De-noising option prices with the wavelet method
AbstractFinancial time series are known to carry noise. Hence, techniques to de-noise such data deserve great attention. Wavelet analysis is widely used in science and engineering to de-noise data. In this paper we show, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. We also find that the estimation of risk-neutral density functions and out-of-sample price forecasting is significantly improved after noise is removed using the wavelet method.
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Bibliographic InfoArticle provided by Elsevier in its journal European Journal of Operational Research.
Volume (Year): 222 (2012)
Issue (Month): 1 ()
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Web page: http://www.elsevier.com/locate/eor
Wavelet analysis; Monte Carlo simulation; Option pricing; De-noise;
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