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Taming the Basel leverage cycle

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  • Aymanns, Christoph
  • Caccioli, Fabio
  • Farmer, J. Doyne
  • Tan, Vincent W.C.

Abstract

Effective risk control must make a tradeoff between the microprudential risk of exogenous shocks to individual institutions and the macroprudential risks caused by their systemic interactions. We investigate a simple dynamical model for understanding this tradeoff, consisting of a bank with a leverage target and an unleveraged fundamental investor subject to exogenous noise with clustered volatility. The parameter space has three regions: (i) a stable region, where the system always reaches a fixed point equilibrium; (ii) a locally unstable region, characterized by cycles and chaotic behavior; and (iii) a globally unstable region. A crude calibration of parameters to data puts the model in region (ii). In this region there is a slowly building price bubble, resembling a “Great Moderation”, followed by a crash, with a period of approximately 10-15 years, which we dub the Basel leverage cycle. We propose a criterion for rating macroprudential policies based on their ability to minimize risk for a given average leverage. We construct a one parameter family of leverage policies that allows us to vary from the procyclical policies of Basel II or III, in which leverage decreases when volatility increases, to countercyclical policies in which leverage increases when volatility increases. We find the best policy depends critically on three parameters: The average leverage used by the bank; the relative size of the bank and the fundamentalist, and the amplitude of the exogenous noise. Basel II is optimal when the exogenous noise is high, the bank is small and leverage is low; in the opposite limit where the bank is large or leverage is high the optimal policy is closer to constant leverage. We also find that systemic risk can be dramatically decreased by lowering the leverage target adjustment speed of the banks.

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  • Aymanns, Christoph & Caccioli, Fabio & Farmer, J. Doyne & Tan, Vincent W.C., 2015. "Taming the Basel leverage cycle," LSE Research Online Documents on Economics 65089, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:65089
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    4. Nava, Noemi & Di Matteo, Tiziana & Aste, Tomaso, 2018. "Financial time series forecasting using empirical mode decomposition and support vector regression," LSE Research Online Documents on Economics 91028, London School of Economics and Political Science, LSE Library.
    5. Llacay, Bàrbara & Peffer, Gilbert, 2017. "Impact of value-at-risk models on market stability," Journal of Economic Dynamics and Control, Elsevier, vol. 82(C), pages 223-256.
    6. Mazzarisi, Piero & Lillo, Fabrizio & Marmi, Stefano, 2019. "When panic makes you blind: A chaotic route to systemic risk," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 176-199.
    7. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
    8. Gabriele Tedeschi & Fabio Caccioli & Maria Cristina Recchioni, 2020. "Taming financial systemic risk: models, instruments and early warning indicators," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 1-7, January.
    9. Ermanno Catullo & Antonio Palestrini & Ruggero Grilli & Mauro Gallegati, 2018. "Early warning indicators and macro-prudential policies: a credit network agent based model," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 13(1), pages 81-115, April.
    10. Noemi Schmitt & Ivonne Schwartz & Frank Westerhoff, 2022. "Heterogeneous speculators and stock market dynamics: a simple agent-based computational model," The European Journal of Finance, Taylor & Francis Journals, vol. 28(13-15), pages 1263-1282, October.
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    12. Piero Mazzarisi & Fabrizio Lillo & Stefano Marmi, 2018. "When panic makes you blind: a chaotic route to systemic risk," Papers 1805.00785, arXiv.org.
    13. Noemi Nava & Tiziana Di Matteo & Tomaso Aste, 2018. "Financial Time Series Forecasting Using Empirical Mode Decomposition and Support Vector Regression," Risks, MDPI, vol. 6(1), pages 1-21, February.
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    More about this item

    Keywords

    Financial stability; capital regulation; systemic risk;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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