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Taming the Basel leverage cycle

Author

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

Abstract

We investigate a simple dynamical model for the systemic risk caused by the use of Value-at-Risk, as mandated by Basel II. The model consists of a bank with a leverage target and an unleveraged fundamentalist investor subject to exogenous noise with clustered volatility. The parameter space has three regions: (i) a stable region, where the system has a fixed point equilibrium; (ii) a locally unstable region, characterized by cycles with chaotic behavior; and (iii) a globally unstable region. A calibration of parameters to data puts the model in region (ii). In this region there is a slowly building price bubble, resembling the period prior to the Global Financial Crisis, followed by a crash resembling the crisis, with a period of approximately 10-15 years. We dub this the Basel leverage cycle. To search for an optimal leverage control policy we propose a criterion based on the ability to minimize risk for a given average leverage. Our model 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 on the market impact of the bank. 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 and leverage is high the optimal policy is closer to constant leverage. In the latter regime systemic risk can be dramatically decreased by lowering the leverage target adjustment speed of the banks. While our model does not show that the financial crisis and the period leading up to it were due to VaR risk management policies, it does suggest that it could have been caused by VaR risk management, and that the housing bubble may have just been the spark that triggered the crisis.

Suggested Citation

  • Aymanns, Christoph & Caccioli, Fabio & Farmer, J. Doyne & Tan, Vincent W.C., 2016. "Taming the Basel leverage cycle," LSE Research Online Documents on Economics 65676, London School of Economics and Political Science, LSE Library.
  • Handle: RePEc:ehl:lserod:65676
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    Cited by:

    1. Marcus Miller & Lei Zhang & Songklod Rastapana, 2017. "Subprime assets and financial crisis: theory, policy and the law," CAGE Online Working Paper Series 340, Competitive Advantage in the Global Economy (CAGE).
    2. Jondeau, Eric & Khalilzadeh, Amir, 2017. "Collateralization, leverage, and stressed expected loss," Journal of Financial Stability, Elsevier, vol. 33(C), pages 226-243.
    3. Gaffeo, Edoardo, 2019. "Leverage and evolving heterogeneous beliefs in a simple agent-based financial market," Finance Research Letters, Elsevier, vol. 29(C), pages 272-279.
    4. Wiersema, Garbrand & Kleinnijenhuis, Alissa M. & Wetzer, Thom & Farmer, J. Doyne, 2023. "Scenario-free analysis of financial stability with interacting contagion channels," Journal of Banking & Finance, Elsevier, vol. 146(C).
    5. Piero Mazzarisi & Fabrizio Lillo & Stefano Marmi, 2018. "When panic makes you blind: a chaotic route to systemic risk," Papers 1805.00785, arXiv.org.
    6. 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.
    7. 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.
    8. Mariya Gubareva, 2019. "Weight of the Default Component of CDS Spreads: Avoiding Procyclicality in Credit Loss Provisioning Framework," Complexity, Hindawi, vol. 2019, pages 1-19, July.
    9. 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.
    10. Nava, Noemi & Di Matteo, T. & Aste, Tomaso, 2018. "Dynamic correlations at different time-scales with empirical mode decomposition," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 502(C), pages 534-544.
    11. 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.
    12. Ellis, Scott & Sharma, Satish & Brzeszczyński, Janusz, 2022. "Systemic risk measures and regulatory challenges," Journal of Financial Stability, Elsevier, vol. 61(C).
    13. Douglas da Rosa München & Herbert Kimura, 2020. "Regulatory Banking Leverage: what do you know?," Working Papers Series 540, Central Bank of Brazil, Research Department.
    14. Wang, Xiaoting & Hou, Siyuan & Shen, Jie, 2021. "Default clustering of the nonfinancial sector and systemic risk: Evidence from China," Economic Modelling, Elsevier, vol. 96(C), pages 196-208.
    15. 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.
    16. 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.
    17. Wiersema, Garbrand & Kemp, Esti & Farmer, J. Doyne, 2026. "Liquidity spirals," Working Paper Series 3169, European Central Bank.
    18. Deborah Noguera & Gabriel Montes-Rojas, 2022. "Credit-constrained fluctuations and uncertainty in a network economy," Ensayos Económicos, Central Bank of Argentina, Economic Research Department, vol. 1(80), pages 5-52, November.
    19. 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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    JEL classification:

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

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