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A fractal forecasting model for financial time series

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  • Gordon R. Richards

    (Sprint, Kansas, USA)

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    Abstract

    Financial market time series exhibit high degrees of non-linear variability, and frequently have fractal properties. When the fractal dimension of a time series is non-integer, this is associated with two features: (1) inhomogeneity-extreme fluctuations at irregular intervals, and (2) scaling symmetries-proportionality relationships between fluctuations over different separation distances. In multivariate systems such as financial markets, fractality is stochastic rather than deterministic, and generally originates as a result of multiplicative interactions. Volatility diffusion models with multiple stochastic factors can generate fractal structures. In some cases, such as exchange rates, the underlying structural equation also gives rise to fractality. Fractal principles can be used to develop forecasting algorithms. The forecasting method that yields the best results here is the state transition-fitted residual scale ratio (ST-FRSR) model. A state transition model is used to predict the conditional probability of extreme events. Ratios of rates of change at proximate separation distances are used to parameterize the scaling symmetries. Forecasting experiments are run using intraday exchange rate futures contracts measured at 15-minute intervals. The overall forecast error is reduced on average by up to 7% and in one instance by nearly a quarter. However, the forecast error during the outlying events is reduced by 39% to 57%. The ST-FRSR reduces the predictive error primarily by capturing extreme fluctuations more accurately. Copyright © 2004 John Wiley & Sons, Ltd.

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    File URL: http://hdl.handle.net/10.1002/for.927
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    Bibliographic Info

    Article provided by John Wiley & Sons, Ltd. in its journal Journal of Forecasting.

    Volume (Year): 23 (2004)
    Issue (Month): 8 ()
    Pages: 586-601

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    Handle: RePEc:jof:jforec:v:23:y:2004:i:8:p:586-601

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    Web page: http://www3.interscience.wiley.com/cgi-bin/jhome/2966

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    Cited by:
    1. repec:ebl:ecbull:v:7:y:2007:i:1:p:1-11 is not listed on IDEAS
    2. Subbotin, Alexandre, 2009. "Volatility Models: from Conditional Heteroscedasticity to Cascades at Multiple Horizons," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 15(3), pages 94-138.
    3. John W. Galbraith & Greg Tkacz, 2007. "How Far Can Forecasting Models Forecast? Forecast Content Horizons for Some Important Macroeconomic Variables," Working Papers 07-1, Bank of Canada.
    4. Sergio Da Silva & Annibal Figueiredo & Iram Gleria & Raul Matsushita, 2007. "Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-11.

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