Finding Acceptable CCP Margin Model Parameters

The mission of theMIT Golub Center for Finance and Policy (GCFP)is to serve as a catalyst for innovative, cross-disciplinary and non-partisan research and educational initiatives that address the unique challenges facing governments in their role as financial institutions and as regulators of the financial system.

MIT Golub Center for Finance and Policy

The advent of mandatory central clearing for certain types of over-the-counter derivatives and margin requirements for others means that margin is the most important mitigation mechanism for many counterparty credit risks. Initial margin requirements are typically calculated using risk-based margin models, and these models must be tested to ensure that they are prudent. However two different margin models can calculate substantially different levels of margin yet both pass the usual tests. A more discriminating tool is therefore needed to justify the choice of parameters used in margin models.
A key feature of initial margin models is that they must predict the potential loss that a portfolio might suffer over some margin period of risk to a specified level of confidence. Often this prediction relies on the estimation of the conditional volatilities of various pertinent risk factors or on the definition of a set of risk factor returns to characterise their future dynamics. We show how to construct a probability distribution for the worst loss experienced over the margin period of risk in liquidating a portfolio conditional on either of these types of estimate. This allows us to calculate the probability of the empirical worst loss, and thus to construct a test based on the accuracy of the inferred distribution of worst losses.
The test proposed is used on a variety of volatility estimation and margin calculation techniques applied to a long history of the returns of the S&P 500 index. Exponentially weighted moving average volatility estimation, generalised autoregressive conditional heteroskedasticity approaches and filtered historical simulation value-at-risk models are all considered. In each case a range of model parameters which give rise to acceptable risk estimates is identified.
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David Murphy is the senior advisor in the Prudential Policy Directorate at the Bank of England where he focusses on derivatives and quantitative risk management issues. He is the author of numerous articles on risk management, financial regulation and market infrastructure, recently focussing on central clearing of OTC derivatives and its consequences. He co-chaired the FSB’s Derivatives Assessment Team, whose final report on the post-crisis OTC derivatives market reforms appeared in November 2018. His most recent book, OTC Derivatives: Bilateral Trading and Central Clearing, was published by Palgrave Macmillan in 2013.
His prior roles include Global Head of Risk at International Swaps and Derivatives Association (ISDA), where he was responsible for all of ISDA’s activities relating to capital, risk management and accounting standards as well as leading ISDA’s research effort.
Before that, Dr. Murphy was the Chief Operating Officer at Merrill Lynch’s Reinsurance Group, and he has held a number of other senior risk management roles in leading banks and broker/dealers. He holds PhD and MSc degrees in theoretical computer science and an MA in Physics.

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Finding Acceptable CCP Margin Model Parameters

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