Haven, Emmanuel and Liu, Xiaoquan and Shen, Liya (2012) De-noising option prices with the wavelet method. European Journal of Operational Research, 222 (1). pp. 104-112. DOI https://doi.org/10.1016/j.ejor.2012.04.020
Haven, Emmanuel and Liu, Xiaoquan and Shen, Liya (2012) De-noising option prices with the wavelet method. European Journal of Operational Research, 222 (1). pp. 104-112. DOI https://doi.org/10.1016/j.ejor.2012.04.020
Haven, Emmanuel and Liu, Xiaoquan and Shen, Liya (2012) De-noising option prices with the wavelet method. European Journal of Operational Research, 222 (1). pp. 104-112. DOI https://doi.org/10.1016/j.ejor.2012.04.020
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 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. © 2012 Elsevier B.V. All rights reserved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Wavelet analysis; Monte Carlo simulation; Option pricing; De-noise |
| Subjects: | H Social Sciences > HG Finance |
| Divisions: | Faculty of Social Sciences Faculty of Social Sciences > Essex Business School Faculty of Social Sciences > Essex Business School > Essex Finance Centre |
| SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
| Depositing User: | Unnamed user with email elements@essex.ac.uk |
| Date Deposited: | 18 Dec 2012 14:30 |
| Last Modified: | 18 Aug 2022 12:09 |
| URI: | http://repository.essex.ac.uk/id/eprint/4781 |