De-noising option prices with the wavelet method
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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