Application of Wavelet method in de-noising option prices

In so much financial time series are known to carry noise, elimination of noise is necessary. Due to multi-scaling property, the wavelet method is very efficient in dealing with noisy data series. In specific, we propose to use the wavelet method to de-noise option prices before estimating the option-implied risk neutral density (RND) and forecasting future option prices. We use of two RNDs estimated from the perturbed prices and the filtered prices to forecast the out-of-sample options, respectively. Moreover, we compare them with the true Black-Scholes option prices. Results of this study show that, through the use of Monte Carlo simulations, the power of the wavelet method in the de-noising of option price data. It is clearly seen that, by de-noising the perturbed option prices using the wavelet method, most of the noise is removed and the wavelet de-noising method is robust to different levels of noise variance.

Application of Wavelet method in de-noising option prices Keywords:

De-noise , Option pricing , RND , Wavelet analysis , Monte Carlo simulation