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De-noising option prices with the wavelet method

  • Haven, Emmanuel
  • Liu, Xiaoquan
  • Shen, Liya
Registered author(s):

    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.

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    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 222 (2012)
    Issue (Month): 1 ()
    Pages: 104-112

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    Handle: RePEc:eee:ejores:v:222:y:2012:i:1:p:104-112
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