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Option Pricing from Wavelet-Filtered Financial Series

V. T. X. de Almeida and L. Moriconi

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Abstract: We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most ($\simeq$ 99.6%) of the wavelet coefficients can be neglected for the purpose of option premium evaluation. The relevance of the hugely compressed information provided by low-pass wavelet-filtering is related to the fact that the non-gaussian statistical structure of the original financial time series is essentially preserved for expiration times which are larger than just one trading day.

New Economics Papers: this item is included in nep-mst
Date: 2011-03, Revised 2012-12
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