Case Study — Mar 5, 2026
Large Asset Manager adopts Buy-Side Liquidity Risk Solution
By Kamil Zielinski
THE CLIENT:
A large asset management firm
USERS:
The risk management team
The buy-side risk management landscape is evolving rapidly as asset managers face the new realities of volatile markets and geopolitical uncertainty. On top of this, investment firms in Europe are expanding their liquidity risk management frameworks to keep pace with the technical standards mandated by local regulators. This rapidly changing landscape is driving the adoption of innovative solutions that incorporate sophisticated tools and data.
Our client was using an in-house liquidity risk solution but viewed it as outdated and lacking appropriate data coverage, particularly for fixed income instruments. In addition, the framework could not scale effectively to manage growing data volumes and increasing computational demands. Members of the risk management team realized they needed to transform their liquidity analytics framework while keeping operational expenses in check. On attending an S&P thought leadership event they wished to learn more about the capabilities of our liquidity risk solution for buy-side firms.
These days, members of risk teams view liquidity analytics as an indispensable piece of the puzzle and need risk‑management systems that can flexibly respond to the ever‑growing regulatory requirements.
Pain Points
The risk management team faced considerable challenges due to limited liquidity coverage and inadequate tools. They wanted to adopt an agile liquidity framework that could keep pace with their ever-growing investment universe, and that was fully compliant with the latest regulatory requirements and internal risk-management use cases. Specifically, they needed a solution that could address the following:
- Liquidity Risk Measurement and Reporting: The client needed a reliable risk management framework that would enable the classification of assets into liquidity buckets based on time to liquidation and estimation of the cost of converting assets into cash. In addition, the envisaged solution was supposed to assess vulnerabilities of investment portfolios by means of customizable liquidity stress tests.
- Liquidity Coverage: Client’s liquidity coverage was not sufficient to manage effectively liquidity risks in the future. Their expanding investment universe required much broader coverage, particularly in fixed come markets
- Inadequate Methodology and Lack of Accuracy The firm had developed an internal liquidity risk system, but some outputs from their model were unintuitive. Because the in-house model did not capture key liquidity dimensions – such as market impact and market depth - the risk team frequently struggled with the accuracy of the estimates for less liquid assets.
- Insufficient Operational Efficiency: The existing liquidity management workflow was largely manual and based on an unstandardized data collection process. The risk team spent an inordinate amount of time managing fragmented liquidity data and badly needed an end-to-end solution with a consolidated dataset.
- Regulatory Pressure: ESMA required EU-based asset management firms to adopt new liquidity risk tools by April 2026. These tools require regular input of granular cost metrics (e.g., market impact), which the risk team could not generate on its own.
The Solution
The asset‑management firm faced several operational challenges because of an inadequate liquidity data management process and an inadequate methodology. Their previous approach to liquidity modelling did not leverage asset‑specific liquidity surfaces that capture the time, cost, and volume dimensions of market trades, so the old framework needed a fundamental transformation. To avoid the high cost of in‑house development, the client chose a risk‑as‑a‑service model, which provides access to S&P Global’s risk‑management capabilities, advanced technology, and cloud infrastructure.
The solution is designed to use a single, transparent liquidity risk model for all funds and across asset classes. It supports compliance with the AIFMD regulation by measuring time to liquidation based on trading size and cost constraints, and by classifying each fund’s investments into the prescribed liquidity buckets. The solution also includes a set of customizable stress scenarios that reveal potential portfolio vulnerabilities under both waterfall and proportional liquidation assumptions.
Liquidity data and analytics are currently provided in CSV format, but they can also be accessed through a stylish user interface. The client benefits from complete transparency through QA-verified liquidity data inputs, outputs, and intermediate results, which together form a dynamic, end-to-end framework. With this solution, the investment firm has now a liquidity risk management system that consolidates data and analytics into a single source. The risk management team can now combine the qualitative view from trading desks with the quantitative output from S&P Global’s liquidity model to provide a firmwide view of liquidity risk.
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Extensive coverage and operational efficiency |
A comprehensive solution that supports broad risk measures, extensive multi-asset classes, and regulatory compliance. The Buy-Side Liquidity Risk Solution includes: Global multi-asset class coverage supported by S&P Global’s quote and trade data for fixed income and Virtu Analytics’ cost curves for equities Methodology that models the multiple facets of liquidity risk – time, cost, and volume – in a consistent manner Automated workflow with large scale batch processing |
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State-of-the-Art Methodology |
Cost curves – Stock and ETF data are encapsulated in curves that represent the functional relationship between traded volume and liquidation cost for a given asset and trading strategy. Liquidity surfaces – Bond, loan, and securitized product data are processed in a quote‑based model to construct a unique liquidity surface for each asset. The surface captures liquidity risk along three dimensions: time, size, and cost. Sophisticated Proxy Logic – When there are insufficient transactions to construct a surface for a particular security, a comprehensive proxy methodology falls back to issuer‑level or sector‑level data. |
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Analytics - from asset to portfolio level |
Metrics available in the solution include: Time-to-Liquidation – the time horizon needed to execute a trading order given a transaction cost threshold Volume-to-Liquidation – the maximum size of a position that can be liquidated given transaction cost and time horizon constraints Cost-to-Liquidation – expected difference between realized price of an order and the asset’s mid-price Liquidity Parameters – i.e. stress testing parameters: bid/ask spread, quote range, quote count, quote size Liquidity bucketing – ready-to-use regulatory and custom scoring Liquidation Strategies – an optimization algorithm supporting proportional and waterfall approaches |
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Liquidity solution in practice |
A model highly responsive to liquidity shocks by continuously capturing changes in market elasticity Stress Definition Language (SDL) allows for designing highly granular stress tests Liquidity diagnostic ensures full transparency of results Fast-responding modern User Interface Custom and Regulatory reporting Multi-year data storage of portfolios and results |
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Benefit from trusted expertise |
24/7 support by professional services and financial engineering teams lets users free up internal resources to focus on other business priorities. |
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Reduce total cost of ownership |
Risk-as-a-Service model eliminates the need for an upfront investment in deployment of the solution Cloud-enabled technology lets users pay for what they use only, while lowering the administrative burden of maintaining the risk solution without sacrificing flexibility. |
Key Benefits
Members of the risk management team were impressed with the solution and decided to adopt the liquidity analytics offered by S&P Market Intelligence. The solution is now delivering:
- Reliable and Transparent Risk Results: Calculated outcomes are more dependable thanks to extensive granular data and specialized modelling functions. Stakeholders can view the liquidity parameters in the diagnostic report, which lets them trace the underlying results.
- Enhanced Liquidity Coverage: Previously existing coverage gaps have been eliminated; the solution is well‑equipped to support analysis of new instrument types going forward.
- Operational Efficiency: The burden of liquidity‑data sourcing, verification, and archiving has been taken off the client’s shoulders. Liquidity models and tools no longer need to be maintained or adapted to evolving regulatory requirements.
- Regulatory Compliance: The solution ensures that all relevant regulatory requirements and standards are met.
- Cost Efficiency: Running costs can be kept in check with the cloud-based risk-as-a-service model, which delivers on-demand analytics to support rapid, critical decisions.
- User Satisfaction: Overall, the flexible solution design and deep product expertise create a user-friendly experience.
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