Why liquidity risk is the silent clearing killer

A quant paper shows feedback effects can amplify CCP margin requirements in stressed markets

It is well known that central counterparties (CCPs) can significantly reduce counterparty credit risk. It is the liquidity risk they pose in exchange for this benefit that is becoming a growing concern for market players.

A live example was seen on June 24, the day the Brexit referendum results were announced, when wild moves on a number of markets generated combined margin calls that are estimated to have totalled between $25 billion and $40 billion, with the largest members asked to cough up multiple billions. Initial margin alone was not enough to cover the exposures on the day, so CCPs had to issue ad hoc margin calls, causing funding stress for some members.

Alex Lipton, a connection science and engineering fellow at the Massachusetts Institute of Technology (MIT), argues that for large banks especially, credit risk should be a secondary concern when it comes to clearing.

"The tier one banks, who are the biggest players, will not be toppled by trading through CCPs. But what could happen – and did happen during Brexit – is uncontrolled margin calls. Because of that, there might be a sudden demand for an enormous amount of liquidity, and that is a real danger, which needs to be assessed," says Lipton.

This is easier said than done.

Assessing liquidity risk on a systemic level involves looking at the position of a clearing member during stress and trying to model the domino effect on positions the member has with other CCPs, and contagion effects – if any – on other clearing members at all of these CCPs.

In this month's first technical paper, Systemic risks in CCP networks, Lipton, along with Russell Barker, head of global macro modelling at Morgan Stanley, Andrew Dickinson and Rajeev Virmani, both directors within global risk analytics at Bank of America Merrill Lynch, tackle these problems head-on. They propose a model that replicates the systemic exposure of CCPs by carrying out a massive Monte Carlo simulation of a network of the 10 largest CCPs globally and 101 clearing members.

The end goal is to capture the total loss a CCP can face by simulating close to 10,000 scenarios, taking into account a few key effects the quants claim existing literature ignores due to their complexity.

First is the feedback effect a member default can have on market volatility, which in turn can affect margin calls – in short, the liquidity impact. Market drivers such as rates, spot foreign exchange and defaults are simulated here with jumps to generate the various scenarios and capture the feedback effect.

Second, they factor in the variability in the size of clearing members, which means they aren't all assumed to have equal-sized positions.

This, Lipton argues, is to ensure the individual nature of different clearing members are taken into account. For large financial institutions, cleared trades will only make up a minority of their business, but for proprietary funds, for instance, margin calls can create a much bigger impact.

One key result of the paper is that the feedback effect can dramatically amplify the tail of the loss distribution during default

In an attempt to address sudden margin calls, the quants introduce a ‘regime-switching model', which creates sudden changes in initial margin and default fund contributions when the simulation hits a stressed scenario.

One key result of the paper is that the feedback effect can dramatically amplify the tail of the loss distribution during default.

"One of the important ideas that comes out of the paper is the importance of feedback effects through market volatility – the failure of one clearing member can create a spike in volatility that then risks pushing other members to fail. The paper shows these types of higher-order effects are crucial to understanding the systemic risk of CCPs," says Paul Glasserman, a professor at Columbia Business School.

A more significant finding is that liquidity risk seems to be a bigger contributor to overall loss compared with credit risk. Although the study concludes that CCPs pose little systemic risk overall, it shows the demand for liquidity can grow by a factor of two-to-three over a period of one year because of stress, potentially knocking out smaller players.

This ties up with recent concerns raised by market participants, with some urging regulators to introduce liquidity stress testing for CCPs, in addition to credit. However, both market players and academics have long argued that any kind of modelling will be tricky without better transparency from CCPs and clearing members.

Considering how challenging it will be for all of the pieces to come together, an industry-wide solution, including possible regulatory measures, seems far away.

In the paper, the quants use publicly available information released by clearing houses on specific derivatives categories and extrapolates that information to make assumptions about positions of clearing members. In addition, clearing members have full knowledge of their own positions, which can come in very handy.

For the moment at least, it might therefore make sense for banks to take it upon themselves to study the risk using whatever information they can find, so that when the next margin shock comes, they are fully prepared.