HQLA is, by its very nature, supposed to be ‘liquid’, and not just in periods of market equilibrium, but also in periods of the most unforeseen stress and market dislocation. Basel Rules specifically require that instruments be “Liquid & Readily Marketable” (LRM) to qualify as High Quality Liquid Assets (HQLA) for liquidity coverage and capital adequacy purposes. The purpose is clear, but the practice is much more challenging as there is no shared definition or criteria for LRM across jurisdictions.
Now, more than ever, it is important to be critical of the liquidity and marketability of a bank’s HQLA and large US banks have signaled that increased scrutiny of the “Liquid & Readily Marketable” component was on the Fed’s 2023 testing agenda, even before the recent market events.
A recent article in Risk.net highlighted the tendency of major banks to classify their HQLA at Held-to-Maturity, allowing them to be measured at cost in their financial statement. The article highlighted the mismatch between the accounting treatment and the regulatory purpose of this liquidity buffer, a mismatch that obscures transparency into the value and liquidity of this essential capital buffer.
How to determine if a High Quality Liquid Asset is Liquid & Readily Marketable?
With no agreed-upon guidance globally, regional interpretations and implementations vary. The Federal Reserve Bank defines a Liquid and Readily Marketable security as “a security that is traded in an active secondary market with: (1) more than two committed market makers; (2) a large number of non-market maker participants on both the buying and selling sides of transactions; (3) timely and observable market prices; and (4) a high trading volume.”
Demonstrating the criteria above can be quite challenging and requires robust, granular market depth and liquidity information.
First and foremost, it requires detailed information on actual observed quotes and trades, both on specific HQLA securities and on peer or comparable securities. In particular, demonstrating the “two committed market makers” means that this data will need to be sourced from a large number of brokers. The “timely” and “large number” requirements further require information on the age and number of quotes and trades. Best practice is to capture and leverage data on the count, age, type, executability and standard deviation/spread of observed quotes and trade, at a minimum.
However, observed quote and trade data does not always tell the whole story. Liquidity is position-specific and past data is not always a good indicator of future liquidity. As a result, forward-looking liquidity models are often used to prospectively estimate liquidity cost and horizon based on position-specific data.
Beyond the Basel Rules: Liquidity and regulation
The regulatory value of robust liquidity data goes well beyond just the LCR & HQLA rules. Market depth and liquidity data has increasingly become an input in various global regulations and rules. Some regulations include:
Prudent Valuation: PruVal rules require calculating adjustments (AVAs) between the fair value and “prudent valuation”. These adjustments are based on the level of “valuation uncertainty” inherent in an organization’s fair values. The lower the valuation uncertainty, the lower the adjustment and therefore the lower the capital requirements and the cost to the organization. Robust liquidity data, especially standard deviations, can provide evidence of low valuation uncertainty, reducing PruVal obligations and the cost of compliance.
Fair Value Leveling – IFRS 13 & ASC 820: Global accounting standards require companies to include a “leveling” table in the notes to the financial statements, which classifies all investments presented at fair value as Level 1, 2 or 3, based on the significance of unobservable inputs. Quote and trade data can be used to classify instruments based on these rules.
Bloomberg liquidity tools
In order to meet the challenge of demonstrating the Liquid & Readily Marketable criterion and the various other liquidity-driven regulatory challenges, Bloomberg provides extensive liquidity data and analytics.
Market depth and transparency fields
BVAL, Bloomberg’s evaluated pricing service processes more than 1 billion market observations daily to evaluate more than 2.7 million fixed income instruments. From this massive universe of data, Bloomberg produces granular consolidated data on the count, age, type, and standard deviation of trades and broker quotes (both executable and indicative). This in-depth quote and trade data can be used to provide strong empirical evidence of liquidity of instruments and positions, often in conjuncture with LQA outputs.
Bloomberg
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