Nicholas Vause and David Murphy
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 (Covid) global pandemic in March 2020. This reinvigorated the debate about dampening such ‘procyclicality’ of margin requirements. In a recent paper, we suggest a cost-benefit approach to mitigating margin procyclicality, whereby alternative mitigation strategies would be assessed not only in terms of the reduction in procyclicality they would deliver (the benefit), but also any increase in average margin requirements over the financial cycle (the cost). Strategies with the best trade-offs could then be put into practice. Our procyclicality metrics could also be used to report margin variability to derivative users, assisting them with their liquidity risk management.
As financial market volatility spiked amid the onset of the Covid global pandemic, initial margin requirements increased sharply, doubling or even tripling for some exchange-traded derivatives (Figure 1). Counterparties to these derivatives had to either meet the increased collateral requirements of the exchange’s clearing house or reduce their positions, which could have further increased market volatility.
Figure 1: Initial margin requirements for selected exchange-traded futures in 2020 H1
Sources: CME Group and Bank calculations.
(a) S&P 500 future.
(b) 10-year US Treasury note future.
(c) US$/JP¥ future.
(d) WTI oil future.
This led to calls from both market participants and regulators to investigate whether margin procyclicality could be reduced. The Futures Industry Association ‘urge[d] all stakeholders in the global clearing system to consider what steps [could] be taken to mitigate the procyclicality of margin models’, while the Chair of the Committee on Payments and Market Infrastructure argued that ‘we need to dampen down as far as possible procyclical effects without reducing appropriate protection against counterparty risk’.
That caveat is important. Margins provide protection against counterparty risk, as derivative users can claim this collateral posted by counterparties should they default on their obligations. As the potential value of those obligations typically increases with market volatility, margin requirements naturally tend to rise at times of stress. Hence, in our cost-benefit analysis (CBA), we stipulate that margin requirements after any procyclicality mitigation strategy should pass backtests to help ensure that counterparty risk remains adequately covered.
Cost-benefit analysis
We use two metrics to measure the benefits of alternative mitigation strategies in reducing procyclicality. The first is the large-call (LC) metric. Essentially, this measures the largest cumulative increase in margin requirements that could be expected within a 30-day period. The purpose of this metric is to give forewarning of large margin calls like those seen in March 2020. Indeed, our choice of horizon was motivated partly by the period over which these margin calls materialised, as well as the 30-day horizon of the Liquidity Coverage Ratio, which is a regulation to help ensure banks can cope with liquidity stresses. The second measure is the peak-to-trough (PT) metric. Essentially, this measures the ratio of maximum to minimum margin requirements over the financial cycle. Low requirements at certain points in the cycle allow market participants to take large derivative positions relative to their equity, but these could be difficult to maintain if requirements were much higher at other points in the cycle. High values of the PT metric capture this risk. Note, the ‘essentially’ phrase in the definitions of the LC and PT metrics reflects that we take our measurements a little inside the extremes, where there is less statistical uncertainty.
We capture the cost of procyclicality mitigation strategies in a single metric, average cost (AC). This is the average amount of collateral required over the financial cycle. The expense of meeting this requirement would be the cost of funding the collateral assets, so market participants might prefer to multiply AC by a cycle-average interest rate. However, that would not affect the relative costs of alternative mitigation strategies. Figure 2 illustrates the LC, PT and AC metrics for a simple single-derivative portfolio.
Figure 2: Cost and benefit metrics for a single US$/JP¥ FX futures portfolio
Sources: Federal Reserve Economic Data and Bank calculations.
(a) Large-call procyclicality is the 99.7th percentile of increases in margin requirements within a 30-day period.
Anti-procyclicality
We study six different procyclicality mitigation strategies, though the same CBA could be applied to others. One strategy adjusts parameters of the margin model, while the others supplement it with an anti-procyclicality (APC) tool.
For illustrative purposes, we use a relatively simple margin model. This generates margin requirements that are proportional to the volatility of portfolio returns, which in turn is calculated using an exponentially weighted moving average (EWMA) model. This relates today’s volatility to yesterday’s volatility and today’s portfolio return, with the relative weight on the yesterday’s volatility known as the decay parameter. Our first mitigation strategy increases this parameter, so margin requirements vary less from day to day.
Three of our APC tools reflect those in European Market Infrastructure Regulation (EMIR). The first is the ‘floor’ tool, which sets margins equal to the higher of the floor or the requirements from the no-mitigation model. The second is the ‘buffer’ tool, which adds a constant proportion to the no-mitigation margin requirement and shrinks this buffer when that requirement is relatively high. The third is the ‘stress-weight’ tool, which forms margin requirements as a weighted average of the no-mitigation requirement and the requirement of a previous high-stress day.
We also investigate two further APC tools. One is an adaptive variant of the stress-weight tool, which increases the weight on the no-mitigation margin requirement as that requirement rises towards or even beyond the high-stress requirement. This addresses a shortcoming of the stress-weight tool, which sees margin requirements pulled below no-mitigation requirements on days of greater stress than the historical high-stress day. Our final APC tool is the ‘cap’ tool. This is the opposite of the floor tool. It sets margin requirements equal to the lower of the cap or the requirements from the no-mitigation model. By putting an upper limit on margin requirements, the cap is very direct in mitigating procyclicality. However, any miscalibration could reintroduce significant counterparty risk: if the value of derivative exposures increased above the cap, the difference would not be backed by initial margin and any losses would instead fall to the default fund of the clearing house.
Figure 3 shows the costs and benefits of the alternative procyclicality mitigation strategies applied to our simple single-derivative (US$/JP¥ future) portfolio. Each of these strategies passes a simple backtest, suggesting the resulting margin requirements still cover counterparty risk to an adequate degree. The model recalibration strategy (ie increased decay parameter) (pink dots) reduces both LC and PT procyclicality substantially and at relatively low cost, demonstrating that procyclicality reduction does not necessarily require an explicit APC tool. The cap tool (dark purple dots) also performs relatively well, notably reducing LC procyclicality by much more than the floor tool. This is because large margin calls often reflect jumps in margin requirements from average to high levels and the cap binds against high requirements while the floor does not bind against average requirements. The EMIR tools (in different shades of blue) are successful in reducing both LC and PT procyclicality to varying degrees, albeit at higher costs than some of the other mitigation strategies.
Figure 3: Comparison of procyclicality cost trade-off across procyclicality mitigation strategies(a)
Sources: Federal Reserve Economic Data and Bank calculations.
(a) λ is the decay parameter in the EWMA volatility model.
Conclusions
While these results are specific to our simple portfolio, in the paper we perform similar cost-benefit analyses on other portfolios with different return characteristics, such as volatility, skewness and kurtosis. The relative performance of our alternative mitigation strategies varies somewhat across these portfolios, suggesting that regulators might prefer an outcome-based to a methodology-based approach to procyclicality mitigation. That is, regulators could set targets for procyclicality mitigation (eg reduce LC and/or PT procyclicality to particular levels) and allow clearing houses to find the least-cost strategy for achieving those targets while still meeting back-testing requirements, rather than prescribe the use of particular procyclicality mitigation tools.
Having settled on a preferred strategy, we suggest that clearing houses should make available to their counterparties measures of LC and PT procyclicality for their specific portfolios. These could be calculated using historical data, as above. This information could help derivative users to decide how much liquidity to carry for potential large margin calls and how much leverage to take on when establishing their positions.
Nicholas Vause works in the Bank’s Capital Markets Division and David Murphy works at London School of Economics and Political Science. David worked in the Bank’s Prudential Policy Directorate when this work was undertaken.
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