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Econophysics of Stock and other MarketsModelling Financial Time Series

Econophysics of Stock and other Markets: Modelling Financial Time Series [Financial time series, in general, exhibit average behaviour at “long” time scales and stochastic behaviour at ‘short” time scales. As in statistical physics, the two have to be modelled using different approaches — deterministic for trends and probabilistic for fluctuations about the trend. In this talk, we will describe a new wavelet based approach to separate the trend from the fluctuations in a time series. A deterministic (non-linear regression) model is then constructed for the trend using genetic algorithm. We thereby obtain an explicit analytic model to describe dynamics of the trend. Further the model is used to make predictions of the trend. We also study statistical and scaling properties of the fluctuations. The fluctuations have non-Gaussian probability distribution function and show multi-scaling behaviour. Thus, our work results in a comprehensive model of trends and fluctuations of a financial time series.] http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png

Econophysics of Stock and other MarketsModelling Financial Time Series

Part of the New Economic Windows Book Series
Editors: Chatterjee, Arnab; Chakrabarti, Bikas K

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References (6)

Publisher
Springer Milan
Copyright
© Springer-Verlag Italia 2006
ISBN
978-88-470-0501-3
Pages
183 –191
DOI
10.1007/978-88-470-0502-0_19
Publisher site
See Chapter on Publisher Site

Abstract

[Financial time series, in general, exhibit average behaviour at “long” time scales and stochastic behaviour at ‘short” time scales. As in statistical physics, the two have to be modelled using different approaches — deterministic for trends and probabilistic for fluctuations about the trend. In this talk, we will describe a new wavelet based approach to separate the trend from the fluctuations in a time series. A deterministic (non-linear regression) model is then constructed for the trend using genetic algorithm. We thereby obtain an explicit analytic model to describe dynamics of the trend. Further the model is used to make predictions of the trend. We also study statistical and scaling properties of the fluctuations. The fluctuations have non-Gaussian probability distribution function and show multi-scaling behaviour. Thus, our work results in a comprehensive model of trends and fluctuations of a financial time series.]

Published: Jan 1, 2006

Keywords: Discrete Wavelet Transform; Probability Distribution Function; Financial Time Series; Smooth Trend; Trend Series

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