Alveo Blog Data Governance

7 Data Sins: Insufficient model risk management

Stef Nielen Red Swan Risk Vlog

As a continuation of our 7 Data Sins series, Stef Nielen, Director Strategic Business Development at Alveo speaks with John Matway, CEO and founding partner at Red Swan Risk. During the discussion, Stef and John explore whether data models and data assignments are reliable enough to be trusted to navigate you through risky waters.

Q1: What are the challenges around modelling securities – why is it so challenging? I mean, when a company has just bought a risk system, doesn’t it deal with coverage out of the box?

A: Sometimes there is no suitable model or the right data might not be readily at hand (yet), which prompts one to resort to proxying. Here one wants to tread even more carefully to avoid creating additional model risk. Most generally speaking, model risk occurs when models don’t behave as they ought to. This may be due to an insufficient analytical model, misuse of the model, or plain input errors such as bad market data or incorrect terms & conditions or simply wrongfully chosen reference data such as sector classifications, ratings, etc.

Why is this so important?

Models can misbehave at the security level for long periods before showing up at the portfolio level.  Perhaps the size of the hedge was small and has grown larger, or the volatility suddenly changed.  This may suddenly create distortions at the portfolio, benchmark, or higher aggregate level. These problems often surface during times of market stress and can be very resource-intensive to troubleshoot at a critical time.

Q2: Why is it so resource-intensive to change, troubleshoot, and manage data?

A: When rules are hardcoded or implemented in an inflexible manner (i.e. model queries and scripts are being based on rigid and narrowly defined model schemas and inputs with too few degrees of freedom)  the problem is often exacerbated, making it truly difficult to interrogate and correct changes, when they are critically required.  Too often, the developer or analyst is given a set of functional requirements that are too narrowly defined, based on the current state of holdings and securities.

Given the dynamic nature of portfolio holdings, OTC instruments, available market data, and model improvements, it is essential to have a very flexible mapping process with and transparent and configurable rules that make it much easier to identify modeling issues and resolve them more efficiently.  A unified data model that tracks the data lineage of both model inputs and outputs (including risk stat, stress tests, and simulations), model choices, mapping rules, and portfolio holdings provides a highly robust and efficient framework for controlling this process. The benefit of working with a commercial tool is that it has been designed to address a very wide range of instrument types, data fields, and market data sources so you won’t outgrow its utility. So, in essence, having a unified model and data lineage capabilities combined together implies less digging and troubleshooting for the business user

Q3: Can we discuss some real-life examples perhaps?

A: Some examples are…

  1. Corporate bond credit risk derived from equity volatility using the credit grades model can cause significant distortions. A more direct method uses the observed pricing of single-name CDS prices or a sector-based credit curve. However, these must be properly assigned to the security with either the correct CDS red code or a waterfall structure for assigning the sector credit curve.  In the case of capital structure arbitrage where there are corporate bonds at various seniority and CDS, it is very important to be consistent in the mapping rules so that both the bond and the CDS have the same market data inputs.
  2. A similar issue occurs when using constant maturity commodity curves for convenience. This is easier to maintain than assigning the correct futures data set each time.  Calendar spread risk is underestimated with constant maturity curves that share data.  The negative front-month crude prices that occurred in March are an example of why constant maturity would have underestimated the risk significantly.  (I like this example because PassPort is a good solution for managing commodity future curve names in RiskMetrics).
  3. Changing over to the new Libor curves will likely be a very painful process for banks unless they have a very flexible mapping process that can easily be configured to assign the new curves to the right security types. (This is a simple procedure with the Map Editor and PassPort).
  4. But perhaps a more benign example is that of modelling one’s complete book with the right mapping for each individual security (i.e.: choosing the right risk factors as well as the correct reference data, such as ratings and sector classifications), whilst skipping to model all this stuff for its benchmark. This modelling inconsistency between portfolio and benchmark will introduce a TE-risk which can be contributed completely to inconsistent data mapping, rather than true market dynamics.

In summary, to model things properly – be it a simple proxy or something more granular and exact- one needs a setup that can dynamically configure the users ‘modelling choices and data mapping logic’. And as market conditions and data availability evolves over time, one should have a system that can adapt. Both Alveo and Red Swan allow the users to control their model and data mapping choices in a very flexible, transparent, user-friendly, and visual way. This doesn’t’ just help you during a setup or implementation phase but perhaps, more importantly, it drastically improves your ever-evolving modelling choices and (proxy) coverage over time as well as ongoing operational efficiencies. In short, it enables greater control over your model risk management.

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How we plan to optimize customer service through the current crisis

These are uncertain and difficult times, and we hope that you and your teams remain safe and well. As the coronavirus (Covid-19) outbreak continues to progress, we remain committed to ensuring the continuity of our services across the world.

Alveo is a resilient and stable company that is in a good position to operate through a period of extended disruption.  We are a global leader in providing reliable, high performance software solutions and managed services for financial data management. Our headquarters are in London but we also have offices in New York, the Netherlands, Singapore and São Paulo and have long worked with geographically dispersed teams.

The stability and reach this provides enables us to continue to provide the highest level of service to customers throughout the current situation. We understand the impact the coronavirus is having on organisations and have the capability to deliver our normal service levels through the current unsettling times. In particular, we appreciate that markets have been extremely volatile and this is a very challenging time for our clients who may need additional support at this time.

We invoked our business continuity plan in advance of the remote-working guidelines and new product development, maintenance enhancements and customer support are proceeding as usual. With safety everyone’s top priority, we will operate in line with World Health Organisation (WHO) and government guidelines. While we will maintain contact with customers over phone and email rather than face-to-face, we are focused on making it as close to business as usual as possible, without any reduction in, or break in, the service we deliver.

Should you require further information, please don’t hesitate to get in touch with us either by contacting your account manager or by directly reaching out to me at