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A. Shapoval 1,2 , V. Gisin 1 , V . Popov 1,3,4

A. Shapoval 1,2 , V. Gisin 1 , V . Popov 1,3,4. 1. Finance academy under the government of the RF. 2. International institute of earthquake prediction thoory. 3. Moscow State University. 4. Space research institute. Super-exponential trends as the precursors of crashes.

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A. Shapoval 1,2 , V. Gisin 1 , V . Popov 1,3,4

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  1. A. Shapoval1,2, V. Gisin1, V. Popov1,3,4 1. Finance academy under the government of the RF 2. International institute of earthquake prediction thoory 3. Moscow State University 4. Space research institute

  2. Super-exponential trends as the precursors of crashes

  3. Are crises predictable?

  4. Scheme of actions: 1. To detect the indicators of crises. 2. To construct the prediction algorithms involving these indicators.

  5. Super-exponential growth • Tulips mania in Holland • Demographical growth up to the middle of the previous centrury. • Boom in the 1920th on the American stock market

  6. Theoretical background • The absence of the bubbles under the restrictive assumtions about rationality of the agents (Tirole, 1982). • The bubbles exist under weaker assumptions: • De Long B. et al., 1990.Irrational agents • Weil, 1987, The bubbles because of the beliefs in them • Allen & Gorton, 1993. Groups with different information → the bubbles

  7. Implicit detection of the bubbles • West, 1987. Two ways to calculate some characteristics of the data. They have to coincide if the bubbles are absent. The West procedure tests the standard present value model against an unspecified alternative which is interpreted as having arisen from a speculative bubble. • Wu, 1997, estimates the bubbles using the Kalman filter

  8. Explicit detection of the bubbles • Idea: to formulate a model equation for the the bubbles

  9. Hypothesis. Super-exponential growth (speculative bubbles) preceeds the crashes Specification. Log-periodic oscillations

  10. Evolutionary equation with a positive feedback (Sornette, 02) m>1, w(t) – the Wiener process, dj = 0 or 1 Due to he special arrangements of the terms there exists the filter mapping the data into the normal sample! It gives a criterion of the model adequacy

  11. New model The solution is derived analytically!

  12. Evaluation • Regressions • Pattern recognition Gel'fand, Guberman, Keilis-Borok, Knopoff, Press, Ranzman, Rotwain Sadovsky (1976)

  13. Pattern recognition. IDEA • To find a pattern that preceeds the events-to-predict but rarely occurs during «ordinary intervals» • To construct a prediction algorithm involving this pattern

  14. Prediction efficiency Prediction algorithm of any nature divides the time axisinto the intervals of two sorts:(1) the alarm is announced (the event-to-predict is expected);(2) the alarm is not announced.

  15. Error diagram(Molchan, 1991) • n and  are the rate of the failure-to-predict and the alarm rate • The complement startegy declares the alarm if A does not declare • A is better thanB, A andC cannot be compared until the goal function is introduced • The goal function:  = n + 

  16. Prediction of the daily falls of DJI andHS • The alarm of a fixed duration T is declared immediately after the crash • The red markers are the real prediction • The black markers correspond to changes of T

  17. Precursor t the collection of the sliding windows [t, t-wi), iI di– the deviation of the solution from the data on [t, t-wi), A(t) = #(di(t) < d*) A(t) > A* bubbles

  18. bA,N (t) – the trend of А on [t, t-N) bX,N (t) – the trend ofX on [t, t-N) Either bA,N (t)<0, or bX,N (t)<0 the bubbles the alarm

  19. the bubbles A(t) > A* A «calm period» bA,N (t)<0 or bX,N (t)<0 Crash occurred or alarm was declared T days ago the alarm

  20. HS: Dec 86 – Nov 08

  21. HS: Dec 86 – Nov 08

  22. HS: Dec 91 – Dec 97

  23. DJI: Oct 28 – Dec 08n + =0.41, the parameters are fixed

  24. Results • The losses  [0.4, 0.5] are stable with respect to the parameters of the algorithm. • The bubbles are usually identified directly before the end of the growth. • Just a part of ascendent trends identified as the bubbles end with a crash.

  25. Conclusion • The prediction efficiency is well estimated by the error diagram. • The algorithm which predicts crashes following the booms is evaluated • The size of the fall following the boom has a significant random component.

  26. Thank you!

  27. DJI: Oct 86 – Nov 87

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