Market liquidity risk product of the year: Bloomberg

Zane Van Dusen, Bloomberg

Compared with credit and market risk, accurate measurement of liquidity risk remains a significant challenge across the financial industry. This is a growing concern as regulators worldwide strengthen their focus on market liquidity and liquidity risk frameworks. Firms need to quantify liquidity risk and generate accurate, defendable metrics – not only for compliance but also for internal portfolio assessment and investor reporting. The monitoring of market liquidity and asset/liability mismatches now forms an intrinsic part of many firms’ risk management frameworks.

Illiquid and opaque markets are a major challenge for liquidity risk management. Bloomberg’s Liquidity Assessment (LQA) solution uses advanced financial models to fill in the gaps where data is not available, accommodating a wider universe of securities, including those with little-to-no recent trading activity. By training the model on a large database of executed trades from a variety of sources globally, the model is calibrated daily to quickly capture changing market conditions. This means LQA has consistently produced accurate results during the extreme and unexpected market events that have occured over the past two years.

Because every asset class has different trading styles and market nuances, Bloomberg created asset-class-specific models to reflect different underlying conditions. All LQA’s liquidity metrics for cost, horizon and volume are consistent and comparable, making it easy to use and aggregate across diverse portfolios.

A team of data scientists monitor LQA output, with robust backtesting to track and validate the model’s performance and support clients’ model validation procedures. The model can be customised to cater for a firm-specific view and increasingly to support stress-/scenario-testing. In addition to stressing underlying market conditions at the security level, clients can request transaction-level output metrics based upon different percentiles of liquidation cost and horizon distributions. This allows clients to better simulate ‘tail events’. This is particularly useful when building limit monitoring frameworks and responding to regulator demand for stress scenario outputs where greater flexibility than a simple default output may be required.

Over the past 12 months, Bloomberg has made several enhancements to LQA, including:

• Developing a new suite of metrics that provide more transparency on actual trading volumes of exchange-traded funds, and their implied liquidity.
• •Expanding the LQA model to include coverage for collateralised loan obligations (CLOs). This has been done by partnering with experts in this sector to onboard high-quality pricing data for CLOs and to integrate that data into Bloomberg’s liquidity assessment framework.
• Expanding LQA’s functionality to support the upcoming liquidity assessment guidelines from the Japan Financial Services Agency.
• Enhancing LQA’s models to improve the quality and coverage of liquidity metrics. This includes integrating additional data sources and incorporating more advanced quantitative modelling techniques, such as leveraging deep neural networks to predict available volume.

Judges said:

• “LQA’s use of sophisticated modelling techniques to fill the gaps where there is simply no data available creates a potentially very strong tool.”
• “Very strong market liquidity product with a data-driven approach and backtesting evidence.”

Zane Van Dusen, Head of Risk & Investment Analytics products at Bloomberg, says:

“We’re honoured to have LQA recognised as the market liquidity risk product of the year in the Risk Markets Technology awards for the third year in a row. LQA has become an increasingly integral part of our clients’ risk frameworks by providing an easy-to-use solution for the ongoing challenge of assessing market liquidity. In addition to regulatory compliance, we are excited to see our clients finding innovative ways to leverage LQA throughout the full trade lifecycle, including pre-trade to proactively manage liquidity risk. While there may be simpler ways to assess liquidity – like static rules-based approaches or trader intuitions – numerous events over the past few years have highlighted the need for dynamic data-driven liquidity metrics.” 

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