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
We setup stylized calculations for a credit portfolio with the following main parameters and assumptions:
The post-crisis world presents portfolio managers with the significant challenge to assimilate in day-to-day management the variety of conceptual frameworks now simultaneously applicable in the assessment of portfolio credit risk:
The above developments are overlaid into pre-existing conceptual and practical frameworks such as
IFRS 9 (and the closely related CECL) is a brand new financial reporting standard developed and approved by the International Accounting Standards Board (IASB).
Strictly speaking IFRS 9 concerns only the accounting and reporting of financial instruments (e.g. bank loans and similar credit products). Yet the introduction of the IFRS 9 standard has significant repercussions beyond financial reporting, and touches e.g., bank risk management as well. This is prompted by the fact that the framework requires embedding forward looking risk assessments in the measurement of the value of credit assets currently on the balance sheet.
While the creation of the eurozone was a landmark of the European integration process, the financial crisis highlighted that the eurozone remains an incomplete design which can lead to unpredictable and adverse situations in the event of a (the) next major crisis. One of the key such incompleteness features of the current eurozone architecture is that it does not have a truly risk-free (safe) euro debt instrument: one that continues being serviced (avoids a default event) at virtually any point in time and state of the world, no matter how severe.
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
In an insightful recent piece, “Overcoming non-performing loan market failures with transaction platforms”, Fell et al. dug deeply into the market failures that help perpetuate the Non-performing loan (NPL) problem. They highlight, in particular, information asymmetries and the attendant costs of valuing NPL portfolios as key obstacles. In the same wavelength, the European Banking Authority published standardized NPL data templates as a step towards reducing the obstacles that prevent the reduction of NPL’s.
Open Risk released version 0.1 of the Transition Matrix Library
State transition phenomena where a system exhibits stochastic (random) migration between well-defined discrete states ( see picture below for an illustration) are very common in a variety of fields. Depending on the precise specification and modelling assumptions they may go under the name of multi-state models, Markov chain models or state-space models.
At Open Risk we are firm believers in balancing art and science when developing quantitative risk tools. The introduction of the IFRS 9 and CECL accounting frameworks for reporting credit sensitive financial instruments is a massive new worldwide initiative that relies in no small part on quantitative models. The scope and depth of the program in comparison with previous similar efforts (e.g. Basel II) suggests that much can go wrong and it will take considerable time, iterations, communication and training to develop a mature toolkit that is fit-for-purpose.
March 2016 wasn’t a good month for so-called internal risk models, the quantitative tools constructed by banks for determining such vital numbers as how much buffer capital is needed to protect the savings of their clients.
This visualization from the World Bank shows the current distribution of non-performing loans (NPL’s in short) around the world, as fraction of the total outstanding loans:
A stress testing methodology for analyzing FX lending risk. The methodology extends standard credit risk modelling tools to capture the increased risks of FX lending in a consistent way
Concentration Risk Management is a staple of risk management. Open Risk developed a unique and novel set of risk management resources to assist with building in-house knowledge for managing credit concentration risks.
Resources range from courses and online manuals to open source calculators and mobile eLearning games. In this post we have a brief summary of what is available, you can find more details by clicking on the embedded links
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.
Senior economists such as Ben Bernanke were still studying the 30’s Great Depression when the financial crisis struck in full force circa 2007. Given the complexity of the modern economic and financial landscape compared to the blessed good old days - we have no reports of FWMD (financial weapons of mass destruction) from back then - we can reasonably project that economists will be studying and pontificating on causes and remedies for the current crisis for the next 100 years or so.
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:
We are happy to publish the first installment of a trilogy that focuses on the risk factors that can turn any credit portfolio toxic.
The first topic is default correlation, a topic that is both core to understanding credit risk and much misunderstood.
Enjoy!
A Python module of analytic solutions suitable for testing credit portfolio models has been uploaded in the Open Risk github repository
Building out further the Open Risk Academy curriculum around Credit Risk Concentration management, we just added a course on sector concentration.
Pilot Course on Credit Name Concentration
We just added a new course at the Open Risk Academy!
The course covers name concentration risk measurement, chiefly the construction of suitable metrics based on portfolio data (Free registration is required for this course).
For more details see the course page.
New Tools for Credit Portfolio Calculations
The first batch of open source tools focusing on Credit Portfolio calculations is now available here.
The functionality aims to standardize Name Concentration Measurement (concentrationMetrics)