Pedro Gurrola-Perez, Head of Research, WFE

CFTC Global Markets Advisory Committee

March 11th, 2021

PROCYCLICALITY OF CCP MARGIN 

MODELS: SYSTEMIC PROBLEMS 

NEED SYSTEMIC APPROACHES
AGENDA*

• Introduction: why the focus on IM models is misplaced

• An empirical case study

• Worst-case scenario analysis

• Complexity

• Conclusions

2

*The full report is available at:

https://www.world-exchanges.org/storage/app/media/Procyclicality_cut7.pdf
INTRODUCTION

• After the 2007-08 crisis, there were concerns that, in a stress, margin calls would put 

further pressure on clearing members, creating a feedback loop that could 

deteriorate the situation.

• Since then, CCPs have implemented different procyclicality mitigation measures, 

including monitoring procyclicality levels, recalibrating their margin models, and 

increasing transparency and reporting.

• Despite these measures, after the March 2020 events, there have been renewed 

claims regarding the procyclicality of initial margin (IM) models. 

• The debate ignores the trade-offs and constraints involved, including any 

assessment of the costs and financial stability implications.

3
THE FOCUS ON IM IS LARGELY MISPLACED

• Margin calls are mainly about variation margin (VM), not IM. They can also be driven by 

changes in the portfolio.

• Unavoidable trade-offs and constraints: Since market risk models need to be risk sensitive 

and central clearing needs to be economically efficient, there is a limit to what can be 

achieved.

• Randomness: The same calibration may produce different outcomes under different 

initial conditions. Prescribing calibrations does not make the unpredictable any more 

predictable.

• Volatility clustering: It is critical to distinguish between long-term properties (for which, for 

example, extending the lookback period may be useful) from those that reflect market 

conditions at a given point in time (e.g., conditional volatilities)

• Complexity: The problem is that feedback loops are amplified through system 

interactions; therefore, we should look for solutions that address system-wide robustness. 

4
CASE STUDY: THE MARCH 2020 EVENTS

To illustrate the points above, we will consider an empirical example: 

• Data: S&P500 daily prices (15 years, from January 2005 to May 2020) 

• Models: Historical simulation VaR (HS VaR) and filtered historical 

simulation (FHS VaR)

• Single-tailed 99% confidence level

• Lookback periods of 1-, 10- and 12-years.

• One-day MPOR

• Decay factor (for FHS models)=0.985

• We are working with a constant portfolio, therefore the volatility 

observed is only caused by changes in market prices.

5
WHY CONDITIONAL VOLATILITY MATTERS

6

• The spike in (conditional) volatility observed in March 2020 reaches a maximum which is 6.5 

times larger than the long-term (unconditional) volatility.

• Conditional volatilities vary through time. 

• Crucially, the resilience of the CCP (and of the system) does not depend on long-term 

properties but on getting the numbers right under the prevailing market conditions at a specific 

point in time.
MODEL PERFORMANCE MEASURES

• Coverage: Number and size of backtesting breaches. 

• Procyclicality: 

• Peak-to-trough (PT) ratio: the ratio of the maximum initial margin required 

to the minimum margin required during the period.

• n-day measure: the largest increase in margin over an n-day period 

assuming a constant portfolio. It is a relative measure of the speed of 

change. We will consider n=1 and n=5.

• Cost of over-margining: measured as the average excess 

margin C(M) (it is not an estimate of the real costs of posting 

collateral to the CCP, but only a way of comparing two 

models in terms of how much they over-margin)

7
LESS PROCYCLICALITY VS ADEQUATE COVERAGE

8

• No significant difference between 10- and 12 -years FHS 

(even though the 12-year captures the 2008 stress)

• The 1-year is more procyclical but produces significantly 

smaller breaches

• This illustrates the contradiction in simultaneously asking 

for low procyclicality and smaller/less breaches. 

• Similar issues if we instead vary the decay factor λ Given the magnitude of the shock, would 

adding/increasing a floor make a 

significant difference? 

At what cost?
LESS PROCYCLICALITY VS VIABLE CLEARING COSTS

We would need to increase the floor by a factor of around 2 before we 

see any significant reduction in procyclicality

9

Baseline: We assume a 10-year HS VaR floor
WORST-CASE SCENARIO

• We consider the potential losses had the member defaulted on any 

day during March 2020.

• We will consider the FHS models together with three standard antiprocyclicality (APC) tools:

• Buffer: 25% additional IM.

• Stress: We estimate VaR in a stressed period (VaRstressed), and we consider the 

weighted average

SVaR=0.25 x VaRstressed + 0.75 x FHSVaR

• Floor: We use the 10-year HS VaR as a floor. 

10
MODEL PERFORMANCE (1-YEAR LOOKBACK):

None of the APC tools 

can prevent having 

large margin increases

11
...AND SIMILARLY FOR THE 10-YEAR LOOKBACK

12

``In the March market turmoil, IM increased 

sharply after a few days, which suggests that 

APC tools were able to dampen or slow 

down the IM increase only for a short time 

period" (FSB, 2020)
LOSSES ASSUMING A DEFAULT ON DAY T*

13

• 10-year FHS model produces losses that can be 64% larger compared to the 1-year one

• SVaR tends to perform better but not always.

*Losses estimated assuming one S&P500 E-mini futures contract long position.
IN SUMMARY:

14

• We face a three-way set of 

trade-offs

• We have constraints on costs 

and coverage

• + Relations f and g are nonlinear

• + Randomness: Different 

scenarios will produce different 

relations f and g, and different 

solution sets

• + Different risk factors will have 

different dynamics

Smoothing 

the risk 

model

More risk to the CCP 

and to the system 

More 

Procyclicality

Higher costs of 

clearing

f

Increasing 

floor

Maximum 

viable cost

Minimum 

acceptable 

coverage

f

g
COMPLEXITY

• In a complex system, procyclicality is only one among the possible 

mechanisms that may contribute to the propensity to generate adverse 

feedback loops. 

• Reductive approaches do not work because the safety of the system is a 

consequence of the interactions and interdependencies. 

• It is important to also consider the incentives and behaviours: artificially 

"smoothing" the IM requirements may induce moral hazard in CCP 

members and lead to additional risk taking.

• Procyclicality mitigation is one layer of defence but others are needed, 

including market participants ensuring their liquidity management 

strategies take account of the possibility that margin requirements may rise 

significantly during periods of market stress, or liquidity-focused 

macroprudential stress tests. 

15
CONCLUSIONS

• We all agree IM models should be calibrated to address procyclicality to the extent that it is 

prudent and practical. 

• But, if at the end of the day, fragilities in the system remain that contribute to adverse liquidity 

feedback loops, what else we need to do?

• Given the limitations discussed, the answer cannot simply be to impose further constraints into 

the IM models. 

• Constraining the ability of the CCP to set prudent but adequate margins has the double 

negative effect of incentivizing risk-taking while curtailing the ability of the CCP to correctly 

collateralize its exposures. Due weight should be given to the role of CCPs in ensuring the right 

resources are in the right place at the right time, holding risk takers to account. 

• While the tensions and trade-offs we have discussed will not go away, they would be better 

addressed by acknowledging that adverse feedback loops are a consequence of interactions 

across the system and, as such, that the problem requires system-wide solutions.

16