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:
Translated in absolute numbers (according to IMF data) the European NPL book alone stands at around 1 trillion EUR.
The big data problem:
As certainly as the sun will set today, the big data explosion will lead to a big clean-up mess
How do we know? It is simply a case of history repeating. We only have to study the still smouldering last chapter of banking industry history. Currently banks are portrayed as something akin to the village idiot as far as technology adoption is concerned (and there is certainly a nugget of truth to this). Yet it is also true that banks, in many jurisdictions and across trading styles and business lines, have adopted data driven models already a long time ago. In fact, long enough ago that we have already observed how it call all ended pear shaped, Great Financial Crisis and all.
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.
A stress testing methodology for analyzing FX lending risk. Extends standard credit risk modelling tools to capture the increased risks of FX lending in a consistent way
Warren Buffet famously declared financial derivatives as weapons of mass destruction (although apparently this did not prohibit him from using them when convenient). The leverage afforded by such contracts, their potential complexity, or simply their novelty which may imply lack of understanding, are some reasons why one would classify them as potential contributors of systemic risk, which is a polished rephrasing of the more popular this sucker is going down quote.
We develop a simple methodology for stress testing portfolios of credit instruments classified as foreign exchange lending. Loans whose repayment schedule is denominated in a currency other than that of the borrower’s domestic currency are commonly seen in many jurisdictions and have a risk profile that is considerably more complicated than domestic currency loans. Yet the literature for credit risk assessment and stress testing of portfolios of such loans is very limited, which means that Stress Testing and Internal Capital Adequacy Assessment (ICAAP) requirements are harder to meet. Our methodology builds on existing standard tools used in portfolio credit risk modeling and enables obtaining insights into the additional risk factors embedded in foreign currency lending.
Sustainable business models that demonstrate adequate profitability over long horizons are key to a healthy market economy. This applies to firms and organizations of any size and in any sector. But how do we determine what is sustainable and how can we tell a risky business structure from a stable one?
We develop an analytical framework for the systematic identification of business model risks. The framework utilizes as a starting point a simplified business model schema known as the Business Model Canvas. We review each one of the elements of the schema in turn, identifying the main risk characteristics associated with each.
It has become trendy since the financial crisis to be wearing an anti-complexity hat in matters concerning the shape of the financial system. This is an understandable reaction to the entangled constructions that had sprung to existence in the hyper-leveraged markets of the naughty noughts.
We develop a taxonomy for risk models that aims to support an open source risk models framework. The proposal builds on and extends some commonly used risk taxonomies within financial services firms but introduces some significant new elements. We first review the motivation for risk taxonomies, the concepts and tools that are involved and some weaknesses of current schemes. We try also to clarify the link between risk models and risk taxonomies.
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 .
If you work in financial risk management you will most likely recognize where the following sentence is coming from:
One of the most significant lessons learned from the global financial crisis that began in 2007 was that banks information technology (IT) and data architectures were inadequate to support the broad management of financial risks. This had severe consequences to the banks themselves and to the stability of the financial system as a whole
We develop a proposal for an open source application programming interface (API) that allows for the distributed development, deployment and use of financial risk models. The proposal aims to explore the following key question: how to integrate in a robust and trustworthy manner diverse risk modeling and risk data resources, contributed by multiple authors, using different technologies, and which very likely will evolve over time.
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.
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?
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.
We review the definitions of widely used concentration metrics such as the concentration ratio, the HHI index and the Gini and clarify their meaning and relationships.
We review 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. We also propose that the sensitivity of concentration indexes to growing concentration should be a defining criterion for adopting an index and explore the sensitivity of common indexes to changing portfolio concentrations. We show that this sensitivity can vary significantly between indexes for parametric families of portfolio distributions and hence selecting and using a simple concentration index should take this aspect carefully into consideration
(See BIS D304)
Our view is that securitisation is fundamental financial technology and there is no intrinsic technical reason why it could not be harnessed to best serve the functioning of modern economies.
We believe, though, that a comprehensive overhaul of historical securitisation practices is the best means of addressing the stigma that has been attached to it in the follow up to the recent financial crisis. The laudable objective of introducing criteria for simple, transparent and comparable securitisations, (STCS Criteria for short) should aim to achieve a quantum leap in transparency and avoid the risk of perceived ineffectual measures. Technically this would require a visible reduction of model risk for prospective investors, expressed for example in tangible new ability to perform relevant risk analyses.
Overview of the motivation for initiating Open Risk and the solutions being delivered in the current phase.
Overview of the challenges and opportunities offered by internal capital models (economic capital models) in the post-crisis era. Conference Presentation given at: