What is Risk Compensation? Risk Compensation is a behavioral model of human attitudes towards risk which suggests that people might adjust their behavior in response to the perceived level of risk. It follows that, depending on the strength of the effect, that it might counteract and even annul the impact of risk mitigation, if the updated attitude and behavior modifies the actual underlying risk Examples of potential risk compensation effects abound A prominent example of potential risk compensation in recent times that established the concept in more formal terms in public policy debates concerned the beneficial role of safety belts in automobiles.
The community mobility reports and OpenCPM In a previous post we introduced the 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. While these reports are unlikely to persist as open data sources, the current availability (as of May 2020) enables providing within OpenCPM a mobility data dashboard that can help draw insights through visualization and statistical analysis.
The community mobility reports and OpenCPM As the COVID-19 pandemic unfolded technology providers (notably Google and Apple) made available to the public aggregated, anonymized insights about human mobility in this crisis period on the basis of smartphone location data. These Community Mobility Reports provide insights into how mobility patterns changed in response to news and policies aimed at combating COVID-19. 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.
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:
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.
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)
A survey of existing definitions When looking up the meaning of risk we are immediately confronted with a surprising situation. There is no satisfying and authoritative general purpose one-liner that we can adopt without second thoughts. Let us start with the standard dictionary definitions: The online Merriam Webster Dictionary defines risk as the possibility of loss or injury The online Cambridge Dictionary opines that risk means the possibility of something bad happening The Oxford English (Concise, Hardcover!
Intro 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.
Making Open Risk Data easier 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 EU (Sustainable Finance) Taxonomy is one such initiative of fundamental significance as it attempts to map at a granular level economic activities with respect to their climate risk mitigation or adaptation potential and create tangible metrics and thresholds to measure progress (the ultimate anti-greenwashing treatment)
Semantic Web Technologies 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.
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).
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. The principal stylistic element is the “double brackets” [[ ]], which is the standard way one adds connectivity between different parts of the wiki in wikitext (the mediawiki markup language).
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. A new entry at the Open Risk Manual aims to provide a brief overview of ongoing projects / initiatives.
Overview of the Julia-Python-R Universe 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. Motivation 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.
The Motivation Risk Management is a vast and ever expanding domain of knowledge. The required skills are of an applied nature, occasionally ery technical and certainly ever-changing. This specialized nature of the knowledge base is not served fully by either the traditional academic literature or classic publications in printed book form. Wikipedia comes frequently to the rescue of risk managers (and will likely continue to do so!), but there is a clear need for a more specialized, open, and collaborative wiki that focuses on risk management.
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: Introduction to Risk Data Review Exploratory Data Analysis using Pandas, Seaborn and Statsmodels
What is a risk taxonomy? There are 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) Everybody knows (or should know!) that the different types of fire (the underlying Risk in this context) cannot be treated the same way because they respond in different ways to the substances used to suppress the fire.
Limit frameworks are fundamental tools for risk management 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. Why do we need limit frameworks? A limit framework is expressing in concrete terms the Risk Appetite of an institution to assume certain risks.