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Time is money


  • Ausloos, Marcel
  • Vandewalle, N.
  • Ivanova, K.


Abstract. Specialized topics on financial data analysis from a numerical and physical point of view are discussed when pertaining to the analysis of coherent and random sequences in financial fluctuations within (i) the extended detrended fluctuation analysis method, (ii) multi-affine analysis technique, (iii) mobile average intersection rules and distributions, (iv) sandpile avalanches models for crash prediction, (v) the (m, k)-Zipf method and (vi) the i-variability diagram technique for sorting out short range correlations. The most baffling result that needs further thought from mathematicians and physicists is recalled: the crossing of two mobile averages is an original method for measuring the ”signal” roughness exponent, but why it is so is not understood up to now.

Suggested Citation

  • Ausloos, Marcel & Vandewalle, N. & Ivanova, K., 2000. "Time is money," MPRA Paper 28703, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:28703

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    References listed on IDEAS

    1. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
    2. Bollerslev, Tim & Ole Mikkelsen, Hans, 1996. "Modeling and pricing long memory in stock market volatility," Journal of Econometrics, Elsevier, vol. 73(1), pages 151-184, July.
    3. Giraitis, Liudas & Kokoszka, Piotr & Leipus, Remigijus, 2000. "Stationary Arch Models: Dependence Structure And Central Limit Theorem," Econometric Theory, Cambridge University Press, vol. 16(01), pages 3-22, February.
    4. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    5. repec:adr:anecst:y:1999:i:54:p:03 is not listed on IDEAS
    6. Thomas Mikosch & Catalin Starica, 2004. "Long range dependence effects and ARCH modelling," Econometrics 0412004, EconWPA.
    7. Baillie, Richard T. & Bollerslev, Tim & Mikkelsen, Hans Ole, 1996. "Fractionally integrated generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 74(1), pages 3-30, September.
    8. Breidt, F. Jay & Crato, Nuno & de Lima, Pedro, 1998. "The detection and estimation of long memory in stochastic volatility," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 325-348.
    9. Bougerol, Philippe & Picard, Nico, 1992. "Stationarity of Garch processes and of some nonnegative time series," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 115-127.
    10. Ding, Zhuanxin & Granger, Clive W. J., 1996. "Modeling volatility persistence of speculative returns: A new approach," Journal of Econometrics, Elsevier, vol. 73(1), pages 185-215, July.
    11. Davidson, James, 2004. "Moment and Memory Properties of Linear Conditional Heteroscedasticity Models, and a New Model," Journal of Business & Economic Statistics, American Statistical Association, vol. 22(1), pages 16-29, January.
    12. Nelson, Daniel B., 1990. "Stationarity and Persistence in the GARCH(1,1) Model," Econometric Theory, Cambridge University Press, vol. 6(03), pages 318-334, September.
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    Cited by:

    1. M. Ausloos & K. Ivanova & N. Vandewalle, 2001. "Crashes : symptoms, diagnoses and remedies," Papers cond-mat/0104127,, revised Apr 2001.
    2. Ausloos, Marcel & Pe¸kalski, Andrzej, 2007. "Model of wealth and goods dynamics in a closed market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 373(C), pages 560-568.

    More about this item


    sand-pile; avalanches; Zipf; variability diagram; signal; roughness exponent; detrended fluctuation analysis; moving average; multi-affine analysis;

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics


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