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Quantum Majorization in Market Crash Prediction

Author

Listed:
  • J Rhet Montana

    (CWI Amsterdam, 1098 XG Amsterdam, The Netherlands)

  • Luis A. Souto Arias

    (Mathematical Institute, Utrecht University, 3584 CS Utrecht, The Netherlands
    Rabobank, 1321 CB Utrecht, The Netherlands
    Any views, thoughts, and opinions expressed by the author are solely that of the author and do not reflect the views, opinions, policies, or position of Rabobank.)

  • Pasquale Cirillo

    (Institute of Business Information Technology, ZHAW School of Management and Law, 8400 Winterthur, Switzerland)

  • Cornelis W. Oosterlee

    (CWI Amsterdam, 1098 XG Amsterdam, The Netherlands
    Mathematical Institute, Utrecht University, 3584 CS Utrecht, The Netherlands)

Abstract

We introduce the Quantum Alarm System, a novel framework that combines the informational advantages of quantum majorization applied to tail pseudo-correlation matrices with the learning capabilities of a reinforced urn process, to predict financial turmoil and market crashes. This integration allows for a more nuanced analysis of the dependence structure in financial markets, particularly focusing on extreme events reflected in the tails of the distribution. Our model is tested using the daily log-returns of the 30 constituents of the Dow Jones Industrial Average, spanning from 2 January 1992 to 30 August 2024. The results are encouraging: in the validation set, the 12-month ahead probability of correct alarm is between 73 % and 80 % , while maintaining a low false alarm rate. Thanks to the application of quantum majorization, the alarm system effectively captures non-traditional and emerging risk sources, such as the financial impact of the COVID-19 pandemic—an area where traditional models often fall short.

Suggested Citation

  • J Rhet Montana & Luis A. Souto Arias & Pasquale Cirillo & Cornelis W. Oosterlee, 2024. "Quantum Majorization in Market Crash Prediction," Risks, MDPI, vol. 12(12), pages 1-18, December.
    • Handle: RePEc:gam:jrisks:v:12:y:2024:i:12:p:204-:d:1545469
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    References listed on IDEAS

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    1. V. Chavez-Demoulin & A. C. Davison & A. J. McNeil, 2005. "Estimating value-at-risk: a point process approach," Quantitative Finance, Taylor & Francis Journals, vol. 5(2), pages 227-234.
    2. Nikolay Gospodinov, 2005. "Testing For Threshold Nonlinearity in Short-Term Interest Rates," Journal of Financial Econometrics, Oxford University Press, vol. 3(3), pages 344-371.
    3. Keys, Benjamin J. & Mukherjee, Tanmoy & Seru, Amit & Vig, Vikrant, 2009. "Financial regulation and securitization: Evidence from subprime loans," Journal of Monetary Economics, Elsevier, vol. 56(5), pages 700-720, July.
    4. Alessi, Lucia & Detken, Carsten, 2011. "Quasi real time early warning indicators for costly asset price boom/bust cycles: A role for global liquidity," European Journal of Political Economy, Elsevier, vol. 27(3), pages 520-533, September.
    5. Markus Haas, 2004. "A New Approach to Markov-Switching GARCH Models," Journal of Financial Econometrics, Oxford University Press, vol. 2(4), pages 493-530.
    6. Andreas Park & Hamid Sabourian, 2011. "Herding and Contrarian Behavior in Financial Markets," Econometrica, Econometric Society, vol. 79(4), pages 973-1026, July.
    7. Alan G. Hawkes, 2022. "Hawkes jump-diffusions and finance: a brief history and review," The European Journal of Finance, Taylor & Francis Journals, vol. 28(7), pages 627-641, May.
    8. Markus Holopainen & Peter Sarlin, 2017. "Toward robust early-warning models: a horse race, ensembles and model uncertainty," Quantitative Finance, Taylor & Francis Journals, vol. 17(12), pages 1933-1963, December.
    9. Spyros Makridakis & Evangelos Spiliotis & Vassilios Assimakopoulos, 2018. "Statistical and Machine Learning forecasting methods: Concerns and ways forward," PLOS ONE, Public Library of Science, vol. 13(3), pages 1-26, March.
    10. Cornelis W Oosterlee & Lech A Grzelak, 2019. "Mathematical Modeling and Computation in Finance:With Exercises and Python and MATLAB Computer Codes," World Scientific Books, World Scientific Publishing Co. Pte. Ltd., number q0236, February.
    11. Berg, Andrew & Pattillo, Catherine, 1999. "Predicting currency crises:: The indicators approach and an alternative," Journal of International Money and Finance, Elsevier, vol. 18(4), pages 561-586, August.
    12. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
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