### Input Output Models as Graph Networks

We discuss the relation of economic input-output models with graph theory and networks

We discuss the relation of economic input-output models with graph theory and networks

Motivation Fig 1. An economic network as a graph. The economy is a complex tangle of various agents that interact via transactions (sales and purchases) and contracts (lending, investing). In recent times more and more techniques from graph theory and network science are brought to bear on economic analysis. On the other hand, ever since the seminal contributions of Leontief, Input-Output Models (IO) have been widely used to describe economic relationships between economic actors (e.

A DeepDive Course into using Python to work with Input-Output Models

What are Input-Output Models? Environmentally Extended Multi-Regional Input-Output (EE MRIO) tables describe economic relationships of economic actors (e.g. industrial sectors) operating within and between regions and their environmental repercussions.
An EE MRIO augments the more basic and historically first proposed Input-Output Models (IO) with additional datasets and/or modeling assumptions in order to provide insights into the environmental foorprint of economic activity. Presently, the emphasis on negative externalities of economic activity (e.

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.

Concentration, diversity, inequality and sparsity in the context of economic 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. 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 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 seem to be everywhere in modern data science: Graphs (and the related concept of Networks) have emerged from a relative mathematical and physics niche to an ubiquitous model for describing and interpreting various phenomena. While the scholarly account of how this came about would probably need a dedicated book, there is no doubt that one of the key factors that increased the visibility of the graph concept is the near universal adoption of digital social networks.

Using a simplified version of the rules of the US Electoral College system we illustrate how the use of Monte Carlo techniques allows exploring systems that show combinatorial explosion

The role of simulation in risk management and decision support A Simulation is a simplified imitation of a process or system that represents with some fidelity its operation over time. In the context of risk management and decision support simulation can be a very powerful tool as it allows us to assess potential outcomes in a systematic way and explore what-if questions in ways that might otherwise be not feasible. Simulation is used when the underlying model is too complex to yield explicit analytic models (An analytic model is one can be “solved” exactly or with standard numerical methods, for example resulting in a formula).

In this Open Risk White Paper, the first in a series of three, we introduce and explore the concept of federated credit systems as a potentially interesting domain for the application of federated analysis and federated learning.

Federated Credit Systems, Part I: Unbundling the Credit Provision Business Model: 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.

Course Content: 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
Who Is This Course For: The course is useful to:
Any developer or data scientist that wants to work with geospatial features encoded in the geojson format How Does The Course Help: Mastering the course content provides background knowledge towards the following activities:

Course Content: This course is an introduction to the concept of credit contagion. It covers the following topics:
Contagion Risk Overview and Definition Various Contagion Types and Modelling Challenges The Simple Contagion Model by Davis and Lo Supply Chains Contagion Sovereign Contagion Who Is This Course For: The course is useful to:
Risk Analysts across the financial industry and beyond Risk Management students Quantitative Risk Managers developing or validating risk models How Does The Course Help: Mastering the course content provides background knowledge towards the following activities:

Connecting the Dots: Economic Networks as Property Graphs: 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 a context of a property graph.
A typical use case for the proposed framework is the study of credit networks.
You can find the white paper here: (OpenRiskWP08_131219)

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 0.3 of the Concentration Library: Further building out the OpenCPM set of tools, we release version 0.3 of the Concentration 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
Atkinson Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index The image illustrates a simple use of the library where the HHI and Gini indexes are computed and compared for a range of randomly generated portfolio exposures.