De-noising option prices with the wavelet method

Emmanuel Haven, Xiaoquan Liu and Liya Shen

European Journal of Operational Research, 2012, vol. 222, issue 1, 104-112

Abstract: Financial 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.

Keywords: Wavelet analysis; Monte Carlo simulation; Option pricing; De-noise (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (36)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:222:y:2012:i:1:p:104-112

DOI: 10.1016/j.ejor.2012.04.020

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