The Covid-19 pandemic has caused a period of high market volatility in March 2020. Notably, large increases in aggregate margin requirements were seen in both the centrally and non-centrally cleared markets. This prompted a review of margining practices by international regulatory bodies, in part to assess the efficacy of the global financial regulatory reforms following the Financial Crisis of 2007–09.
In the same vein, the Russia-Ukraine conflict in March 2022, led to another period of significant market volatility, causing a surge in all major commodity prices, especially energy products. This presented yet another real-world test of margin practices and the need to accelerate the international policy work recommended in the “Review of margining practices” report.
During these stress market conditions, many market participants have experienced significantly higher margin calls than their average daily flows, facing challenges in estimating and anticipating margin changes for trading and risk management purposes.
Here we discuss the key challenges in accessing, using and understanding central counterparty (CCP) margin models, and highlight the importance of the relevant tools that aid with the prediction of stressed margin calls.
The challenges faced by market participants
The review of margining practices in relation to the Covid-19 market turmoil, looked into whether market participants were fully prepared for the margin calls they experienced, and their ability to liquidate assets to meet margin calls under stressed conditions, as well as the role of margining practices, both in centrally cleared and bilateral markets, in amplifying strains.
The preparedness of market participants for potential margin calls is highly important in periods of stress and reflects the resiliency of financial markets. This preparedness is generally helped by analytical tools and data that allow them to estimate potential margin needs.
To understand the challenges of market participants, it is worth distinguishing between the intermediaries, namely the clearing members and broker-dealers who have direct relationships with the exchanges and CCPs, and the end-user clients who trade derivatives via the intermediaries.
Margin tools and data are generally provided by CCPs to their clearing members to help them estimate margin requirements. However, according to the report on margin practices, only less than half (46%) of the surveyed intermediaries indicated that they have the data and tools available to estimate CCP margin calls prior to the call being issued to clearing members.
Clearing members who do leverage the provided margin tools are nevertheless challenged by the fact that their capabilities differ across CCPs. Other issues to note include gaps in data, information and/or tool features, such as a lack of disclosure of the specific parameters that CCPs use to calculate Initial Margin (IM) requirements.
By way of an indirect relationship with CCPs, clients tend to face a lack of transparency and understanding of CCPs’ margin practices compared to clearing members. This makes it difficult to estimate and anticipate margin calls from CCPs that are passed through via the clearing brokers.
Clients typically rely on the tools provided by the intermediaries or independent software vendors (ISVs), as they find the CCP tools to be generally more complex, or advanced, than those provided by their clearing members or third-party providers. The fact that the tools provided by CCPs vary substantially does not help with their wide adoption.
Perhaps the most relevant insight, from the review of margining practices, is the broadly held view that the CCP-provided tools are generally better for determining the impact of a new trade on margin requirements than for performing analysis related to risk managing the portfolios’ exposures.
Margin estimation vs anticipation
As noted above, transparency around margin models differs across CCPs and jurisdictions. Various tools and data are provided by most CCPs to help clearing members and clients with the estimation of margin requirements for their existing portfolios as well as hypothetical portfolios e.g. changes to portfolios that participants expect to make.
However, only some of the CCPs provide additional functionality, allowing participants to estimate margin requirements under “what-if” scenarios of how IM requirements might evolve under various simulated volatility conditions. Scenario analysis tools allow market participants to better anticipate the potential changes in IM requirements during periods of stress.
For example, whilst margin calculation tools are needed by traders to determine the portfolio margin impact pre-execution of new trades, “what-if” analysis tools are needed by treasurers to forecast margin changes in order to anticipate the funding of large margin calls.
Increasing transparency with the right tools
The review of margining practices has highlighted the need for tools and simulators and what is expected of them, in order to provide increased transparency of CCP margin models and parameters, including for margin add-on components such as those relating to large positions.
Clearing members and clients require these tools, which not only enable them to estimate margin requirements for existing portfolios or expected changes to portfolios, but also to understand ex ante how individual models respond to various market scenarios and be better prepared for stressed liquidity needs.
To increase the predictability of margin calls during stress events, “what-if” scenario analysis tools are needed. They provide market participants with the ability to anticipate margin changes and perform risk management. Key considerations of such tools should include forward-looking hypothetical margin changes and stress scenarios of price moves on both portfolio and collateral.
Besides the provision of tools, the understanding of CCP models is enhanced via backward-looking disclosures concerning procyclicality, responsiveness to volatility and model performance.
Evidence from the recent review of margining practices suggests that increased transparency on margin models and parameters would leave market participants better prepared for market stress events.
Margin data and analytics tools are needed to aid with the prediction of stressed margin calls. The main functionality of these tools include the ability to calculate margin requirements for existing portfolios and expected changes to portfolios. Additional functionality is also expected to include the ability to estimate margin requirements under simulated market conditions, namely “what-if” scenarios.
Moreover, to foster preparedness, and therefore the ability to meet margin calls in times of stress, it is crucial that information on model performance, parameters and recalibration processes are also available to market participants.
Such a comprehensive set of tools and data would allow market participants to better anticipate the potential changes in margin requirements during a period of stress.
Get in touch to find out more about Cumulus9.