
Proceedings Paper
Scaling properties of long-range correlated noisy signals: application to financial marketsFormat | Member Price | Non-Member Price |
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Paper Abstract
Long-range correlation properties of financial stochastic time
series y have been investigated with the main aim to
demonstrate the ability of a recently proposed method to extract
the scaling parameters of a stochastic series. According to this
technique, the Hurst coefficient H is calculated by means of
the following function: EQUATION where yn(i)is the moving average of y(i), defined as EQUATION the moving average window and Nmax is the dimension of the stochastic series.
The method is called Detrending Moving Average Analysis (DMA) on account of the several analogies with the well-known Detrended Fluctuation Analysis (DFA). The DMA technique has been
widely tested on stochastic series with assigned H generated by
suitable algorithms. It has been demonstrated that the ability of
the proposed technique relies on very general grounds: the
function EQUATION generates indeed a sequence of clusters with power-law distribution of amplitudes and lifetimes. In particular the exponent of the distribution of cluster lifetime varies as the fractal dimension 2 - H of the series, as expected on the basis of the box-counting method. In the present paper we will report on the scaling coefficients of real data series (the BOBL and DAX German future) calculated by the DMA technique.
Paper Details
Date Published: 7 May 2003
PDF: 9 pages
Proc. SPIE 5114, Noise in Complex Systems and Stochastic Dynamics, (7 May 2003); doi: 10.1117/12.497039
Published in SPIE Proceedings Vol. 5114:
Noise in Complex Systems and Stochastic Dynamics
Lutz Schimansky-Geier; Derek Abbott; Alexander Neiman; Christian Van den Broeck, Editor(s)
PDF: 9 pages
Proc. SPIE 5114, Noise in Complex Systems and Stochastic Dynamics, (7 May 2003); doi: 10.1117/12.497039
Show Author Affiliations
Giuliano Castelli, INFM (Italy)
Politecnico di Torino (Italy)
Politecnico di Torino (Italy)
Published in SPIE Proceedings Vol. 5114:
Noise in Complex Systems and Stochastic Dynamics
Lutz Schimansky-Geier; Derek Abbott; Alexander Neiman; Christian Van den Broeck, Editor(s)
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