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Financial Crisis Dynamics: Attempt to Define a Market Instability Indicator

Author

Listed:
  • Youngna Choi

    (Department of Mathematical Sciences - MSU - Montclair State University [USA])

  • Raphaël Douady

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique)

Abstract

The impact of increasing leverage in the economy produces hyperreaction of market participants to variations of their revenues. If the income of banks decreases, they mass-reduce their lendings; if corporations sales drop, and they cannot adjust their liquidities by further borrowing due to existing debt, then they must immediately reduce their expenses, lay off staff, and cancel investments. This hyperreaction produces a bifurcation mechanism, and eventually a strong dynamical instability in capital markets that is commonly called systemic risk. In this article, we show that this instability can be monitored by measuring the highest eigenvalue of a matrix of elasticities. These elasticities measure the reaction of each sector of the economy to a drop in its revenues from another sector. This highest eigenvalue--the spectral radius--of the elasticity matrix can be used as an early indicator of market instability and potential crisis. Grandmont and subsequent research showed the possibility that the 'invisible hand' of markets becomes chaotic, opening the door to uncontrolled swings. Our contribution is to provide an actual way of measuring how close to chaos the market is. Estimating elasticities and actually generating the indicators of instability will be the topic of forthcoming research.

Suggested Citation

  • Youngna Choi & Raphaël Douady, 2012. "Financial Crisis Dynamics: Attempt to Define a Market Instability Indicator," Post-Print hal-00666245, HAL.
  • Handle: RePEc:hal:journl:hal-00666245
    DOI: 10.1080/14697688.2011.627880
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    References listed on IDEAS

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    1. Anand, Kartik & Gai, Prasanna & Marsili, Matteo, 2012. "Rollover risk, network structure and systemic financial crises," Journal of Economic Dynamics and Control, Elsevier, vol. 36(8), pages 1088-1100.
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    Cited by:

    1. Olivier de Weck & Daniel Krob & Li Lefei & Pao Chuen Lui & Antoine Rauzy & Xinguo Zhang, 2020. "Handling the COVID‐19 crisis: Toward an agile model‐based systems approach," Systems Engineering, John Wiley & Sons, vol. 23(5), pages 656-670, September.
    2. Natasa Golo & Guy Kelman & David S. Bree & Leanne Usher & Marco Lamieri & Sorin Solomon, 2015. "Many-to-one contagion of economic growth rate across trade credit network of firms," Papers 1506.01734, arXiv.org.
    3. Piero Mazzarisi & Fabrizio Lillo & Stefano Marmi, 2018. "When panic makes you blind: a chaotic route to systemic risk," Papers 1805.00785, arXiv.org.
    4. Castellacci, Giuseppe & Choi, Youngna, 2015. "Modeling contagion in the Eurozone crisis via dynamical systems," Journal of Banking & Finance, Elsevier, vol. 50(C), pages 400-410.
    5. Ekaterina Panttser & Weidong Tian, 2013. "A Welfare Analysis of Capital Insurance," Risks, MDPI, vol. 1(2), pages 1-24, September.
    6. Youngna Choi, 2019. "Borrowing Capacity, Financial Instability, And Contagion," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 22(01), pages 1-25, February.
    7. Youngna Choi, 2018. "Masked Instability: Within-Sector Financial Risk in the Presence of Wealth Inequality," Risks, MDPI, vol. 6(3), pages 1-15, June.
    8. 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.
    9. Marian Gidea & Yuri Katz, 2017. "Topological Data Analysis of Financial Time Series: Landscapes of Crashes," Papers 1703.04385, arXiv.org, revised Apr 2017.
    10. Mainik Georg & Schaanning Eric, 2014. "On dependence consistency of CoVaRand some other systemic risk measures," Statistics & Risk Modeling, De Gruyter, vol. 31(1), pages 1-29, March.
    11. Tang, Qihe & Tong, Zhiwei & Yang, Yang, 2021. "Large portfolio losses in a turbulent market," European Journal of Operational Research, Elsevier, vol. 292(2), pages 755-769.
    12. Fabrizio Lillo & Giulia Livieri & Stefano Marmi & Anton Solomko & Sandro Vaienti, 2021. "Analysis of bank leverage via dynamical systems and deep neural networks," Papers 2104.04960, arXiv.org.

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