Journal of Risk

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Procyclicality control in risk-based margin models

Lauren Wong and Yang Zhang

  • We develop a simple yet efficient technique to provide smooth transition of risk drivers across market conditions in risk-based margin models.
  • We use a dynamic scaling factor to scale up the levels of risk drivers in quiet periods to avoid inadequately low risk coverage and to temper down their elevated levels in stress periods.
  • We show that the technique can effectively mitigate procyclicality in risk-based margin models.

The traditional risk-based margin models are risk sensitive but can be procyclical, especially under stressed market conditions. The issue of procyclicality has returned to the forefront of policy discussions due to the significant increases in margins because of market turmoil related to the Covid-19 pandemic. In this paper, we re-visit the procyclicality issue in risk-based margin models. Most of the existing procyclicality mitigations focus on imposing a buffer or floor on the initial margin to avoid inadequately low margins during quiet periods. However, a more efficient anti-procyclicality mechanism should be able to provide relatively stable and adequate margins across different market conditions in a dynamic way, especially during stress periods. To address this issue, we develop a simple technique that explicitly provides a smooth transition of the key risk drivers in risk-based margin models across different market conditions. Specifically, we use a dynamic scaling factor to control procyclicality. This dynamic scaling factor scales up the key risk drivers during quiet periods to avoid inadequately low risk coverage and tempers down their elevated levels during stress periods. Finally, we show that the technique can provide an efficient control to mitigate procyclicality in risk-based margin models using simple illustrations.

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