The evolution of liquidity risk management

The evolution of liquidity risk management

Following the global financial crisis that began in 2007–08, policy-makers have multiplied 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 tackle due to its systemic component.

By Thierry Ciszewski, senior risk manager, HSBC Asset Management

Thierry Ciszewski, HSBC Asset Management
Thierry Ciszewski, HSBC Asset Management

To account for this difficulty, industry-wide regulations have been introduced to stem liquidity risk. These include the Securities and Exchange Commission’s Rule 22e‑4, which prompted industry participants to evolve their frameworks, placing market impact at their cores; the International Organization of Securities Commissions’ 2018 principles, following which many regulators refreshed their approaches to liquidity risk; and the European Securities and Markets Authority’s guidelines on liquidity stress-testing, which proposed an end-to-end framework to mitigate systemic risk, reduce contagion risk and strengthen the global financial system.

While these policies made strides towards greater liquidity risk management, the Covid‑19 pandemic put asset managers under pressure beyond regulatory control and changed how risk is perceived globally. As significant volatility ensued, bid/ask spreads widened and transaction costs increased dramatically, dealers’ capacities to provide liquidity was noticeably constrained and the cost of accessing liquidity skyrocketed, despite decent levels of exchange volumes. Given this, liquidity risk also carries reputational risk for asset managers – especially if not proactively managed. Sound fund liquidity risk management is a prerequisite for managers, and stakeholder demand to prove this risk is being managed appropriately has never been greater.

Accounting for liquidity risk is front of mind in today’s market with rising interest rates, inflation concerns and increasing geopolitical tensions. These dynamics are putting liquidity risk assessments in the spotlight because they provide a critical foundation for funds to better anticipate and manage fund liability demands.

A framework for liquidity risk management

Liquidity trend analysis, Bloomberg 0522
Bloomberg LQA data, as of April 2022

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

  • Products should be understandable and meet the needs of investors
  • Commitments should be delivered on and reflected in investment strategies
  • Potential liquidity mismatches should be prevented and managed to protect the interests of investors.

Disclosure is another key piece of the puzzle – and not only disclosure to regulators. Investors can choose to participate in less liquid assets, but those decisions must be made on a fully informed basis. Disclosures should be sufficiently comprehensive to cover the key features of a fund, the potential for illiquidity associated with different types of asset classes, the potential impact when open-ended funds experience significant redemptions and the diluting effect of excessive cash during periods of significant inflows. Fund managers have a reporting duty to their investors, and failing to provide adequate disclosures can create reputational and relationship damage.

Funds’ abilities to raise liquidity depend on their capacities to divest the underlying investments in dynamic market conditions within a given timeframe at an acceptable cost and market impact. The changing and often binary nature of market liquidity complicates this process. While analysis of market liquidity has evolved significantly, analysis of funds’ liabilities is often limited due to a lack of availability of and limitations within data, such as overreliance on historical redemptions to predict future redemption activity. This lack of efficient data management can ultimately impede firms’ abilities to accurately project investors’ inflows and outflows.

Ensuring the right liquidity data for fund managers

By leveraging cutting-edge data and expanding frameworks initially developed for regulatory compliance, many firms can holistically and proactively manage liquidity risk across their businesses.

An example of an effective solution is Bloomberg’s Liquidity Assessment (LQA) tool, which uses advanced financial models to accommodate a wider universe of securities, including those with no available data or little to no recent trading activity. By training models on a large database of executed trades from a variety of sources worldwide, 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 has three explanatory variables: bid/ask spread, volatility risk and participation rates. The benefit and purpose of this is to identify early warning signs and form a better-informed view of the multidimensional liquidity concept, which first requires the liquidity ratio to be assessed before liquidity costs can be estimated. As the liquidation ratio will depend on three factors – the liquidity of the portfolio to sell, the amount to sell and the liquidation policy – it is important to reflect that the liquidity ratio is relative to trading constraints and liquidation schedule (or trade structure).

Generally, the liquidity cost is measured by the transaction cost. However, in a liquidity stress-testing programme, this measure is merely theoretical since it is based on the transaction cost model. Therefore, it can be completed by the ex-post-liquidity cost, which is also called the effective cost.

If an asset manager does not have enough data, regression and advanced modelling techniques will need to be employed to set the value of these parameters. The LQA model is widely used across the industry for regulatory compliance and, since this model is data-driven, it removes subjectivity and provides a more robust and defendable liquidity assessment.

When finding the optimum liquidity risk approach, managers must consider the big picture of having the right access to the right data in the right context. Liquidity risk assessment is an increasingly integral part of asset managers’ frameworks for regulatory compliance and the proactive management of liquidity risk – especially in more turbulent markets.

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