The evolution of liquidity risk management

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Following the global financial crisis that began in 2007-2008, policymakers stepped up their efforts and implemented reforms to strengthen the resilience of the financial sector. But – while established frameworks and models have been implemented and understood by the industry for market, credit and operational risk – liquidity risk has been the most difficult to model and address due to its systemic component.

By Thierry Ciszewski, senior risk manager, HSBC Asset Management

Thierry Ciszewski, HSBC Asset Management

To accommodate this difficulty, industry-wide regulations have been introduced to stem liquidity risk. These include the Securities and Exchange Commission’s Rule 22e-4, which has prompted industry players to evolve their frameworks, placing market impact at the heart of their concerns; the 2018 principles of the International Organization of Securities Commissions, following which many regulators have renewed their approach to liquidity risk; and the European Securities and Markets Authority’s Liquidity Stress Testing Guidelines, which offered an end-to-end framework to mitigate systemic risk, reduce the risk of contagion and strengthen the global financial system.

While these policies have made progress towards better management of liquidity risk, the Covid-19 pandemic has put asset managers under pressure beyond regulatory control and changed the way risk is viewed at the global level. global scale. As significant volatility ensued, bid/ask spreads widened and transaction costs increased dramatically, dealers’ abilities to provide liquidity were significantly constrained and the cost of accessing liquidity skyrocketed, despite decent levels of trading volumes. Given this, liquidity risk also carries reputational risk for asset managers, especially if not proactively managed. Sound management of fund liquidity risk is a prerequisite for managers, and the demand from stakeholders for evidence that this risk is being managed appropriately has never been greater.

Accounting for liquidity risk is a priority in today’s market with rising interest rates, inflation fears and growing geopolitical tensions. These dynamics put liquidity risk assessments in the spotlight as they provide a critical foundation for funds to better anticipate and manage fund liability demands.

A framework for managing liquidity risk

Analysis of liquidity trends, Bloomberg 0522

Bloomberg AQL data, in April 2022

The main objectives of any approach to liquidity risk management should be based on the following principles:

  • Products must be understandable and meet the needs of investors
  • Commitments must be respected and reflected in investment strategies
  • Potential liquidity mismatches should be avoided and managed to protect investors’ interests.

Disclosure is another key piece of the puzzle – and not just disclosure to regulators. Investors can choose to participate in less liquid assets, but these decisions must be made consciously. The information provided must be comprehensive enough to cover the main characteristics of a fund, the potential for illiquidity associated with different types of asset classes, the potential impact when open funds experience large redemptions and the effect of dilution excessive liquidity during periods of large inflows. Fund managers have a duty to report to their investors, and failure to provide adequate information can damage reputations and relationships.

The ability of funds to raise cash depends on their ability to dispose of the underlying investments in dynamic market conditions within a given time frame at an acceptable cost and market impact. The changing and often binary nature of market liquidity complicates this process. Although the analysis of market liquidity has evolved considerably, the analysis of fund liabilities is often limited due to a lack of data availability and limitations, such as over-reliance on historical redemptions to predict the future buyback activity. This lack of effective data management can ultimately hamper companies’ ability to accurately project investor inflows and outflows.

Ensuring the right liquidity data for fund managers

By leveraging state-of-the-art data and extending frameworks originally developed for regulatory compliance, many companies can comprehensively and proactively manage liquidity risk across their businesses.

An example of an effective solution is Bloomberg’s Liquidity Assessment (AQL) tool, which uses advanced financial models to fit a broader universe of securities, including those for which no data is available or little or no recent trading activity. By training models on a large database of trades executed from various sources around the world, it is calibrated daily to capture changing market conditions.

Bloomberg’s model has three parameters: spread sensitivity, price impact sensitivity, and price impact exponent. It also includes three explanatory variables: bid/ask spread, volatility risk and participation rates. The interest and purpose of this is to identify the warning signs and to form a more informed view of the concept of multidimensional liquidity, which requires first to assess the liquidity ratio before being able to estimate the costs of liquidity. As the liquidation ratio will depend on three factors – the liquidity of the portfolio to be sold, the amount to be sold and the liquidation policy – ​​it is important to reflect that the liquidity ratio is relative to the trading constraints and the timetable for liquidation (or bargaining structure).

Generally, the cost of liquidity is measured by the transaction cost. However, in a liquidity stress test program, this measure is only theoretical since it is based on the transaction cost model. It can therefore be supplemented by the ex-post liquidity cost, also called effective cost.

If an asset manager does not have enough data, advanced regression and modeling techniques will have to be used to define the value of these parameters. The AQL is widely used in the industry for regulatory compliance and because this model is data-driven it removes subjectivity and provides a more robust and defensible liquidity assessment.

When looking for the optimal approach to liquidity risk, managers need to consider the big picture, i.e. have the right access to the right data in the right context. Liquidity risk assessment is increasingly becoming an integral part of asset managers’ regulatory compliance and proactive liquidity risk management frameworks, especially in more turbulent markets.

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