
FX Lending Risk
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
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
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.
Financial services jobs continue being decimated. A recent (as of the initial post date) FT article was a sobering summary of the continuing transformation of the financial sector: 2015 alone has seen more than 10% reduction of the total workforce across large EU/US banks:
Open Risk API: Improved financial risk management through open data, open source and web technologies. This is a Presentation given at the TopQuants/DNB Autumn Meeting, Amsterdam, Nov 18, 2015.
Improved financial risk management through open data, open source and web technologies is a Presentation given at the TopQuants/DNB Autumn Meeting, Amsterdam, Nov 18, 2015.
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 .
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.
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 know the periodic table of elements, even if you flunked your science courses! It is the large colorful and blocky table that hanged on every school’s classrooms before becoming yet another mobile app. The periodic table is one of the early and iconic achievements of science. It lists all the pure chemical elements found in nature, the building blocks of all possible material substances. Each block contains a set of numbers that unambiguously characterizes each element and a single or two letter abbreviation for each: H for Hydrogen, He for Helium and so forth, going on for over a hundred different elements. When the periodic table was discovered by Mendeleev (apparently in his dream!) it was an extraordinary realization that the physical world has an underlying order at the microscopic level. In his own words:
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
There is(/was) renewed interest in EU-land over deepening a capital markets union, aka CMU. It is among the initiatives being pursued by the Commission in order to help accelerate growth in the European Union.
The initiative encompasses many elements, both around equity (shares) and debt markets. One important pillar of the CMU aims to re-launch some version of an EU securitisation market. This segment was never really defined in a EU-wide basis. In the pre-crisis European financial landscape there were wide disparities in the degree of adoption, legal frameworks, preferred structures etc. among the different countries comprising the EU. In any case, since the financial crisis there has been a steady decline in securitisation volumes, which amongst others, hinders certain types of exceptional central bank measures (i.e., purchasing securitised assets).
When talking about risk management, it is not very clear what we are talking about in broad terms, definitely not getting clearer when we start getting into the details and it is even not clear how to best use the (possibly flawed) insights we produce.
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!