Risk Markets Technology Awards 2021: Market liquidity risk product


Market liquidity risk product of the year: Bloomberg

Brad Foster, Bloomberg

Brad Foster, Bloomberg

Building a framework to assess market liquidity requires a data-driven approach to ensure all liquidity risk metrics are observable and marked to market. Bloomberg’s Liquidity Assessment (LQA) solution incorporates daily market data, allowing its models to quickly react to changing market conditions. Users can also customise the models to suit their individual use cases and define their own stress scenarios.

LQA provides a consistent framework that captures the dynamics of different asset classes to support a consolidated view of liquidity at the portfolio level. For the many securities where visible markets may be sporadic at best, LQA’s data-driven model estimates execution measures, such as market depth, expected liquidation cost and time-to-liquidate. 

Where data does not exist for a given security, various algorithms, including machine learning techniques, fill the gaps. Bloomberg has access to significant trade data, spanning exchanges, trade repositories, clearing houses, axe lists and bilaterally permissioned contributions, that provides the basis for robust data cleansing and filtration algorithms.

Liquidity risk must now be assessed under a variety of stressed market conditions. LQA allows users to apply fully customisable hypothetical scenarios or use predefined historical stress events. This is possible at any level of the portfolio – from individual transactions to the portfolio itself – which allows for more realistic scenarios that account for the different behaviours across asset classes, including flight-to-quality scenarios.

LQA offers flexible workflow options and integration of liquidity measures into a client’s infrastructure, including via Bloomberg terminal, Excel application programming interface (API) or programmatic API for Python, C++/, Java and .NET, as well as daily batch files via secure file transfer protocols. 

Recent enhancements to LQA’s models include integrating additional data sources and incorporating more advanced machine learning techniques, such as leveraging deep neural networks to predict available volume. The fixed income model now allows users to assess liquidity on both the bid and offer side of a transaction by accounting for the asymmetrical nature of liquidity when buying versus selling. The new offer-side metrics provide analytics to prepare for the mandatory buy-in regime imposed by the Central Securities Depositories Regulation settlement discipline rules, as well as other use cases such as exchange-traded fund basket creation. 

LQA has continually been subject to model validation and performance reviews at tier one, two and three banks globally, especially in the aftermath of the Covid-19 pandemic, resulting in more extensive and improved model validation and backtesting documentation to satisfy even the strictest model governance. 

Judges said:

  • “Bloomberg has designed an elegant, effective data-driven framework that ensures liquidity metrics for cost, horizon and volume are consistent and comparable, and are then put into service.”
  • “Bloomberg offers multiple delivery channels for its liquidity risk service.”
  • “Model validation and performance reviews at a spectrum of clients have improved and confirmed the value of the service.”

Brad Foster, global head of enterprise data content, Bloomberg, says:

“Winning the award for market liquidity risk product of the year two years in a row is testament to our continued investment and deep client engagement and partnership as it relates to risk data and liquidity risk. Given recent unprecedented market disruption, it has become increasingly important for the industry to shift focus from historical scenarios and qualitative liquidity assessments to data-driven liquidity metrics that capture rapidly changing market conditions across the full trade lifecycle. Bloomberg LQA consistently produced accurate results throughout the past volatile year by applying cutting-edge financial modelling techniques to Bloomberg’s vast amount of trade data.”



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