upon the exchange of ActivityStreams messages that follow the Activity Vocabulary. The ActivityPub proposal has been standardized and published by the W3C and has motivated the design of several federated social networking systems.
We discuss the relation of economic input-output models with graph theory and networks
The objective of the course is to discuss the relation of economic input-output models with graph theory and networks.
In this second Open Risk White Paper on "Connecting the Dots" we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures.
In this second Open Risk White Paper on Connecting the Dots we examine measures of concentration, diversity, inequality and sparsity in the context of economic systems represented as network (graph) structures. We adopt a stylized description of economies as property graphs and illustrate how relevant concepts can represent in this language. We explore in some detail data types representing economic network data and their statistical nature which is critical in their use in concentration analysis. We proceed to recast various known indexes drawn from distinct disciplines in a unified computational context.
We explore a variety of distinct uses of graph structures in data science. We review various important graph types and sketch their linkages and relationships. The review provides an operational guide towards a better overall understanding of those powerful tools
Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niches to become mainstream models for describing and interpreting various phenomena. The objective of the course is to review various important graph types as they are increasingly explored in data science and sketch their linkages and relationships (a graph of graphs!).
We discuss the Python language built-in data types and a visualization that organizes them according to key attributes
Data science is about data, but data are not simple and tame beasts. They have character and attitude, which can cause a lot of friction between them and the data scientist. There is a lot of sweat and tears involved when confronting data, but data scientists can do worse than know how to handle in particular Data Type quirks. Namely, a good fraction of data science involves not modelling data, not transforming data, not even cleaning data but simply goading data around the right containers, providing them with the right stage that fits their character. Getting the data type wrong is many a source of both spectacular bugs and hidden, insidious application errors.
We visualize global mobility patterns over a full year of pandemic induced lockdowns
As we move into February 2021 the world will be experiencing almost a year under pandemic conditions. This has markedly changed behavioral patterns of human mobility across the board. One major difference with previous pandemics is that through the use of a variety of digital technologies and new data collection channels we know have an unprecedented view of those changing mobility patterns.
We introduce a global mobility index that averages Google mobility data across all available countries (weighting by population) to provide an overall view of how the pandemic has influenced human mobility
In previous posts (here, and here) we introduced new Open Risk Dashboard functionalities that integrate COVID-19 community mobility data (currently focusing on the datasets provided by Google).
As a reminder, these reports chart over time human mobility trends collected from mobile geolocation data. The granularity is by geography and across different categories of places / activities such as retail and recreation areas, groceries and pharmacies, parks, transit stations, workplaces, and residential areas. Through these data sets we have available (for the first time in history) an overall quantitative view of global mobility (and Mobility Risk )!
Is Global Debt Truly Astronomical?
The notion of astronomical numbers and figures is quite frequently seeping in everyday language when large quantities of something are encountered in “normal” life. The strict definition of astronomical is obviously something of, or relating to, astronomy and astronomical observations but in common usage it also denotes something enormously or inconceivably large. This is, of course, because astronomical figures are inconceivably large!
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. In the latest release of transitionMatrix we include an example of a log-scale version of Sankey
Sankey Diagrams are a type of flow diagram composed of interconnected arrows. The width of the arrows is proportional to the flow rate. Sankey diagrams are often used in physical sciences (physics, chemistry, biology) and engineering but also in economics. They can be used to represent the relative role and significance of various inputs and outputs in a given process.
We explore a variety of distinct ways to visualize the same simple dataset. The post is an excursion into the fundamentals of visualization - a partial deconstruction of the process that highlights some common techniques and associated issues.
This course is a deep-dive into the structure of visualizations, in particular visualizations of timeseries data. The course is now live at the Academy.
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
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.
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.
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.
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.
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.