Supply Chain Finance

The Game of Life With Macroeconomic Stimulus

The Game of Life With Macroeconomic Stimulus

Agent-based models is a major class of simulation models, with many potential applications in economics and finance

Reading Time: 7 min.
Agent-Based Models: The origins and early years 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. A cellular automaton is a particular class of ABM. It is a discrete dynamical model used and studied in a variety of fields: computer science, mathematics, physics, complexity science, theoretical biology among others.
New Open Risk Academy Course: Simulation of Credit Contagion

New Open Risk Academy Course: Simulation of Credit Contagion

Reading Time: 2 min.
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

Connecting the Dots: Economic Networks as Property Graphs

Reading Time: 0 min.
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)
Federated Credit Risk Models

Federated Credit Risk Models

Reading Time: 4 min.
The motivation for federated credit risk models: 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).