Replicating CCP methodologies for validation, pricing and ‘resource management’

Replicating CCP methodologies not only improves control through validation it also enables the development of sophisticated portfolio measures, analogous to CVA, for pricing and managing the cost of clearing. Early
April 7, 2014 - Editor
Category: Clearing

Replicating CCP methodologies not only improves control through validation it also enables the development of sophisticated portfolio measures, analogous to CVA, for pricing and managing the cost of clearing. Early adopters are already considering how pricing in the ‘Margin Value Adjustment’ can help efficiently manage their OTC derivatives portfolios.

Replicating CCP methodologies not only improves control through validation it also enables the development of sophisticated portfolio measures, analogous to CVA, for pricing and managing the cost of clearing. Early adopters are already considering how pricing in the ‘Margin Value Adjustment’ can help efficiently manage their OTC derivatives portfolios.

Banks have become very sophisticated in how they capture and optimise the use of key economic resources like funding value adjustment (FVA), credit value adjustment (CVA), capital and balance sheet across their business. Many have set up dedicated ‘Resource Management Units’ or similar to perform this function. Mandatory clearing means that such resource costs associated with portfolios of OTC derivatives are increasingly sensitive to the risk methodologies employed by CCPs, most notably for initial margin.

In this article we explore the opportunities presented by adopting novel risk measures based on replicating CCP methodologies. These include traditional control functions like validating margin calls. But perhaps the real payoff is in using simulation techniques to understand the marginal lifetime cost of funding a cleared position, leading to differential pricing and balance sheet optimisation of OTC derivatives portfolios. Firstly, let’s consider the methodologies themselves.

CCP methodologies
CCPs aim to reduce systemic credit risk in the markets they serve by redistributing losses due to member default and preventing vicious circles of contagion and panic during times of market stress. They achieve this by maintaining various risk sensitive buffer funds designed to be used up in a specific order, in a ‘risk waterfall’ arrangement. CCP members contribute to these funds, and are exposed to their loss in different ways governed by legal agreements and now by regulatory standards. 

The exact nature of the risk waterfall will vary from CCP to CCP, although regulatory standards are driving commonality. Generally speaking the order is guided by a ‘defaulter pays first’ philosophy, incentivising ‘good behaviour’ and reducing stress on surviving members. A typical structure may be defaulter’s variation margin, defaulters’ initial margin, defaulter’s default fund contribution, CCP capital, survivors default fund contributions, used in that order. Further arrangements can be made for loss sharing in cases of such extreme loss that the CCP service is wound up.

The methodologies employed to determine the contributions to these funds are clearly important for members to understand. Although some of these are quite opaque, we believe that banks are increasingly anxious to understand the basis on which they are calculated. Taking them in order:

  1. Variation margin is generally quite transparent as it is simply the mark to market of the portfolio using either public market prices, or derived from observable market data. This should tie in closely with the bank’s own valuations.
  2. Initial margin is designed to cover close out risk and will generally be calculated using a VaR type approach. Regulatory standards now stipulate confidence levels and close out periods as well as parameters defining lookback periods. The level of transparency varies significantly between CCPs. Most provide some kind of ‘what if’ tool as part of their reporting suite but these are not truly independent. While some CCPs publish their methodologies and much of the required input data, others are completely opaque. Even at the more transparent end, CCPs may include add-ons and multipliers in IM calls, for example based on bank’s relative size in the market, which they reserve the right to vary as they see fit. These can make the behavior of IM in extreme circumstances difficult to predict.
  3. Default fund contributions are generally opaque, although we expect them to be a function of initial margin and possible relative size in the market. As such they cannot be easily replicated without access to privileged data that CCPs are required to protect.

Should these funds prove insufficient, CCPs have legal rights to call more capital, either through requiring replenishment of depleted default fund from members or using bail in from shareholders. These are difficult to model though banks need to be aware of the liabilities they create. Beyond their ability to recapitalise themselves, CCPs may distribute remaining losses on their members by winding up their clearing services, although these should be viewed as very extreme events. For a large CCP their occurrence would probably indicate a more or less total collapse of the global financial system.

Although elements of IM and default fund methodologies remain unpublished, and their behaviour in extreme circumstances difficult to predict, banks can collect time series data during business as usual, and may be able to use these to build empirical models. Such models could be quite accurate during normal market conditions, and so provide useful information for day to day purposes. We now consider some applications of replicating CCP methodologies

Margin Validation
CCPs make margin calls to their members, and are sometimes legally empowered to execute payment on member’s behalf. Many banks have no systematic way of checking these amounts – they simply pay them out, day after day, on the assumption that the CCPs are doing their sums right. However CCP systems and people are not infallible. There are many ways that their calculations can go wrong and lead to erroneous calls. Clearly ‘over calls’ are a worry but banks should also be concerned about ‘under calls’, after all, IM is protecting them from the failure of other banks.

There is little that can be done on the day about erroneous calls, and these cannot necessarily be unambiguously detected. However Margin replication can still highlight CCP errors and aid in the recovery of interest on over calls. Further, by implementing well controlled margin replication and daily reporting members are not only protecting themselves against potential losses, they are incentivising the CCP to ensure the accuracy of their calls, helping to ensure the integrity of the system as a whole.
Validation is perhaps the most obvious use case for IM replication, however we are increasingly seeing the front office take an interest in the potential application of IM calculation to pricing, which we will consider now.

Comparative portfolio measures and optimisation
Optionality now exists on how OTC derivatives are cleared. For example, certain interest rate swaps are now accepted at multiple CCPs. For certain counterparties banks will retain legacy ‘old style’ Credit Support Annexes (CSAs) while signing the ‘new style’ CSAs that require initial margin, becoming mandatory under regulations.

An un-collateralized bilateral derivative position attracts a CVA charge, as does the period of risk exposure even with collateral under old style CSAs. In most banks the mechanisms for capturing this charge and ensuring trader’s P&L accurately reflects credit risk are well established. Many also calculate CVA pre-execution and integrate it into pricing. CVA is a portfolio measure, where the important number is the marginal effect of the new trading activity on the portfolio CVA, rather than the standalone CVA for that trade.

A cleared trade does not attract CVA in the same way – the counterparty risk is mitigated by clearing – but there is still an analogous ‘charge’ resulting from the need to pay IM (to a CCP or bilateral counterparty). This charge is the marginal impact of the new trading activity on the cost of funding the IM (and other funds) called against the portfolio by CCPs. In other words, every new trade, or trading event, will have an impact on the amount that the portfolio IM will cost to fund over its lifetime. Clearly this number is related to IM somehow but can it be calculated, pre execution, and incorporated into pricing in the same was as CVA?

Were it possible to calculate such a measure for the various clearing venues available, prior to execution, then firms would have the ability to offer different prices for different trading venues. The clearing cost add-ons would be sensitive to the overall swap portfolio between the bank and its counterparty, incentivising the creation of the most balance sheet efficient distribution across venues. Such optimisation could also be carried out as a bulk process. An optimisation algorithm run on an existing portfolio on the introduction of a new clearing venue could crunch the numbers and generate suggested novation opportunities. So how could such comparative measures for the cost of CCP (and IM sensitive bilateral clearing) clearing be calculated?

A measure of IM sensitive clearing cost – The 'Margin Value Adjustment'
The cost of funding IM for a given portfolio is a function of the IM posted over the entire lifetime of the portfolio, not just the amount calculated today. Assuming that the ‘Instantaneous IM’ is some kind of VaR, then calculating it requires the valuation of all trades in the portfolio, and the application of an array of yield curve shifts, representing historical or simulated scenarios, and finding the maximum net move for the portfolio. To build an IM profile, the VaR would need to be considered over simulated future evolutions of the yield curve shift array.

The use of risk neutral models to simulate the evolution of interest rates is well established. Monte Carlo techniques are used to create many simulation paths, and first order moments used to estimate values such as Potential Future Exposure (PFE), Expected Positive Exposure (EPE) and Expected Negative Exposure (ENE). Using a similar approach an IM profile could be generated, using ‘historical’ scenarios augmented within each path on a rolling basis. The total funding charge could then be estimated by integrating using a funding curve in a similar way to CVA or FVA. The marginal impact is then the effect of a new trading activity on this portfolio measure.

Measures for the default fund would be less exact as the methodologies for these are generally not published, but using data that is available, empirical models could be calibrated and the lifetime funding costs estimated. Armed with such analytics, the front office would have a powerful tool for managing the cost of clearing in a similar way to other key economic resources. Further, by comparing portfolio costs across different clearing venues large portfolios can be optimised, ensuring the most efficient use of key economic resources given the available clearing options.

Development Approaches

As with many new challenges and opportunities there are various development approaches available to banks. Some may choose to implement in house solutions,perhaps leveraging existing CVA architecture. Others, in particular those who’s risk architecture is based on a vendor platform will look to specialist software houses for a solution. We are already seeing several vendors offer products in this area, some of which have already been implemented by a few early adopters.

The costs of doing business in OTC derivatives markets are increasing. Regulatory changes, tightening bid-offer spreads and expensive funding mean that key economic resources need to be carefully managed. Costs will vary across clearing venues, both CCP and bilateral. There is a growing need for analytics that model these costs, and enable banks to manage risk and price competitively. As is often the case, early adopters are likely to have an advantage.

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