Credit Risk

Course Objective

Digging into the meaning of credit data collections, the logic that binds them together towards understanding what they can be used for and what limitations and issues they may be affected by, this new course in the Credit Portfolio Management category explores a new angle to look at an old practice.

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.

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:

9 Things they do not tell you about Risk Management

1. Risks don’t fall from the Sky. They are generated by other People

Informal Risk Management has been 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.

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 proceed to recast various known indexes drawn from distinct disciplines in a unified computational context.

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). We look into the main data types: elementary data types, choice lists, arrays and unstructured text. We close with discussing some more complex issues involving graph and timeseries data.

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).

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.

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. The context of modern banking is substantially different from the above-mentioned use cases. Understanding and shaping federated information systems to cater to its unique features and constraints (key added value, competitive landscape, regulatory frameworks) will help accelerate the adoption of new designs. Towards that purpose we construct a framework that conceptually unbundles the complex operation that is modern credit provision. We introduce a number of fundamental business entities (subunits) and their associated functions and discuss the underlying business models. We discuss, in particular, how and why they exchange data and metrics and the key risk management challenges of each. Finally, we sketch current architectures for credit information sharing with an overture to the new possibilities opening up with federation architectures.

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:

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.

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). This design stands in contrast to traditional model estimation where all data reside (or are brought into one computational environment).

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 operational assumption is that staying within the risk limits defined by the framework is consistent with the degree of risk the firm is willing to accept while pursuing its business model. Limit frameworks offer the necessary flexibility demanded both by risk takers (the persons within the firm that undertake or underwrite risky projects) and the variable market environment with its ever evolving set of risks and opportunities.

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:

1. Feature: Added functionality for conditioning multi-period transition matrices
2. Training: Example calculation and visualization of conditional matrices
3. 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. This version of the transitionMatrix includes a canonical implementation that assumes a Gaussian single factor process as the driver of the joint rating dynamics. The technical documentation is available under in Open Risk Manual under the transition matrix category.

Release of version 0.4 of the transitionMatrix package

Further building the open source OpenCPM toolkit this realease of transitionMatrix features:

1. Feature: Added Aalen-Johansen Duration Estimator
2. Documentation: Major overhaul of documentation, now targeting ReadTheDocs distribution
3. Training: Streamlining of all examples
4. Installation: Pypi and wheel installation options
5. 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:

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 While both Regulatory Stress Testing and IFRS 9 / CECL accounting require investment in analytic capabilities and provide unique new insights, both are aimed at satisfying evolving prudential or investor disclosure requirements. Neither is designed to help credit portfolio managers analyse and steer their portfolios in the bottom-up fashion that is an essential part their mandate.

The above developments are overlaid into pre-existing conceptual and practical frameworks such as