What do we mean by credit data? This post is a discussion around mathematical terminology and concepts that are useful in the context of working with credit data, taking us from network graph representations of credit systems to commonly used reference data sets
Definition of Credit Data What do we mean by credit data? For our purposes Credit Data is any well-defined dataset that has direct applications in the assessment of the Credit Risk of an individual or an organization, or, more generally, a dataset that allows the application of data driven Credit Portfolio Management policies. The appearance of credit data is quite familiar to practitioners: A spreadsheet, or a table in a database, with a number of columns and rows full of all sorts of information about borrowers and loans.
Solstice is a flexible open source economic network simulator. Its primary outcomes are quantitative analyses of the behavior of economic systems under uncertainty. In this post we provide a first overall description of Solstice to accompany the first public release.
Modeling economic networks and their dynamics. Economic networks are the primary abstractions though which we can conceptualize the state (condition) and evolution of economic interactions. This simply reflects the fact that human economies are quite fundamentally systems of interacting actors (or nodes in a network) with transient or more permanent relations between them.
In practice the network character of an economy is frequently suppressed or under-emphasized and does not play a particularly important role.
In this blog post we discuss a number of financial terms whose precise meaning is frequently intentionally or unintentionally obscured. As a result those terms may, like a Rorschach Blot, mean different things to different people. Unlike this famous psychological test, ambiguity in weighty financial matters can have adverse consequences.
According to wikipedia Conflation is the merging of two or more sets of information, texts, ideas, opinions, etc., into one, often in error. This may lead to misunderstandings, as the fusion of distinct subjects might obscure analysis of relationships which are emphasized by contrasts. Why does conflation happen in the first place? There are several possible factors which in some contexts may be co-existing and overlapping:
gratuitous (over)simplification driven by laziness or habit literacy gaps in either the originator or the receiver of information an objective to frame, mislead or otherwise be economical with the truth In this blog post we discuss a number of interrelated financial terms whose precise meaning is frequently intentionally or unintentionally obscured.
Risk management means different things to different people. In this post we explore some truths about professional risk management that highlight both the challenges it is facing as a discipline and the significant role it can play towards a sustainable future
9 things they do not tell you about risk management Risks don’t fall from the sky, they are generated by other people Informal Risk Management was practiced by individuals since time immemorial. This is the domain of intuitive decision-making, assessing a situation on the spot and taking immediate action to avoid obvious risks. Over aeons empirical risk management has collected a treasure of heuristics, rules of thumb and colorful Risk Management One-Liners such as: There is never only one cockroach.
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.
Summary The Open Risk Academy course NPL270672 is a CrashCourse introducing the EBA NPL Templates.
Content: We start with the motivation for the templates and the domain of credit data (to which NPL data belongs). We discuss three core classes that capture the essence of lending operations from a lenders point of view (Counterparty, Loan, Collateral). Next we explore classes that capture events in the lending relationship lifecycle (which we term NPL Scenarios).
The Non-Perfoming Loan Ontology The Non-Performing Loan Ontology is a framework that aims to represent and categorize knowledge about non-performing loans using semantic web information technologies. Codenamed NPLO, it codifies the relationship between the various components of a Non-Performing Loan portfolio dataset.(NB: Non-performing loans are bank loans that are 90 days or more past their repayment date or that are unlikely to be repaid, for example if the borrower is facing financial difficulties).
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.
Open Risk White Paper 9: Federated Credit Systems, Part I: Unbundling The Credit Provision Business Model In this (the first of series of three) white paper, we introduce and explore the concept of federated credit systems. We review the rapidly developing fields of Federated Analysis and Federated Learning as already actively studied in the domains of medicine and consumer computing devices. This forms the backdrop for understanding the potential and challenges of applying similar concepts in finance and more particular credit provision.
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)
Open Risk White Paper 8: 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 context of a property graph. A typical use case for the proposed framework is the study of credit networks.
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).
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.
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 0.4.1 of the transitionMatrix package focuses on stressing transition matrices: Further building the open source OpenCPM toolkit this realease of transitionMatrix features:
Feature: Added functionality for conditioning multi-period transition matrices Training: Example calculation and visualization of conditional matrices Datasets: State space description and CGS mappings for top-6 credit rating agencies Conditional Transition Probabilities The calculation of conditional transition probabilities given an empirical transition matrix is a highly non-trivial task involving many modelling assumptions.
Release of version 0.4 of the transitionMatrix package: Further building the open source OpenCPM toolkit this realease of transitionMatrix features:
Feature: Added Aalen-Johansen Duration Estimator Documentation: Major overhaul of documentation, now targeting ReadTheDocs distribution Training: Streamlining of all examples Installation: Pypi and wheel installation options Datasets: Synthetic Datasets in long format Enjoy!
Credit Portfolio PnL volatility under IFRS 9 and CECL: Objective We explore conceptually a selection of key structural drivers of profit-and-loss (PnL) volatility for credit portfolios when profitability is measured following the principles underpinning the new IFRS 9 / CECL standards
Methodology We setup stylized calculations for a credit portfolio with the following main parameters and assumptions:
A portfolio of 200 commercial loans of uniform size and credit quality Maturities extending from one to five annual periods A stylized transition matrix producing typical multiyear credit curves Correlation between assets typical for a single business sector and geography portfolio Focusing on PnL estimates one year forward, with PnL being impacted both by Realized Losses (defaults) and Provision variability (both positive and negative).
Credit Portfolio Management in the IFRS 9 / CECL and Stress Testing Era: The post-crisis world presents portfolio managers with the significant challenge to asimilate in day-to-day management the variety of conceptual frameworks now simultaneously applicable in the assessment of portfolio credit risk:
The first major strand is the widespread application of regulatory stress testing methodologies in the estimation of regulatory risk capital requirements The second major strand is the introduction of new accounting standards (IFRS 9 / CECL) for the measurement and disclosure of expected credit losses