How Does Procyclicality in Initial Margin Requirements Affect Clearing Member Liquidity during Market Stress? ▴ Question

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The Procyclicality Paradox in Margin Requirements

Procyclicality in initial margin requirements describes a self-reinforcing cycle where margin calls amplify market movements. During periods of low volatility, initial margin requirements tend to be low, which can encourage leverage and risk-taking. However, when a market shock occurs and volatility increases, margin models react by demanding significantly more collateral.

This sudden increase in margin requirements can force market participants to sell assets to raise cash, which in turn can exacerbate the initial price decline and further increase volatility. This feedback loop is the essence of procyclicality and a central concern for financial stability.

Procyclicality in initial margin requirements can create a destabilizing feedback loop during periods of market stress.

The core of the issue lies in the design of the risk models used to calculate initial margin. These models, such as Value-at-Risk (VaR) models, are designed to be risk-sensitive. This means that when market volatility increases, the models will naturally calculate a higher level of potential future exposure, leading to higher initial margin requirements. While this risk sensitivity is a necessary feature for protecting central counterparties (CCPs) from default losses, it can have unintended consequences for the liquidity of clearing members, especially during periods of market stress.

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The Impact on Clearing Member Liquidity

For clearing members, the primary impact of procyclical initial margin requirements is a sudden and significant increase in their liquidity needs. During a market stress event, clearing members may face a barrage of margin calls from CCPs, requiring them to post additional collateral in a short period. This can create a number of challenges:

Funding Stress ▴ Clearing members must have access to a sufficient amount of high-quality liquid assets (HQLA) to meet these margin calls. During a market-wide stress event, the availability of HQLA may be constrained, and the cost of funding can increase significantly.
Asset Fire Sales ▴ If a clearing member does not have enough HQLA on hand, it may be forced to sell other assets to raise cash. These “fire sales” can depress asset prices, further increasing market volatility and potentially triggering even more margin calls.
Contagion Risk ▴ The liquidity pressures on one clearing member can spill over to other market participants. If a clearing member is unable to meet its margin calls, it could default, leading to losses for the CCP and potentially triggering a wider financial crisis.

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Navigating the Procyclicality Challenge

The challenge for CCPs and regulators is to balance the need for risk-sensitive margin models with the need to mitigate the procyclical effects of these models. A number of strategies and tools have been developed to address this challenge. These can be broadly categorized into two groups ▴ those that aim to make margin models less procyclical in the first place, and those that aim to provide clearing members with more flexibility in meeting margin calls.

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Anti-Procyclicality Tools for Margin Models

Regulators have mandated that CCPs incorporate anti-procyclicality (APC) tools into their initial margin models. These tools are designed to smooth out changes in margin requirements and prevent them from becoming excessively volatile. Some of the most common APC tools include:

Margin Buffers ▴ This involves applying a buffer to the calculated margin during normal market conditions. This buffer can then be drawn down during periods of stress, reducing the need for large, sudden margin calls.
Floors ▴ A floor sets a minimum level for initial margin, even during periods of very low volatility. This prevents margin from falling to a level that would require a very large increase in the event of a market shock.
Stressed Value-at-Risk (SVaR) ▴ This involves calibrating the VaR model to a period of significant financial stress. This ensures that the model is always taking into account the potential for a large market shock, even during calm periods.
Weighting of Stressed Observations ▴ This approach involves assigning a higher weight to stressed periods in the lookback window used to calibrate the margin model. This makes the model more sensitive to potential tail risks.

The following table provides a comparison of these different APC tools:

APC Tool	Description	Advantages	Disadvantages
Margin Buffers	A buffer is added to the calculated margin during normal market conditions.	Can be effective in smoothing out margin calls.	Can be difficult to calibrate correctly.
Floors	A minimum level is set for initial margin.	Simple to implement and can be very effective in preventing margin from falling too low.	Can lead to over-margining during periods of low volatility.
Stressed VaR	The VaR model is calibrated to a period of significant financial stress.	Ensures that the model is always taking into account the potential for a large market shock.	Can be difficult to choose an appropriate stress period.
Weighting of Stressed Observations	A higher weight is assigned to stressed periods in the lookback window.	Makes the model more sensitive to potential tail risks.	Can be difficult to determine the appropriate weighting.

The goal of anti-procyclicality tools is to create more stable and predictable margin requirements.

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The March 2020 Case Study

The market turmoil in March 2020 provided a real-world stress test for the post-crisis reforms of the derivatives market. The sudden and extreme increase in volatility led to a massive increase in initial margin requirements. According to the FIA, the aggregate amount of initial margin at nine major CCPs increased by $270.3 billion, or 48%, in the first quarter of 2020. This created significant funding pressures for clearing members and their clients.

The following table shows the increase in initial margin at some of the major CCPs during the first quarter of 2020:

CCP	Initial Margin (Q4 2019)	Initial Margin (Q1 2020)	Increase
CME	$205.2B	$230.7B	$25.5B
Eurex	$76.0B	$96.5B	$20.5B
ICE Clear Europe	$75.0B	$68.5B	-$6.5B
LCH LTD	$204.6B	$206.3B	$1.7B

The March 2020 event highlighted the importance of effective APC tools. While the cleared derivatives markets ultimately withstood the stress, the experience has led to a renewed focus on the procyclicality of initial margin requirements and the need for further improvements to the existing framework.

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Execution

Advanced Modeling and Procyclicality Mitigation

Beyond the standard APC tools, there is a growing body of research on more advanced modeling techniques for mitigating procyclicality. These techniques aim to create more sophisticated and adaptive margin models that can better capture the nuances of market dynamics and reduce the need for large, disruptive margin calls.

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Asymmetric Volatility Models

One promising area of research is the use of asymmetric volatility models, such as the Glosten-Jagannathan-Runkle GARCH (GJR-GARCH) model. These models are designed to capture the “leverage effect,” which is the tendency for volatility to be higher in response to negative returns than to positive returns. By incorporating this asymmetry into the margin model, it is possible to create a more accurate and responsive measure of risk that is less prone to procyclicality.

The following is a list of some of the key features of asymmetric volatility models:

Leverage Effect ▴ These models explicitly account for the fact that negative shocks to asset prices tend to have a larger impact on volatility than positive shocks.
Time-Varying Volatility ▴ These models allow volatility to change over time, which is a more realistic assumption than the constant volatility assumption of simpler models.
Improved Forecasting ▴ By capturing the leverage effect and time-varying volatility, these models can provide more accurate forecasts of future volatility, which can lead to more stable and predictable margin requirements.

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Loss Function Analysis

Another important tool for evaluating and improving initial margin models is loss function analysis. A loss function is a mathematical tool that can be used to quantify the trade-off between two competing objectives, such as risk sensitivity and procyclicality mitigation. By defining a loss function that incorporates both of these objectives, it is possible to evaluate the performance of different margin models and identify the one that provides the best balance between the two.

Advanced modeling techniques can help to create more stable and predictable margin requirements, reducing the risk of procyclicality.

The use of advanced modeling techniques, such as asymmetric volatility models and loss function analysis, is still in its early stages. However, these techniques have the potential to significantly improve the way that initial margin is calculated and reduce the risk of procyclicality in the financial system.

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References

Murphy, D. Vasios, M. & Vause, N. (2014). An investigation into the procyclicality of risk-based initial margin models. Financial Stability Paper No. 29. Bank of England.
Goldman, E. & Shen, X. (2020). Procyclicality mitigation for initial margin models with asymmetric volatility. Journal of Risk, 22(5), 1-41.
Rustad, N. (2020). March Volatility and Clearing. Global Markets Advisory Committee, Commodity Futures Trading Commission.
Wendt, F. (2021). A Regulator’s Perspective on Anti-Procyclicality Measures for CCPs. WFE Focus.
Committee on Payment and Settlement Systems & Technical Committee of the International Organization of Securities Commissions. (2012). Principles for financial market infrastructures. Bank for International Settlements.

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A Systemic View of Liquidity Risk

The procyclicality of initial margin requirements is a complex issue with no easy solutions. It highlights the interconnectedness of the financial system and the potential for well-intentioned risk management practices to have unintended consequences. As we move forward, it is essential that we take a systemic view of liquidity risk and work to develop a more holistic and resilient framework for managing it. This will require a collaborative effort from CCPs, regulators, and market participants, as well as a continued investment in research and innovation.