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Assessing central counterparty margin coverage on futures contracts using GARCH models

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  • Raymond Knott
  • Marco Polenghi
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    Abstract

    This study considers how the probability of exceeding central counterparty (CCP) initial margin levels can be estimated, in order to provide a timely and informative measure of risk coverage. Previous studies of CCP margining have largely focused on the unconditional distribution of returns, estimating margin coverage on a long-term average basis. The present study extends previous work by estimating conditional margin coverage using a GARCH (1,1) model, so that variations in coverage can be tracked over a much shorter time frame. The model is applied to estimating non-coverage probabilities for two heavily traded derivatives contracts, the Brent and FTSE 100 futures. To account for the well-documented fat-tailed characteristics of distributions of futures returns, several variants of the GARCH model are estimated. These assume that innovations are distributed according to either normal, Student t, extreme value or historical distributions. Backtesting is used to select the best performing distribution. During the sample period, margins are found to provide a coverage level generally in excess of 99%, over a one-day time horizon. It is noted, however, that the coverage probability implied by the model is likely to fall under more volatile market conditions; under these circumstances central counterparties will reset initial margin more frequently and call for margin intraday.

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    File URL: http://www.bankofengland.co.uk/research/Documents/workingpapers/2006/WP287.pdf
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    Bibliographic Info

    Paper provided by Bank of England in its series Bank of England working papers with number 287.

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    Date of creation: Jan 2006
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    Handle: RePEc:boe:boeewp:287

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    1. Giovanni Barone-Adesi & Kostas Giannopoulos & Les Vosper, 2002. "Backtesting Derivative Portfolios with Filtered Historical Simulation (FHS)," European Financial Management, European Financial Management Association, vol. 8(1), pages 31-58.
    2. Drees, Holger & Kaufmann, Edgar, 1998. "Selecting the optimal sample fraction in univariate extreme value estimation," Stochastic Processes and their Applications, Elsevier, vol. 75(2), pages 149-172, July.
    3. G. Geoffrey Booth & John Paul Broussard & Teppo Martikainen & Vesa Puttonen, 1997. "Prudent Margin Levels in the Finnish Stock Index Futures Market," Management Science, INFORMS, vol. 43(8), pages 1177-1188, August.
    4. Cotter, John, 2000. "Margin Exceedences for European Stock Index Futures using Extreme Value Theory," MPRA Paper 3534, University Library of Munich, Germany, revised 2001.
    5. Huisman, Ronald, et al, 2001. "Tail-Index Estimates in Small Samples," Journal of Business & Economic Statistics, American Statistical Association, vol. 19(2), pages 208-16, April.
    6. Paul H. Kupiec, 1997. "Margin requirements, volatility, and market integrity: what have we learned since the crash?," Finance and Economics Discussion Series 1997-22, Board of Governors of the Federal Reserve System (U.S.).
    7. Olivier V. Pictet & Michel M. Dacorogna & Ulrich A. Muller, 1996. "Hill, Bootstrap and Jackknife Estimators for Heavy Tails," Working Papers 1996-12-10, Olsen and Associates.
    8. Broussard, John Paul, 2001. "Extreme-value and margin setting with and without price limits," The Quarterly Review of Economics and Finance, Elsevier, vol. 41(3), pages 365-385.
    9. Danielsson, Jon & Morimoto, Yuji, 2000. "Forecasting Extreme Financial Risk: A Critical Analysis of Practical Methods for the Japanese Market," Monetary and Economic Studies, Institute for Monetary and Economic Studies, Bank of Japan, vol. 18(2), pages 25-48, December.
    10. Hans Dewachter & Geert Gielens, 1999. "Setting futures margins: the extremes approach," Applied Financial Economics, Taylor & Francis Journals, vol. 9(2), pages 173-181.
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