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An empirical approach to financial crisis indicators based on random matrices

Author

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  • Raphaël Douady

    (CES - Centre d'économie de la Sorbonne - UP1 - Université Paris 1 Panthéon-Sorbonne - CNRS - Centre National de la Recherche Scientifique, SBU - Stony Brook University [SUNY] - SUNY - State University of New York)

  • Antoine Kornprobst

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

Abstract

The aim of this work is to build a class of financial crisis indicators based on the spectral properties of the dynamics of market data. After choosing an appropriate size for a rolling window, the historical market data inside this rolling window are seen every trading day as a random matrix from which a correlation matrix is obtained. Our goal is to study the correlations between the assets that constitute this market and look for reproducible patterns that are indicative of an impending financial crisis. A weighting of the assets in the market is then introduced and is proportional to the daily traded volumes. This manipulation is realized in order to give more importance to the most liquid assets. Our financial crisis indicators are based on the spectral radius of this weighted correlation matrix. The idea behind this type of financial crisis indicators is that large eigenvalues are a sign of dynamic instability. The out-of-sample predictive power of the financial crisis indicators in this framework is then demonstrated, in particular by using them as decision-making tools in a protective put strategy.

Suggested Citation

  • Raphaël Douady & Antoine Kornprobst, 2018. "An empirical approach to financial crisis indicators based on random matrices," Post-Print hal-03265045, HAL.
    • Handle: RePEc:hal:journl:hal-03265045
    DOI: 10.1142/S021902491850022X
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    References listed on IDEAS

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    1. J. P. Bouchaud & M. Potters, 2009. "Financial Applications of Random Matrix Theory: a short review," Papers 0910.1205, arXiv.org.
    2. M. Potters & J. P. Bouchaud & L. Laloux, 2005. "Financial Applications of Random Matrix Theory: Old Laces and New Pieces," Papers physics/0507111, arXiv.org.
    3. Stanley, H.E. & Gopikrishnan, P. & Plerou, V. & Amaral, L.A.N., 2000. "Quantifying fluctuations in economic systems by adapting methods of statistical physics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 339-361.
    4. Maltritz, Dominik & Eichler, Stefan, 2010. "Currency crisis prediction using ADR market data: An options-based approach," International Journal of Forecasting, Elsevier, vol. 26(4), pages 858-884, October.
    5. Drehmann, Mathias & Juselius, Mikael, 2014. "Evaluating early warning indicators of banking crises: Satisfying policy requirements," International Journal of Forecasting, Elsevier, vol. 30(3), pages 759-780.
    6. Jiang, Zhi-Qiang & Zhou, Wei-Xing & Sornette, Didier & Woodard, Ryan & Bastiaensen, Ken & Cauwels, Peter, 2010. "Bubble diagnosis and prediction of the 2005-2007 and 2008-2009 Chinese stock market bubbles," Journal of Economic Behavior & Organization, Elsevier, vol. 74(3), pages 149-162, June.
    7. Anders Johansen & Didier Sornette, 2010. "Shocks, Crashes and Bubbles in Financial Markets," Brussels Economic Review, ULB -- Universite Libre de Bruxelles, vol. 53(2), pages 201-253.
    8. Singleton, J. Clay & Wingender, John, 1986. "Skewness Persistence in Common Stock Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 21(3), pages 335-341, September.
    9. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    10. Sandoval, Leonidas & Franca, Italo De Paula, 2012. "Correlation of financial markets in times of crisis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(1), pages 187-208.
    11. Niemira, Michael P. & Saaty, Thomas L., 2004. "An Analytic Network Process model for financial-crisis forecasting," International Journal of Forecasting, Elsevier, vol. 20(4), pages 573-587.
    12. Fuertes, Ana-Maria & Kalotychou, Elena, 2007. "Optimal design of early warning systems for sovereign debt crises," International Journal of Forecasting, Elsevier, vol. 23(1), pages 85-100.
    13. van den Berg, Jeroen & Candelon, Bertrand & Urbain, Jean-Pierre, 2008. "A cautious note on the use of panel models to predict financial crises," Economics Letters, Elsevier, vol. 101(1), pages 80-83, October.
    14. Didier SORNETTE, 2009. "Dragon-Kings, Black Swans and the Prediction of Crises," Swiss Finance Institute Research Paper Series 09-36, Swiss Finance Institute.
    15. D. Sornette, "undated". "Dragon-Kings, Black Swans and the Prediction of Crises," Working Papers CCSS-09-005, ETH Zurich, Chair of Systems Design.
    16. Clemen, Robert T., 1989. "Combining forecasts: A review and annotated bibliography," International Journal of Forecasting, Elsevier, vol. 5(4), pages 559-583.
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    Cited by:

    1. Maria Elvira Mancino & Simona Sanfelici, 2020. "Identifying financial instability conditions using high frequency data," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 15(1), pages 221-242, January.
    2. Allaj, Erindi & Sanfelici, Simona, 2023. "Early Warning Systems for identifying financial instability," International Journal of Forecasting, Elsevier, vol. 39(4), pages 1777-1803.
    3. Raphaël Douady, 2019. "Managing the Downside of Active and Passive Strategies: Convexity and Fragilities," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) hal-02488589, HAL.
    4. Lin, Li & Guo, Xin-Yu, 2019. "Identifying fragility for the stock market: Perspective from the portfolio overlaps network," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 62(C), pages 132-151.

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