A cost–benefit analysis of anti-procyclicality: analyzing approaches to procyclicality reduction in central counterparty initial margin models

David Murphy; Nicholas Vause

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There are two different aspects to initial margin model procyclicality. The first is the tendency of margin models to go too low in very placid markets, creating the potential for large margin increases when markets become more volatile. The second is the potential for models to overreact in high stress, generating large margin calls in short periods of time.
These two aspects are captured by the difference between margin in the peak and the trough of the cycle and the size of the largest increase in margin during it, respectively. The paper formalises measures of these aspects.
We suggest that initial margin models should be assessed against our proposed procyclicality metrics. If the results are too high, mitigation should be imposed. This could take the form of model recalibration or the use of an explicit anti-procyclicality tool. Thus, regulation should adopt an outcomes-based approach.
The cost of procyclicality mitigation is an important factor in deciding on the mitigation chosen. Different tools offer a different balance between procyclicality mitigation and cost. Exploring this balance across time and portfolios gives important insight into the behavior of the alternatives.

Abstract

Following a period of relative calm, many derivative users received large margin calls as financial market volatility spiked amidst the onset of the Covid-19 global pandemic in March 2020. This reinvigorated the policy debate about dampening such “procyclicality” of margin requirements. In this paper, we suggest how margin setters and policy makers might measure procyclicality and target particular levels of it by recalibrating parameters in a margin model to reduce its procyclicality or by applying an anti-procyclicality tool. The different options reduce procyclicality by varying amounts, and do so at different costs, which we measure using the average additional margin required over the cycle. This allows us to perform a cost–benefit analysis of the options. We illustrate this using a commonly used type of initial margin model, called filtered historical simulation value at risk. We present the costs and benefits of varying a key model parameter and applying a number of different antiprocyclicality tools to this model, including those in EU legislation. Once margin setters have settled on a model design that manages procyclicality at an acceptable cost, we suggest that they should disclose to their counterparties the behavior of their preferred models, including the potential margin calls they generate in stress, as an aid to liquidity planning.

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