Act Two: Liquidity Risk Stress Testing Scenarios – Dealing With Odile, That Villainous Black Swan

By: Yang Zhang, Global Product Manager, Capital and Liquidity, AxiomSL

In the first act of our miniseries – “It’s Stressful Living With A Black Swan” – we shared that we should consider the need for stress testing scenarios, processes, and technologies that financial institutions can leverage to prepare for systemic or idiosyncratic forces that can severely disrupt liquidity management, as has occurred during this global pandemic black-swan event. We outlined four steps that financial firms can consider as they seek to build industrial-grade liquidity risk stress testing processes.

The first step was exploring the network of factors that drive liquidity, how these factors impact short-term liquidity and medium-term funding buffers in times of extreme economic stress, and what firms can do to bolster their liquidity risk management capabilities, including by connecting critical deposit, customer, and account datasets, and extending connections across the management of liquidity risk, credit risk, and capital.

In this second act, we enter into a veritable ballet of liquidity risk stress testing scenarios and explore why big data is good, but ‘small’ data is what you really need for robust liquidity risk management – especially when Odile is dancing.

Stimulus And Daily Liquidity Reporting – An Allegro Pas De Deux

Central banks quickly and effectively responded to the COVID-19 crisis with aggressive stimulus interventions. Accordingly, regulators expanded liquidity reporting requirements to include new stimulus-related data and ramped up their monitoring – forcing many organizations to abruptly shift to daily reporting. G SIBs and some others were already monitoring liquidity daily, making the transition easier. But many large and small institutions were not and faced a very sudden regulatory burden on top of the operational strains they were already under.

Dancing Beautifully…But Perhaps With Toes Bleeding Inside The Pointe Shoes

We explore the ballet of liquidity risk stress testing scenarios and why big data is good, but ‘small’ data is betterIn stepping up to the crisis-induced challenge, financial institutions’ liquidity risk and regulatory data management and reporting capabilities were taxed – some sorely. Those that had implemented a holistic data integrity and control platform like AxiomSL’s were able to quickly adapt to the new requirements, and if they were not already doing so, rapidly shift to daily reporting. For others, especially smaller institutions, daily reporting remains devilishly tough and is achieved only through nightmarish scrambles involving manual workarounds. Irrespective of firm size, the pressure is relentless – risk managers’ daily peace of mind hinges on the regulators’ acceptance of each day’s submission.

Learning From The Live Performance Experience

An interesting takeaway from surviving daily reporting during a black-swan event is that organizations have been given an opportunity to road-test their liquidity data, risk stress testing, and reporting capabilities under the harsh theater lights of a high-profile live performance. For many, the experience highlighted weaknesses in their liquidity risk management approaches and technology capabilities; firms can now better address gaps in the efficacy of their liquidity risk management ecosystems.

Having to marshal daily reporting also made visible a new depth of valuable liquidity data that many risk managers believe will help them drive stress testing approaches and inform business decisions regarding, for example, how to optimize liquidity facility pricing or to identify optimal times to purchase or make composition changes to high-quality liquid assets (HQLA). For smaller institutions where daily reporting fits their business models about as well as a round peg in a square hole, useful information may still develop in the longer term, as they get used to assembling and reviewing the data frequently.

Princess Odette Loves Small Data

Juxtaposed with the opportunity to obtain insight and value from analysis of daily data, is that Odile highlighted weaknesses in financial institutions’ granular liquidity data – exposing gaps in the quality and consistency of the data itself. This is where the concept of big vs. small data merits consideration. Given the growing and enormous scope of the pandemic-driven liquidity, data ask, big data technologies certainly are attractive. Performance enhancement applications such as Hadoop, Spark, and Redshift can help in terms of running large amounts of data. AxiomSL embraces and continues to innovate with emerging technologies to incorporate them into its ControllerView® data integrity and control platform architecture.

However, right now it is more urgent that banks be able to track, trace, and extract data at an extremely granular level to better manage their balance sheets and face the evolving liquidity landscape. Dealing with the quantities of incoherent or missing small data coming into risk and regulatory systems from multiple sources requires extra monitoring and special preprocessing. Without readily available access to small data and the transparency to be able to understand it well, enhanced processing speed delivered by big data technologies is superfluous.

To vanquish the stresses brought by Odile and release Princess Odette from the evil spell, financial institutions must have clean, consistent granular data flowing into their liquidity risk and regulatory systems. Effectively managing regulatory data at the micro level will enable firms to eliminate manual adjustments and speed up business attestations and liquidity risk management decisions, while being able to comply with regulators’ stringent mandates with the highest efficiency.

A Ballet Of Liquidity Risk Stress Testing Scenarios – The Flock Of Black, White, And Grey Swans

With more stringent reporting came more frequent contact by regulators as they strove to discern the impact of their interventions and take the temperature of financial institutions’ abilities to withstand the current black-swan event. Indeed, many risk managers have been experiencing intraday touch points with their regulators throughout the pandemic crisis along with heightened internal scrutiny from their ALCOs (asset-liability committees).

It is a truism that liquidity risk remains not very well modeled in general and needs to be strengthened and improved. Liquidity risk managers continue to suffer the adage that painfully points out: You don’t know you don’t have enough liquidity until you don’t have enough! The HQLA value one thinks is there, may not be. The inflows and outflows from run-of-the-mill models may not materialize as expected. For some, during COVID-19, some inflows excessively materialized. For many others, outflows excessively materialized beyond their worst-case scenario and the regulatory liquidity coverage ratio (LCR) model. Nevertheless, whether because of regulatory support or not, there has been no public disclosure of an LCR compliance breach.

It is no wonder that now more than ever, liquidity risk managers are obsessed with reviewing the stress scenarios in their risk-management portfolios.

Binding Vs. Informative Scenarios – And The Temptress Odile

When considering liquidity risk stress testing assumptions, financial institutions must always weigh the cost of liquidity, carefully balancing that cost between remaining commercially viable and maintaining a prudent buffer. The temptation now is to focus on scenarios that attempt to deal with Odile and her black-swan cohort. As they live through the ongoing COVID-19 experience, liquidity risk managers are asking themselves if they should develop more black-swan scenarios, and/or what role should black-swan scenarios play in their risk management governance going forward.

While the temptation is there to explore black swans, it is generally agreed that binding firms to such scenarios is a recipe for failure. Equally, the lessons to be learned from this (hopefully) 100-year pandemic event must not be ignored.

Putting New Contingencies On Stage

Using black-swan scenarios in an advisory capacity, risk managers are free to internally explore new contingencies. For example, it would be useful to consider what could supplement HQLA in the LCR calculation. Such replacement or supplemental HQLA could help mediate idiosyncratic impacts. Another avenue to explore would be to develop contingency funding plan relationships among financial firms having low correlated risk profiles and making sure to put these funding capabilities to the test before the black swans appear.

Going a step beyond, risk managers and the industry at large may want to begin to account for the possibility of the ultimate Odile – an event where central bank(s) may not be able to step up and inject liquidity into curtailing a crisis. What supplemental HQLA might be available in that case? Another black-swan case that is tempting to consider is around political risk. What might happen under a severe regime change? Lastly, firms need to contemplate how to manage what happens when Odile-type events last so long that the impacts flow into the credit world, a topic posed in act one of this miniseries. These events can cause financial institutions to change their business models, perhaps radically.

Organizations will be asking themselves how they can better incorporate the granular daily data they produced during the coronavirus period. Eventually, that query will lead the industry to engage on how to accomplish intraday liquidity reporting, a Basel-driven objective that is being raised in various jurisdictions globally. Doubtless, dialogue with regulators will play a significant role in the entire liquidity risk stress testing exploration as conversations progress across the industry.

Sophisticated Choreography

Clearly, COVID-19 has presented financial institutions with an opportunity to take a step back and think about liquidity risks and incorporate lessons learned from this macabre dance with Odile into evolving stress testing design and methodology. This is a sophisticated endeavor that requires a sophisticated data and technology platform that enables firms to efficiently execute both binding and informative (advisory) liquidity risk stress testing scenarios within the operational context of daily reporting.

At its most basic, liquidity risk stress testing can be done using a series of assumptions utilizing end-user computing tools to gain insights. However, in a post-COVID-19 new normal, financial institutions will seek more depth and detail, leading to an increasingly complex undertaking that will enable them to examine all types of swans in the flock. Banks now relying on spreadsheet-based scenarios may be limited in their ability to test the impact of more sophisticated scenarios. Thus, to mount and manage more complex scenarios, firms need to ask themselves:

  • Can we emulate the stress scenario in our data and meaningfully analyze our outputs at the granularity we would like?
  • Can we do the calculations needed?
  • Do we have full transparency into our processes and traceability of our data?
  • Do we have the flexibility to report the way we want to both internally and externally?
  • Do we have our liquidity risk stress testing scenarios and data on a single platform?
  • Can we connect to data outside the liquidity silo?

Directing The Ballet With AxiomSL

AxiomSL’s liquidity risk management and reporting ecosystem on its ControllerView data integrity and control platform, deployable securely on RegCloud® or on premises, empowers financial institutions to deal with Odile and embrace small data principles by utilizing its powerful liquidity risk management capabilities including:

  • Highly scalable data management leveraging AxiomSL’s unique extensible data dictionary architecture
    Fast processing of large daily data volumes, end to end
  • Integrated liquidity stress testing (LST)models and scenarios
  • Results visualization via interactive dashboards
  • Daily LCR measure calculation delivered via a robust risk engine that captures relevant data and can trace back the data when necessary
  • Attributions module to track and trace the source of change in LCR across scenarios at the sub-component and granular trade levels

Future-proofing their Basel-related liquidity risk and regulatory reporting with AxiomSL enables financial institutions to strengthen their resilience when Odile or her cohort materializes, nimbly respond to changing and more exacting regulatory mandates, and enhance their liquidity decision-making to drive business growth.

Continue on to Act Three: Capital Stress Testing Scenarios And Swan Lake – Taking On The Most Fiendish Of Double Roles

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