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Borderline: judging the adequacy of return distribution estimation techniques in initial margin models

Author

Listed:
  • Houllier, Melanie

    (The London Institute for Banking and Finance)

  • Murphy, David

    (Bank of England)

Abstract

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. This paper presents a new approach to parameter selection based on the statistical properties of the worst loss over a margin period of risk estimated by the margin model under test. This measure is related to risk estimated at a fixed confidence interval yet leads to a more powerful test which is better able to justify the choice of parameters used in margin models. The test proposed is used on a variety of volatility estimation techniques applied to a long history of returns of the S&P 500 index. Well known techniques, including exponentially weighted moving average volatility estimation and generalised autoregressive conditional heteroskedasticity approaches are considered, and novel approaches derived from signal processing are also analysed. In each case a range of model parameters which give rise to acceptable risk estimates is identified.

Suggested Citation

  • Houllier, Melanie & Murphy, David, 2017. "Borderline: judging the adequacy of return distribution estimation techniques in initial margin models," Bank of England working papers 673, Bank of England.
  • Handle: RePEc:boe:boeewp:0673
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    References listed on IDEAS

    as
    1. Farid Aitsahlia & Tzeung Le Lai, 1998. "Random walk duality and the valuation of discrete lookback options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 5(3-4), pages 227-240.
    2. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Conditional volatility; filtered volatility; GARCH(1; 1); initial margin model; model backtesting; volatility estimation;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing

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