De-noising option prices with the wavelet method

Emmanuel Haven, Xiaoquan Liu, Liya Shen

Research output: Journal PublicationArticlepeer-review

66 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)104-112
Number of pages9
JournalEuropean Journal of Operational Research
Volume222
Issue number1
DOIs
Publication statusPublished - 1 Oct 2012
Externally publishedYes

Keywords

  • De-noise
  • Monte Carlo simulation
  • Option pricing
  • Wavelet analysis

ASJC Scopus subject areas

  • Computer Science (all)
  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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