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Interpreting Financial Market Crashes as Earthquakes: A New early Warning System for Medium Term Crashes

Author

Listed:
  • Francine Gresnigt
  • Erik Kole
  • Philip Hans Franses

    (Erasmus University Rotterdam, the Netherlands)

Abstract

This discussion paper has led to a publication in the Journal of Banking and Finance , 2015, 56, 123-139. We propose a modeling framework which allows for creating probability predictions on a future market crash in the medium term, like sometime in the next five days. Our framework draws upon noticeable similarities between stock returns around a financial market crash and seismic activity around earthquakes. Our model is incorporated in an Early Warning System for future crash days. Testing our EWS on S&P 500 data during the recent financial crisis, we find positive Hanssen-Kuiper Skill Scores. Furthermore our modeling framework is capable of exploiting information in the returns series not captured by well known and commonly used volatility models. EWS based on our models outperform EWS based on the volatility models forecasting extreme price movements, while forecasting is much less time-consuming.

Suggested Citation

  • Francine Gresnigt & Erik Kole & Philip Hans Franses, 2014. "Interpreting Financial Market Crashes as Earthquakes: A New early Warning System for Medium Term Crashes," Tinbergen Institute Discussion Papers 14-067/III, Tinbergen Institute.
  • Handle: RePEc:tin:wpaper:20140067
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    References listed on IDEAS

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    1. Stephen J. Hardiman & Nicolas Bercot & Jean-Philippe Bouchaud, 2013. "Critical reflexivity in financial markets: a Hawkes process analysis," Papers 1302.1405, arXiv.org, revised Jun 2013.
    2. Asger Lunde & Peter R. Hansen, 2005. "A forecast comparison of volatility models: does anything beat a GARCH(1,1)?," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 20(7), pages 873-889.
    3. 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.
    4. F. Baldovin & F. Camana & M. Caporin & M. Caraglio & A.L. Stella, 2015. "Ensemble properties of high-frequency data and intraday trading rules," Quantitative Finance, Taylor & Francis Journals, vol. 15(2), pages 231-245, February.
    5. Alexander M. Petersen & Fengzhong Wang & Shlomo Havlin & H. Eugene Stanley, 2010. "Market dynamics immediately before and after financial shocks: quantifying the Omori, productivity and Bath laws," Papers 1006.1882, arXiv.org, revised Oct 2010.
    6. D. Sornette, 2003. "Critical Market Crashes," Papers cond-mat/0301543, arXiv.org.
    7. Bauwens, Luc & Giot, Pierre & Grammig, Joachim & Veredas, David, 2004. "A comparison of financial duration models via density forecasts," International Journal of Forecasting, Elsevier, vol. 20(4), pages 589-609.
    8. Diebold, Francis X & Mariano, Roberto S, 2002. "Comparing Predictive Accuracy," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 134-144, January.
    9. Yosihiko Ogata, 1998. "Space-Time Point-Process Models for Earthquake Occurrences," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 50(2), pages 379-402, June.
    10. Bowsher, Clive G., 2007. "Modelling security market events in continuous time: Intensity based, multivariate point process models," Journal of Econometrics, Elsevier, vol. 141(2), pages 876-912, December.
    11. Giacomo Bormetti & Lucio Maria Calcagnile & Michele Treccani & Fulvio Corsi & Stefano Marmi & Fabrizio Lillo, 2013. "Modelling systemic price cojumps with Hawkes factor models," Papers 1301.6141, arXiv.org, revised Mar 2013.
    12. Mohler, G. O. & Short, M. B. & Brantingham, P. J. & Schoenberg, F. P. & Tita, G. E., 2011. "Self-Exciting Point Process Modeling of Crime," Journal of the American Statistical Association, American Statistical Association, vol. 106(493), pages 100-108.
    13. Maria Pacurar, 2008. "Autoregressive Conditional Duration Models In Finance: A Survey Of The Theoretical And Empirical Literature," Journal of Economic Surveys, Wiley Blackwell, vol. 22(4), pages 711-751, September.
    14. Clark, Todd E. & West, Kenneth D., 2007. "Approximately normal tests for equal predictive accuracy in nested models," Journal of Econometrics, Elsevier, vol. 138(1), pages 291-311, May.
    15. Chavez-Demoulin, V. & McGill, J.A., 2012. "High-frequency financial data modeling using Hawkes processes," Journal of Banking & Finance, Elsevier, vol. 36(12), pages 3415-3426.
    16. Earvin Balderama & Frederic Paik Schoenberg & Erin Murray & Philip W. Rundel, 2012. "Application of Branching Models in the Study of Invasive Species," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 467-476, June.
    17. Allen, David & Lazarov, Zdravetz & McAleer, Michael & Peiris, Shelton, 2009. "Comparison of alternative ACD models via density and interval forecasts: Evidence from the Australian stock market," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(8), pages 2535-2555.
    18. Fulvio Baldovin & Francesco Camana & Michele Caraglio & Attilio L. Stella & Marco Zamparo, 2012. "Aftershock prediction for high-frequency financial markets' dynamics," Papers 1203.5893, arXiv.org, revised Jul 2012.
    19. Robert F. Engle & Jeffrey R. Russell, 1998. "Autoregressive Conditional Duration: A New Model for Irregularly Spaced Transaction Data," Econometrica, Econometric Society, vol. 66(5), pages 1127-1162, September.
    20. Andersen, Torben G. & Bollerslev, Tim & Christoffersen, Peter F. & Diebold, Francis X., 2006. "Volatility and Correlation Forecasting," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.),Handbook of Economic Forecasting, edition 1, volume 1, chapter 15, pages 777-878, Elsevier.
    21. Stephen Hardiman & Nicolas Bercot & Jean-Philippe Bouchaud, 2013. "Critical reflexivity in financial markets: a Hawkes process analysis," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(10), pages 1-9, October.
    22. Grothe, Oliver & Korniichuk, Volodymyr & Manner, Hans, 2014. "Modeling multivariate extreme events using self-exciting point processes," Journal of Econometrics, Elsevier, vol. 182(2), pages 269-289.
    23. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    24. Bertrand Candelon & Elena-Ivona Dumitrescu & Christophe Hurlin, 2012. "How to evaluate an Early Warning System ?," Working Papers halshs-00450050, HAL.
    25. Veen, Alejandro & Schoenberg, Frederic P., 2008. "Estimation of SpaceTime Branching Process Models in Seismology Using an EMType Algorithm," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 614-624, June.
    26. Adam Clements & Yin Liao, 2013. "The dynamics of co-jumps, volatility and correlation," NCER Working Paper Series 91, National Centre for Econometric Research.
    27. Hua, Jian & Manzan, Sebastiano, 2013. "Forecasting the return distribution using high-frequency volatility measures," Journal of Banking & Finance, Elsevier, vol. 37(11), pages 4381-4403.
    28. repec:qut:auncer:2013_03 is not listed on IDEAS
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    Citations

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    Cited by:

    1. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Specification Testing in Hawkes Models," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 15(1), pages 139-171.
    2. Molina-Muñoz, Jesús & Mora-Valencia, Andrés & Perote, Javier, 2020. "Market-crash forecasting based on the dynamics of the alpha-stable distribution," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 557(C).
    3. Makridakis, Spyros & Hyndman, Rob J. & Petropoulos, Fotios, 2020. "Forecasting in social settings: The state of the art," International Journal of Forecasting, Elsevier, vol. 36(1), pages 15-28.
    4. Rodrigo Herrera & Adam Clements, 2020. "A marked point process model for intraday financial returns: modeling extreme risk," Empirical Economics, Springer, vol. 58(4), pages 1575-1601, April.
    5. Zhi-Qiang Jiang & Gang-Jin Wang & Askery Canabarro & Boris Podobnik & Chi Xie & H. Eugene Stanley & Wei-Xing Zhou, 2018. "Short term prediction of extreme returns based on the recurrence interval analysis," Quantitative Finance, Taylor & Francis Journals, vol. 18(3), pages 353-370, March.
    6. Wei-Zhen Li & Jin-Rui Zhai & Zhi-Qiang Jiang & Gang-Jin Wang & Wei-Xing Zhou, 2020. "Predicting tail events in a RIA-EVT-Copula framework," Papers 2004.03190, arXiv.org, revised Apr 2020.
    7. Jagielski, Maciej & Kutner, Ryszard & Sornette, Didier, 2017. "Theory of earthquakes interevent times applied to financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 68-73.
    8. Lleo, Sebastien & Ziemba, William T., 2017. "A tale of two indexes: predicting equity market downturns in China," LSE Research Online Documents on Economics 85131, London School of Economics and Political Science, LSE Library.
    9. Francine Gresnigt & Erik Kole & Philip Hans Franses, 2017. "Exploiting Spillovers to Forecast Crashes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 36(8), pages 936-955, December.
    10. Cees Diks & Cars Hommes & Juanxi Wang, 2019. "Critical slowing down as an early warning signal for financial crises?," Empirical Economics, Springer, vol. 57(4), pages 1201-1228, October.
    11. Maciej Jagielski & Ryszard Kutner & Didier Sornette, 2016. "Theory of earthquakes interevent times applied to financial markets," Papers 1610.08921, arXiv.org, revised Oct 2016.

    More about this item

    Keywords

    Financial crashes; Hawkes process; self-exciting process; Early Warning System;

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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