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Prediction problem for target events based on the inter-event waiting time

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  • Shapoval, A.

Abstract

In this paper we address the problem of forecasting the target events of a time series given the distribution ξ of time gaps between target events. Strong earthquakes and stock market crashes are the two types of such events that we are focusing on. In the series of earthquakes, as McCann et al. show [W.R. Mc Cann, S.P. Nishenko, L.R. Sykes, J. Krause, Seismic gaps and plate tectonics: seismic potential for major boundaries, Pure and Applied Geophysics 117 (1979) 1082–1147], there are well-defined gaps (called seismic gaps) between strong earthquakes. On the other hand, usually there are no regular gaps in the series of stock market crashes [M. Raberto, E. Scalas, F. Mainardi, Waiting-times and returns in high-frequency financial data: an empirical study, Physica A 314 (2002) 749–755]. For the case of seismic gaps, we analytically derive an upper bound of prediction efficiency given the coefficient of variation of the distribution ξ. For the case of stock market crashes, we develop an algorithm that predicts the next crash within a certain time interval after the previous one. We show that this algorithm outperforms random prediction. The efficiency of our algorithm sets up a lower bound of efficiency for effective prediction of stock market crashes.

Suggested Citation

  • Shapoval, A., 2010. "Prediction problem for target events based on the inter-event waiting time," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(22), pages 5145-5154.
    • Handle: RePEc:eee:phsmap:v:389:y:2010:i:22:p:5145-5154
    DOI: 10.1016/j.physa.2010.07.033
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    References listed on IDEAS

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    1. Granger, Clive W.J. & Machina, Mark J., 2006. "Forecasting and Decision Theory," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 1, chapter 2, pages 81-98, Elsevier.
    2. Selçuk, Faruk, 2004. "Financial earthquakes, aftershocks and scaling in emerging stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 333(C), pages 306-316.
    3. Sornette, Didier & Zhou, Wei-Xing, 2006. "Predictability of large future changes in major financial indices," International Journal of Forecasting, Elsevier, vol. 22(1), pages 153-168.
    4. Kaizoji, Taisei & Kaizoji, Michiyo, 2004. "Power law for the calm-time interval of price changes," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 563-570.
    5. David E. Rapach & Mark E. Wohar, 2006. "Structural Breaks and Predictive Regression Models of Aggregate U.S. Stock Returns," Journal of Financial Econometrics, Oxford University Press, vol. 4(2), pages 238-274.
    6. Raberto, Marco & Scalas, Enrico & Mainardi, Francesco, 2002. "Waiting-times and returns in high-frequency financial data: an empirical study," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 314(1), pages 749-755.
    7. Mantegna,Rosario N. & Stanley,H. Eugene, 2007. "Introduction to Econophysics," Cambridge Books, Cambridge University Press, number 9780521039871.
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