Abstract
We analyze the variations in S&P CNX NSE daily closing index stock values through discrete wavelets. Transients and random high frequency components are effectively isolated from the time series. Subsequently, small scale variations as captured by Daubechies level 3 and 4 wavelet coefficients and modelled by genetic programming. We have smoothened the variations using Spline interpolation method, after which it is found that genetic programming captures the dynamical variations quite well through Padē type of map equations. The low-pass coefficients representing the smooth part of the data has also been modelled. We further study the nature of the temporal variations in the returns.
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Ahalpara DP, Parikh JC (2007) submitted for publication in Physica A
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Ahalpara, D.P., Panigrahi, P.K., Parikh, J.C. (2007). Variations in Financial Time Series: Modelling Through Wavelets and Genetic Programming. In: Chatterjee, A., Chakrabarti, B.K. (eds) Econophysics of Markets and Business Networks. New Economic Windows. Springer, Milano. https://doi.org/10.1007/978-88-470-0665-2_3
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DOI: https://doi.org/10.1007/978-88-470-0665-2_3
Publisher Name: Springer, Milano
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