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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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A shapoval 1 2 v gisin 1 v popov 1 3 4

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


A shapoval 1 2 v gisin 1 v popov 1 3 4

Super-exponential trends as the precursors of crashes


A shapoval 1 2 v gisin 1 v popov 1 3 4

Are crises predictable?


A shapoval 1 2 v gisin 1 v popov 1 3 4

Scheme of actions:

1. To detect the indicators of crises.

2. To construct the prediction algorithms involving these indicators.


Super exponential growth

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


Theoretical background

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


Implicit detection of the bubbles

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


Explicit detection of the bubbles

Explicit detection of the bubbles

  • Idea: to formulate a model equation for the the bubbles


A shapoval 1 2 v gisin 1 v popov 1 3 4

Hypothesis. Super-exponential growth (speculative bubbles) preceeds the crashes

Specification.

Log-periodic oscillations


Evolutionary equation with a positive feedback

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


New model

New model

The solution is derived analytically!


Evaluation

Evaluation

  • Regressions

  • Pattern recognition

    Gel'fand, Guberman, Keilis-Borok, Knopoff, Press, Ranzman, Rotwain Sadovsky (1976)


Pattern recognition idea

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


A shapoval 1 2 v gisin 1 v popov 1 3 4

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.


Error diagram molchan 1991

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 + 


Prediction of the daily falls of dji and hs

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


Precursor

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


A shapoval 1 2 v gisin 1 v popov 1 3 4

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


A shapoval 1 2 v gisin 1 v popov 1 3 4

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


Hs dec 86 nov 08

HS: Dec 86 – Nov 08


Hs dec 86 nov 081

HS: Dec 86 – Nov 08


Hs dec 91 dec 97

HS: Dec 91 – Dec 97


Dji oct 28 dec 08 n 0 41 the parameters are fixed

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


Results

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.


Conclusion

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.


A shapoval 1 2 v gisin 1 v popov 1 3 4

Thank you!


Dji oct 86 nov 87

DJI: Oct 86 – Nov 87


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