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.
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.
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:
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!
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.
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.
The below list provides documentation URL’s for each one of the implemented classic indexes (the Hoover index is a new addition in this release
Atkinson Index Hoover Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index An important new direction that appears first in this release is the introduction of indexes that measure geographical and industrial concentration.
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).
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.
OpenCPM, the credit portfolio management platfrom we launched recently fully integrates the latest versions of the Open Risk API.
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
Release of version 0.3 of the Concentration Library Further building out the OpenCPM set of tools, we release version 0.3 of the Concentration 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
Atkinson Index Concentration Ratio Berger-Parker Index Herfindahl-Hirschman Index Hannah-Kay Index Gini Index Theil Index Shannon Index Generalized Entropy Index Kolm Index The image illustrates a simple use of the library where the HHI and Gini indexes are computed and compared for a range of randomly generated portfolio exposures.
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
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Unbundling the Banks: A How To Guide Talk of unbundling the banks is all the rage these days (if we believe the 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 Risk Data with MongoDB and Python 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.
The mystery of the collapsed cathedral You walk to the center of an old city and you see its glorious cathedral lying in ruins. What in the world has happened here? Your investigative instinct goes into overdrive. This is not supposed to happen. Not in peacetime anyway. How can it be that this magnificent edifice, after gracing the town’s central square for who knows how many centuries, is now little more than a rubble pile in the center of town?
Revisiting simple concentration indexes
Our white paper “Revisiting simple concentration indexes” reviews the definitions of widely used concentration metrics such as the concentration ratio, the HHI index and the Gini and clarify their meaning and relationships.
This new analytic framework helps clarify the apparent arbitrariness of simple concentration indexes and brings to the fore the underlying unifying concept behind these metrics, thereby enabling their more informed use in portfolio and risk management applications.
FuriousBanker(TM) helps you learn risk management concepts in a fun and engaging way. This educational game series for mobiles and tablets is developed by Open Risk to enable modern interactive elearning for people working (or aspiring to work) in financial risk management.
The first episode sees FuriousBanker facing The credit detox challenge:
Game instructions for FuriousBanker:
You Objective: You inherited a pretty toxic credit portfolio and your objective is to reduce the concentration, even while improving your profitability.
