De-noising option prices with the wavelet method

• Published in: European journal of operational research Vol. 222; no. 1; pp. 104 - 112
• Main Authors: Haven, Emmanuel, Liu, Xiaoquan, Shen, Liya
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 01.10.2012; Elsevier; Elsevier Sequoia S.A
• Subjects: Applied sciences; De-noise; Exact sciences and technology; Mathematical functions; Monte Carlo simulation; Operational research and scientific management; Operational research. Management science; Option pricing; Planning. Forecasting; Portfolio theory; Securities prices; Studies; Time series; Wavelet analysis; Wavelet transforms; Wavelet analysis; Option pricing; Monte Carlo simulation; De-noise; Monte Carlo method; Noise reduction; Time series; Financial market; Modeling; Forecasting; Wavelet transformation; Stock exchange; Pricing; Density function; Financial data; Financial option; Tariffication
• ISSN: 0377-2217; 1872-6860
• DOI: 10.1016/j.ejor.2012.04.020

► Wavelets de-noise perturbed option prices very well. ► Wavelet de-noising is necessary for density estimation from the option prices. ► Wavelet de-noising improves density estimation and forecasting ability. 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.