More Liquidity Stress Testing: Is It Enough to Prevent Future Crises?

Overall shortcomings in the liquidity risk management of the stricken firms emerged as common factors in their downfalls. These concerns typically fell into three main areas:

1. The mandated 14- and 30-day horizons for stress testing under Regulation YY lacked sufficient granularity.
2. The modeling assumptions behind liquidity stress testing (LST) were insufficiently sensitive to business dynamics and insufficiently anchored by granular data cohorts.
3. Run-off rates, like those for unsecured wholesale deposits and non-high-quality liquid assets (HQLA) secured funding, needed to be more severe.

Following the events of early 2023, liquidity vulnerabilities were heightened for lenders and depositors alike. This gave rise to calls for regulatory changes in related risk measures. Recommended areas for review included:

• Considering original maturities as a liquidity-risk-sensitivity criteria
• Compounding LST rollover/run-off rates based on original maturities
• Adding a short-term unsecured wholesale-funding regulatory limit
• Requiring the public disclosure of held-to-maturity (HTM) positions marked-to-market unrealized P&L (net of hedges)
• Monitoring changes in the interest rate risk in the banking book (IRRBB)
• Including regulatory capital add-ons 
   

Whereas the liquidity coverage ratio (LCR) is a regulatory one-size-fits-all risk model with a universal regulatory limit, liquidity stress testing encompasses multiple models for regulatory and business requirements with limits.

Nonetheless, both LCR and LST models should be based on the same core data, with the same controls and governance. The LST capability may include data, such as uncommitted credit facilities, not used in LCR.

As previously mentioned, the frequencies of measure should be greater than the current regulatory frequency. (Specifically, time horizons need to be shorter than 14 days for LCR and 30 days for LST.)

Additionally, LST should:

• Comprise more extreme scenarios in its run-off rate severity
• Incorporate stress-scenario results expanded to include net-funding requirements and liquidity-risk grouping-policy-limits compliance
• Show and report sources and uses of liquidity, with net daily and cumulative outputs

In general, LST should be more attuned to the realities of the commercial environment rather than regulatory requirements.
 

An effective Liquidity Stress Testing (LST) solution plays a critical role in boosting a bank's confidence in its liquidity risk management practices. It not only helps future proof the institution against market volatility and evolving regulatory frameworks but also builds trust between the bank and its regulators.

By ensuring that stress tests align with both internal and external expectations, an ideal LST solution contributes to a robust, compliant, and transparent liquidity risk management strategy.

Critical elements include a solution’s ability to seamlessly integrate with existing systems, such as FR 2052a reporting, and other external data sources. This integration ensures that the system is adaptable and capable of delivering accurate, reconciled data. Other hallmarks of a well-designed LST solution include:

• The ability to organize regulatory data within an extensible, dictionary-based architecture, providing a solid foundation for both compliance and strategic decision-making.
• The modeling of idiosyncratic scenarios using the same comprehensive, reconciled data sets, ensuring consistency across all liquidity assessments.
• Clear data lineage, linking source data directly to calculation results. (The efficient use of data enables optimization, stress testing, and what-if analyses, providing transparency and traceability.)

Performance is another key consideration: an effective LST solution must handle large volumes of data with ease, supporting rapid analysis, quick re-runs, and timely reporting. It must also provide business users with transparency and control over applied business rules and logic, ensuring that they can adapt the solution as regulatory or business requirements evolve.

Moreover, a quality LST solution should offer comprehensive features for managing the stress-testing process within a controlled environment—look for granular permissions and roles that ensure the right levels of access and accountability. The solution should also expand stress-test results to include net funding requirements and compliance with liquidity-risk policy limits, while integrating across both trading and banking books for a holistic risk view.

By embedding controls and data governance into the operating model, a strong LST solution supports smooth day-to-day liquidity-risk management, facilitating internal audits and regulatory examinations with ease.

The banking crisis of 2023 prompted regulators and industry leaders alike to pay more attention to the stress-testing methodologies and processes that financial institutions use. This has increased the pressure on firms to make their calculations and data transparent, demonstrating exactly how they are governed by internal controls. Reliance of banks on simple monthly modeling is not sufficient to withstand heightened scrutiny.

Similarly, the increased scope of calculations and analytics will require a comprehensive review of strategic data sourcing and architecture. Maintaining tight data lineage to prove out accuracy is increasingly mission critical.

Equally vital is the ability to dissect the data at varying levels of granularity, allowing one to better isolate and treat different scenarios, using a mix of source data and simulated data sets to assess current and future risks and opportunities. 
 

These are some of the measures that financial institutions may consider for strengthening their liquidity stress testing and helping secure their balance sheets against market turbulence. Typically, achieving a strategic, future-proofed, and flexible foundation for liquidity risk management means sunsetting piecemeal, process-intensive systems and solutions reliant on obscured black-box methods.
 

Take Control of Your Liquidity Risk Management

Our Nasdaq AxiomSL Liquidity Stress Testing Solution on the Nasdaq AxiomSL ControllerView® platform enables you to combine FR 2052a liquidity data with other sources to model various scenarios and much more transparently and flexibly manage your liquidity risk.

In addition to addressing U.S.-related situations, our holistic solution approach meets your liquidity risk management needs globally, while taking care of jurisdictional nuances.

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