Portfolio Management

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).

openNPL 0.2 release

The open source openNPL platform supports the management of standardized credit portfolio data for non-performing loans. In this respect it implements the detailed European Banking Authority NPL loan templates. openNPL aims to be at the same time easy to integrate in human workflows (using a familiar web interface) and integrate into automated (computer driven) workflows.

openNPL now Available in Dockerized Form

Following up on the first release of openNPL the platform is now available to install using Docker. Running openNPL via docker is the installation option that simplifies the manual process (but a working docker installation is required!).

Docker Hub

You can pull the latest openNPL image from Docker Hub (This method is recommended if you do not want to mess with the source distribution).

Non-Performing Loans

The covid-19 crisis will certainly impact the concentration of Non-Performing Loans but given the special nature of this economic crisis compared (in particular) with the 2008 financial crisis it is unclear how precisely things will evolve.

In a previous post and white paper (OpenRiskWP07_022616) we discussed the importance of advancing open and transparent methodologies for managing the risks associated with such credit portfolios. Effective management of NPL is also a top regulatory priority. Following calls from the EU Commission and the EU Council to develop data templates to reduce information asymmetries between potential buyers and sellers of NPL, the European Banking Authority (EBA) has developed such standardised data templates.

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.

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.

Visualization of large scale economic data sets

Economic data are increasingly being aggregated and disseminated by Statistics Agencies and Central Banks using modern API’s (application programming interfaces) which enable unprecedented accessibility to wider audiences. In turn the availability of relevant information enables more informed decision-making by a variety of actors in both public and private sectors. An excellent example of such a modern facility is the European Central Bank’s Statistical Data Warehouse (SDW), an online economic data repository that provides features to access, find, compare, download and share the ECB’s published statistical information.

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 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!

Is the IFRS 9 or CECL standard more volatile? Its all relative

Objective

In this study we compare the volatility of reported profit-and-loss (PnL) for credit portfolios when those are measured (accounted for) following respectively the IFRS 9 and CECL accounting standards.

The objective is to assess the impact of a key methodological difference between the two standards, the so-called Staging approach of IFRS 9. There are further explicit differences in the two standards. Importantly, given the standards are not prescriptive, it is very likely that there will be material differences in interpretation and implementation of the principles (for example on the nature and construction of scenarios). In this study we perform a controlled comparison adopting a ‘ceteris-paribus’ mentality: We assume that all other implementation details are similar and we focus on the impact of the Staging approach.

Release of version 0.4 of the Concentration Library adds Geographic / Industrial concentration indexes

Further building out the OpenCPM set of tools, we release version 0.4 of the Concentration Library, a python library for the computation of various concentration, diversification and inequality indices.

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:

Extending the Open Risk API to include the EBA Portfolio Data Templates

The Open Risk API provides a mechanism to integrate arbitrary collections of risk data and risk modelling resources in the context of assessing and managing financial risk. It is based on two key technologies of the modern Web, RESTful architectures and Semantic Data.

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

Release of version 0.3 of the ConcentrationMetrics Library

Further building out the OpenCPM set of tools, we release version 0.3 of the ConcentrationMetrics 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

Loan Level Templates Using Python

In this Open Risk Academy course we figure step by step how to use python to work with Loan Level Templates, using the ECB SME template as an example.

• Overview of the loan level template
• Manipulating spreadsheets with Python
• The Python Dictionary
• Organization of Portfolio Data
• Generating Test Portfolios

Get an Open Risk Academy account and get started with the course here

Open Risk White Paper 7: Risk Capital for Non-Performing Loans

We develop a conceptual framework for risk capital calculation for portfolios of non-performing loans. In general banking practice, loans that pass a threshold of delinquency are declared non-performing and are provisioned. Yet there is a residual risk that the provisioning is not sufficient. This risk must be covered by capital buffers. The literature for risk capital requirements for NPL portfolios is very limited, which implies that Stress Testing and Internal Capital Adequacy Assessment (ICAAP) requirements for non-performing loans are harder to meet. Our framework builds on tools used in portfolio credit risk modeling and provides a structured approach to address the risk profile that is specific to non-performing loans.

Talk of unbundling the banks is all the rage these days (if we believe the chatter coming from fintech startups). Yet upon closer inspection one gets the feeling that these optimistic people might not necessarily know exactly what they are trying to unbundle, the true complexity of a medium-to-large bank, which in turn reflects, at least in part, the complexity of our modern Financial System .

Open source software is all the rage those days in IT and the concept is making rapid inroads in all parts of the enterprise. An earlier comprehensive survey by Gartner, Inc. found that by 2011 more than half of organizations surveyed had adopted open-source software (OSS) solutions as part of their IT strategy. This percentage may have currently exceeded the 75% mark according to open source advisory firms.

Open Risk White Paper 2: Confidence Capital: The Principle

We review the structure of economic capital frameworks commonly used within financial institutions and identify why the derived capital metrics do not explicitly address the needs for maintaining ongoing confidence on the soundness of the firm. In the follow-up to the financial crisis the need for more explicit such tests has been highlighted by regulatory stress testing methodologies.