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Case study - Traded Market Risk

Background of study

The Fundamental Review of the Trading Book (FRTB) requires banks to make a more rigorous assessment of their exposure to market risk.

This includes new eligibility tests for risk factors used to derive capital requirements under a revised Internal Model Approach (IMA).

With changing deadlines for compliance and varying local interpretations, firms continue to struggle with the complexity of the guidelines under fluctuating budgets.

A leading tier 1 bank looked for an agile yet robust solution that would help them gain insight on and build their internal business case for FRTB IMA.

This both supported regulatory quantitative impact study (QIS) and refined their business as well as infrastructure strategies ahead of regulatory go-live. In particular, they needed to:

  • Reduce the total cost of ownership of their FRTB infrastructure and shorten time-to-delivery both for programmes and QIS
  • Gain visibility on the capital impact of new trades, model assumptions and changing market conditions
  • Manage exposure and resulting market risk capital requirements more effectively
  • Hedge their implementations against global or local regulatory updates

“We rolled out a full scale QIS capability in 2 months delivered the business case 9-12 months earlier than with an internal build model.”


Traded Market Risk from IHS Markit supports compliance with the Basel market risk requirements.

We provide a hosted service that combines IHS Markit’s market-leading data with cutting-edge analytics.

Supported by a team of trusted subject matter experts, Traded Market Risk helps firms to reduce the impact, cost, and complexity of market risk projects.

For FRTB, more specifically, it delivers IMA or SA capital management through to IMA specific requirements of passing the Risk Factor Eligibility Test (RFET) or Expected Shortfall (ES) vs Stressed Expected Shortfall (SES) scenario generation and the non-modellable risk factor (NMRF) proxying.

Under volatile market conditions, early adopters could utilise pre-populated, market representative recovery point objective (RPO) datasets to deliver realistic and actionable RFET results.

These datasets, updated daily, drove pro-active investigations across interest rate, credit, equity or FX derivatives depending on current market liquidity or portfolio composition.

As one user commented, “it was very easy to demonstrate the benefit of RPO pooling on IMA capital”.

FRTB RFET/modellability results on QIS curves over 4 consecutive quarters

“The bank was able to increase modellable risk factors eligible for ES models under IMA by 40% on average.”

Other FRTB teams using Traded Market Risk have also built rapid insight into internal model approach (IMA) vs standard approach (SA) benefits, and key focus areas for data or model fine-tuning despite regulatory delays by leveraging our agile self-service configuration capability.

This has helped shape regulatory interactions and maximise value under growing budget constraints while educating internal stakeholders, and regulators alike on their model assumptions, and capital analyses.

Project outcomes and benefits

Accelerated time-to-delivery

Traded Market Risk offered an accelerated time-to-delivery where the bank rolled out full scale QIS capability for the first asset class in two months and the whole portfolio in under 6 months after an initial proof of concept.

A single user can now update current QIS analyses as a part-time activity in a month. Another bank was able to deliver IMA vs SA business case decision 9 to 12 months earlier compared to internal builds.

Reduced capital charges

One of the banks was able to increase modellable risk factors eligible for ES models under IMA by 40% on average for interest rate derivatives by using the Traded Market Risk FRTB data service.

They were able to proxy another 20% non-modellable risk factors on average across asset classes by using the scenario service thereby attracting much lower SES capital charges on the resulting basis.

Reduced total cost of ownership

Clients have significantly reduced the Total Cost of Ownership (TCO) for FRTB with Traded Market Risk and have on average accelerated their IMA programs by 2 years at least with 50% of the resources originally estimated.

Internal model approach (IMA) builds tend to take 2.5 to 3 years with 20+ specialized resources. The resulting reprioritization of resources enabled them to focus on key internal requirements instead which benefit the bank beyond FRTB, such as pricing library enhancements or data remediation.

Agile implementations

By using the Traded Market Risk modular implementation, the bank could prioritise the most relevant infrastructure components first and leverage in-house developments (or third-party projects) as and when required.

This also helped smooth “stop/start” phases of FRTB programmes subject to budget revisions and timeline changes.

Non-invasive, realistic model tuning

However, a realistic, bank-specific IMA configuration requires 12 to 18 months of “model tuning”.

This ranges from refining RFET mappings to validating market data, and proxy choices to understanding the impact of changes in the portfolio, market liquidity or prices or even the consequences of failing model validation.

This requires the risk analysts to be able to run realistic analyses in accessible, non-invasive infrastructure while implementation is being executed in parallel.

“Access to IHS Markit’s subject matter experts made a huge difference in our implementation.
Their functional and technical thought leadership inspired many of the decisions we have made both from a model configuration and from an architecture point of view” says one technical sponsor."

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Connect with our product expert

Jean Zottner

Throughout his career, Mr. Zottner has worked on the multiple aspects of product management, quantitative risk and system development. With over 13 years of experience in fintech and banking, he is an expert in quantitative risk and product design. He is passionate about using technology to solve business challenges and to streamline processes.Mr. Zottner holds an MSc in Computer Science obtained with distinction from Oxford University, as well as an Engineering degree from ENSIIE, a French Grande Ecole.

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