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Managing Margin (Part 4) – The Future of Margin – Ten Predictions

Thus far in our series on Margin, we have discussed a range of challenges involved in building and managing a margin system that covers the spectrum of business and risk management needs. In this last installment, we offer ten predictions about future developments in the margin arena. Some we expect with a fair amount of certainty, while others require us to gaze more deeply into our crystal ball; nonetheless, we believe all are legitimate and, we hope, thought-provoking.

  1. Margin and clearing will continue to move toward “real-time” settlement. In the future, real-time data and calculations will allow the parties to a transaction, and their clearinghouses, to confirm that funds are available, permitting transfers throughout the day, rather than at the end of the day based on aggregated transactions. Firms may need to post margin, including variation margin, multiple times in a single day. As a corollary, broker-dealers’ margin systems will notify their clients as to how much additional margin they will have to post, in close to real-time. We note that the Australian Stock Exchange is already settling stock transactions intra-day, when the cash is made available.
  2. Firms will rely on frequent intraday alerts. Related to the development of real-time information, margin systems will show brokers when clients are nearing a threshold that would require them to post more collateral. This will allow brokers to proactively inform their buy-side clients the amount of additional funds that may be needed. More sophisticated pre-trade approval from clearinghouses is the next logical step, after latency issues arising from geographic distances are addressed, such as through the use of co-located systems.
  3. Concentration risk will be monitored across clients and prime brokers. Before extreme moves in GameStop disrupted trading on Robinhood’s platform, brokers did not expect aggregated trading across customers to create concentration risk – holding enough margin per client was assumed to be sufficient to cover a firm’s risk. When individual investors bought GameStop en masse via the Robinhood app and the DTCC significantly increased Robinhood’s capital requirements to cover its settlement risk, it almost bankrupted the firm. Similarly, the concentration of risk across a number of prime brokers in the Archegos debacle highlighted the importance of measuring concentration risk comprehensively.
  4. Cross-margining will increase. Trades involving more than one type of security can result in inefficient use of collateral, as each exchange involved in a transaction must hold enough margin to satisfy its regulators. The CME and Cboe are starting to facilitate cross-margining among securities, futures, and swaps, and we expect to see more of this. This could eventually extend to cross-margining opportunities across borders, although FX risk for collateral posted in different currencies would have to be addressed.
  5. Liquidity modeling will improve VaR-based margin. Correlation spikes (see the Angle of Coincidence) produce losses that exceed VaR-based predictions, and sometimes those correlation spikes are driven by liquidity, or a lack thereof. As liquidity modeling improves, VaR calculations can incorporate liquidity risk and its impact on correlation spikes, to produce better estimates of risk, and thus of needed margin.
  6. Improved estimates will reduce VaR’s inherent “complacency” when volatility is low. An inherent, well-known problem with VaR is that it drops when markets are calm. That means firms that do not incorporate stress-tests into their VaR estimates can be “surprised” by jumps in VaR that occur when volatility spikes and they are required to post more margin. In the future, when volatility declines historical VaR might assign a heavier weight to stress-test scenarios that prevent VaR from dipping too low (i.e., offsetting VaR’s inherent “complacency”). This would include improved liquidity modeling (see #5).
  7. VaR-based margin results will do a better job of incorporating fat tails. The distributions of spreads – such as credit spreads, calendar spreads, commodity spreads, and all types of basis risk – tend to have fatter tails than other types of instruments, and parametric VaR estimates do not incorporate this well. Capital requirements are moving toward risk-based requirements that require instruments to be classified based on their risk profile, rather than type. That will require the ability to classify and model spread distributions more accurately.
  8. Artificial Intelligence will assist. AI (or more specifically, machine learning) is very good at classifying things, and could be used determine the type of risk a position represents, including liquidity risk, thereby improving VaR calculations. Machine learning algorithms for clustering and classifying could enhance the speed with which margin engines classify positions, enabling firms to calculate margin faster.
  9. Money market fund sponsors may attract margin. Money market funds promise not to “break the buck”, but if they hold a fraction of low-quality assets it can cause problems in times of stress – when outflows greatly exceed inflows, funds sell their high quality assets to pay redemptions at 100 cents on the dollar, leaving them with a greater percentage of low-quality holdings that are insufficient to cover remaining investors. This has not yet been addressed by regulators; even institutional money market funds that are allowed to break the buck are not immune – those who reach the exit first benefit at the expense of those who are slow to act. In the future, margin requirements for money market funds may be designed to limit this type of “quality dispersion”.
  10. Quantum computing will improve collateral optimization, including “What if” Margin analyses. When buy-side firms have multiple prime brokers that use different CCPs, margin is not as efficient as it could be. Collateral optimization in a multi-prime broker setting is currently limited to viewing a set of trades as a “block”. This will be greatly improved when quantum computers can solve “NP-hard” (non-deterministic polynomial time – in case you were wondering) problems and allocate the optimal portion of every position to a list of broker-dealers. The ability to do so will permit “What-if” Margin analyses and pre-trade approvals that truly optimizes collateral use by moving specific positions to different broker-dealers.

Lastly, while not a prediction, we offer a cautionary note regarding the non-zero possibility of future controls on capital flows across borders, which would clearly affect margin rules. Pricing and risk assessments assume that capital can flow wherever it is needed. There is an argument to be made that trade imbalances, ultra-low interest rates globally, and quantitative easing, which have contributed to a lack of housing affordability, income inequality, high student loan debt, and large government deficits, can be linked to the unfettered movement of capital across borders. With trade relationships becoming more adversarial and social inequities more acute, certain economies may face political pressure to impose controls on capital flows. If that happens — if investors cannot safely assume their capital can be readily retrieved from other countries — the result would be portfolios that are “balkanized”, greatly reducing cross-border investing and with accompanying implications for margin.


The Imagine platform’s margin system, designed around the concepts of Margin Plans, Margin Rules, and a scalable structure based on criteria, values and overrides, is flexible and adaptable, and can offer truly astonishing improvements that can deliver tangible benefits to those who rely on margin calculations to keep critical functions operating smoothly. For more information about how Imagine’s approach could help your business, please contact us.

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The Margin Series

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