De-noising option prices with the wavelet method

Emmanuel Haven; Xiaoquan Liu; Liya Shen

doi:10.1016/j.ejor.2012.04.020

De-noising option prices with the wavelet method

Emmanuel Haven
, Xiaoquan Liu
, Liya Shen

Research output: Journal Publication › Article › peer-review

75 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 language	English
Pages (from-to)	104-112
Number of pages	9
Journal	European Journal of Operational Research
Volume	222
Issue number	1
DOIs	https://doi.org/10.1016/j.ejor.2012.04.020
Publication status	Published - 1 Oct 2012
Externally published	Yes

Keywords

De-noise
Monte Carlo simulation
Option pricing
Wavelet analysis

ASJC Scopus subject areas

General Computer Science
Modelling and Simulation
Management Science and Operations Research
Information Systems and Management

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10.1016/j.ejor.2012.04.020

Cite this

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title = "De-noising option prices with the wavelet method",

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.",

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AU - Liu, Xiaoquan

AU - Shen, Liya

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AB - 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.

KW - De-noise

KW - Monte Carlo simulation

KW - Option pricing

KW - Wavelet analysis

UR - https://www.scopus.com/pages/publications/84861828980

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DO - 10.1016/j.ejor.2012.04.020

M3 - Article

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JO - European Journal of Operational Research

JF - European Journal of Operational Research

IS - 1

ER -

De-noising option prices with the wavelet method

Abstract

Keywords

ASJC Scopus subject areas

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