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European Journal of Operational Research, Volume 222, Issue 1, 1 October 2012, Pages 104-112
Emmanuel Haven, Xiaoquan Liu, Liya Shen
School of Management, University of Leicester, Leicester LE1 7RH, UK
Essex Business School, University of Essex, Colchester CO4 3SQ, UK
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 waveletmethod in the de-noising of optionprice 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 waveletmethod.
