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Systemic Risk: The Dynamics under Central Clearing

Author

Listed:
  • Agostino Capponi

    (Columbia University)

  • W. Allen Cheng

    (Johns Hopkins University)

  • Sriram Rajan

    (Office of Financial Research)

Abstract

We develop a tractable model to resemble asset value processes of financial institutions trading with the central clearinghouse for risk mitigating purposes. Each institution allocates assets between its loan book and the account used to trade with the central clearinghouse. We show that a unique equilibrium allocation profile arises when institutions adjust trading positions to perfectly hedge risk stemming from their loan books. We then analyze the dynamic equilibrium path. As a regulatory monitoring tool, our model shows a buildup of systemic risk, manifested through the increase of market concentration, whose negative size externalities can be internalized via a self-funding systemic risk charge mechanism. We provide new testable predictions, including that (i) the volatility of the traded portfolio of a member can be forecasted by the collective capital committed by all others, (ii) hedging becomes increasingly costly for an institution as its asset value increases, (iii) market shocks have smaller impact on allocation decisions than operational shocks.

Suggested Citation

  • Agostino Capponi & W. Allen Cheng & Sriram Rajan, 2015. "Systemic Risk: The Dynamics under Central Clearing," Working Papers 15-08, Office of Financial Research, US Department of the Treasury.
    • Handle: RePEc:ofr:wpaper:15-08
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    File URL: https://financialresearch.gov/working-papers/files/OFRwp-2015-08_Systemic-Risk-The-Dynamics-under-Central-Clearing.pdf
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    References listed on IDEAS

    as
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    Cited by:

    1. Andrea Aguiar & Dror Y. Kenett & Richard Bookstaber & Thomas Wipf, 2016. "A Map of Collateral Uses and Flows," Working Papers 16-06, Office of Financial Research, US Department of the Treasury.
    2. Hüser, Anne-Caroline & Lepore, Caterina & Veraart, Luitgard, 2021. "How does the repo market behave under stress? Evidence from the Covid-19 crisis," Bank of England working papers 910, Bank of England, revised 18 Jun 2021.
    3. Hamed Amini & Zachary Feinstein, 2020. "Optimal Network Compression," Papers 2008.08733, arXiv.org, revised Jul 2022.
    4. Hamed Amini & Damir Filipović & Andreea Minca, 2016. "To Fully Net or Not to Net: Adverse Effects of Partial Multilateral Netting," Operations Research, INFORMS, vol. 64(5), pages 1135-1142, October.
    5. Hüser, Anne-Caroline & Lepore, Caterina & Veraart, Luitgard Anna Maria, 2024. "How does the repo market behave under stress? Evidence from the COVID-19 crisis," Journal of Financial Stability, Elsevier, vol. 70(C).
    6. Christoph Aymanns & J. Doyne Farmer & Alissa M. Keinniejenhuis & Thom Wetzer, 2017. "Models of Financial Stability and their Application in Stress Tests," Working Papers on Finance 1805, University of St. Gallen, School of Finance.
    7. Amini, Hamed & Feinstein, Zachary, 2023. "Optimal network compression," European Journal of Operational Research, Elsevier, vol. 306(3), pages 1439-1455.
    8. Hüser, Anne-Caroline & Lepore, Caterina & Veraart, Luitgard A. M., 2024. "How does the repo market behave under stress? Evidence from the COVID-19 crisis," LSE Research Online Documents on Economics 121347, London School of Economics and Political Science, LSE Library.
    9. Wenqian Huang & Albert J. Menkveld & Shihao Yu, 2021. "Central Counterparty Exposure in Stressed Markets," Management Science, INFORMS, vol. 67(6), pages 3596-3617, June.

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